SYSTEMS AND METHODS FOR OPTIMIZING SEED TRAINS

Abstract

Techniques for determining parameters for a seed train process for growing a culture of cells. The techniques include obtaining a specification of seed train process constraints for multiple stages of the seed train process; determining the seed train process parameters using the seed train process constraints, the seed train process parameters comprising a respective set of parameters for each particular stage of multiple stages of the seed train process; determining, using historical cell growth data and the seed train process parameters, a likelihood indicative of whether performing the seed train process using the seed train process parameters will satisfy seed train process success criteria; and outputting the set of seed train process parameters for each stage of the multiple stages of the seed train process and the likelihood indicative of whether performing the seed train process will satisfy the seed train process success criteria.

Description

1 / 107 SYSTEMS AND METHODS FOR OPTIMIZING SEED TRAINS RELATED ORDERS

[0001] This application claims the benefit of priority set forth in Title 35 U.S.C. § 119(e) of patent application serial number U.S. 63 / 489,958, filed March 13, 2023, entitled “SYSTEMS AND METHODS FOR SEED TRAIN OPTIMIZATION,” the full content of which is incorporated herein by reference. BACKGROUND

[0002] Biological products are used to treat, prevent, and diagnose various medical conditions and diseases. Biological products can be produced by cells specifically genetically modified for their production. Some biological products include polypeptides (e.g., therapeutic proteins, monoclonal antibodies, vaccines, etc.), nucleic acids (e.g., messenger RNA (mRNA), small interfering RNA (siRNA), microRNA, etc.), or viral vectors (e.g., lentiviral vectors, adenoviral vectors, recombinant adeno-associated virus (rAAv) vectors, etc.).

[0003] Cultivating a cell culture that is used to produce biological products helps to increase its production. Such a culture is typically grown using a seed train process, which refers to the process used to progressively scale up a culture from a small volume of cells to a larger volume of cells. A seed train process typically includes multiple stages, each of which corresponds to a cultivation system used to expand the culture to a larger volume than that achieved during the previous stage. Petition 870250082199, dated 12 / 09 / 2025, page 17 / 156 2 / 107 SUMMARY

[0004] Some embodiments provide a method for determining seed train process parameters for a seed train process for growing a cell culture, wherein the seed train process has multiple stages, the method being performed using at least one software application program comprising a common interface module, a parameter optimization module, and a simulation module. In some embodiments, the method includes the use of at least one computer hardware processor to: obtain, using the common interface module, a specification of seed train process constraints for the multiple stages of the seed train process;To determine the seed train process parameters using the parameter optimization module and the seed train process constraints, wherein the seed train process parameters comprise a respective set of parameters for each particular stage of the multiple stages of the seed train process, and the determination comprises: determining, for each particular stage of the multiple stages and using the parameter optimization module and the seed train process constraints, the respective set of parameters for the respective set of crop cultivation during the particular stage; determining, using the simulation module, historical cell growth data and the determined seed train process parameters, an indicative probability of the possibility of carrying out the seed train process using the determined seed train process parameters; Petition 870250082199, dated 12 / 09 / 2025, page 18 / 156 3 / 107 will satisfy the seed train process success criteria; and issue the respective set of seed train process parameters determined for each stage of the multiple stages of the seed train process and the indicative probability of the possibility that carrying out the seed train process using the determined seed train process parameters will satisfy the seed train process success criteria.

[0005] Some embodiments provide a system comprising: at least one computer hardware processor; and at least one computer-readable non-transient storage medium that stores processor-executable instructions that, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform a method for determining seed train process parameters for a seed train process for growing a cell culture, wherein the seed train process has multiple stages, wherein the method performed using at least one software application program comprises a common interface module, a parameter optimization module, and a simulation module.In some embodiments, the method includes the use of at least one computer hardware processor to: obtain, using the common interface module, a specification of seed train process constraints for the multiple stages of the seed train process; determine the seed train process parameters using the parameter optimization module and the seed train process constraints. Petition 870250082199, dated 12 / 09 / 2025, page 19 / 156 4 / 107 wherein the seed train process parameters comprise a respective set of parameters for each particular stage of the multiple stages of the seed train process, wherein the determination comprises: determining, for each particular stage of the multiple stages and using the parameter optimization module and the seed train process constraints, the respective set of parameters for the respective set of crop cultivation during the particular stage; determining, using the simulation module, historical cell growth data and the determined seed train process parameters, an indicative probability of the possibility that carrying out the seed train process using the determined seed train process parameters will satisfy the success criteria of the seed train process;and to output the respective set of seed train process parameters determined for each stage of the multiple stages of the seed train process and the indicative probability of the possibility that carrying out the seed train process using the determined seed train process parameters will satisfy the seed train process success criteria.

[0006] Some embodiments provide at least one computer hardware processor; and at least one computer-readable non-transient storage medium that stores processor-executable instructions that, when executed by at least one computer hardware processor, cause the at least one computer hardware processor to perform a method for determining Petition 870250082199, dated 12 / 09 / 2025, page 20 / 156 5 / 107 Seed train process parameters for a seed train process to cultivate a cell culture, wherein the method performed using at least one software application program comprises a common interface module, a parameter optimization module, and a simulation module. In some embodiments, the method includes the use of at least one computer hardware processor to: obtain, using the common interface module, a specification of seed train process constraints for the multiple stages of the seed train process;Determine the seed train process parameters using the parameter optimization module and the seed train process constraints, wherein the seed train process parameters comprise a respective set of parameters for each particular stage of the multiple stages of the seed train process, wherein the determination comprises: determining, for each particular stage of the multiple stages and using the parameter optimization module and the seed train process constraints, the respective set of parameters for the respective set of crop cultivation during the particular stage;To determine, using the simulation module, historical cell growth data and the determined parameters of the seed train process, an indicative probability of the possibility that carrying out the seed train process using the determined parameters of the seed train process will satisfy the success criteria of the seed train process; and to issue the respective set of determined seed train process parameters for each stage; Petition 870250082199, dated 12 / 09 / 2025, page 21 / 156 6 / 107 of the multiple stages of the seed train process and the indicative probability of the possibility that carrying out the seed train process using the determined seed train process parameters will satisfy the seed train process success criteria.

[0007] In some embodiments, determining, for each particular stage of the multiple stages, the respective set of parameters for growing the crop during the particular stage comprises: determining at least one seed train process parameter selected from the group consisting of: a viable cell density, an indication of whether the particular stage is included in the seed train process, a targeted duration of the seed train process, a batch medium volume, a working volume per pot and a number of pots.

[0008] In some embodiments, obtaining the specification of seed train process constraints comprises: obtaining a specification of at least one seed train process constraint selected from the group consisting of: a target viable cell density range, a target working volume range, a split ratio crop growth process constraint, and a batch medium volume range.

[0009] In some embodiments, historical cell growth data comprise indicative data of a doubling time associated with the growth of one or more cell cultures.

[0010] In some embodiments, determine the seed train process parameters using the optimization module. Petition 870250082199, dated 12 / 09 / 2025, page 22 / 156 7 / 107 of parameters involves determining the seed train process parameters using a genetic algorithm.

[0011] In some embodiments, determining the indicative probability that performing the seed train process using the determined seed train process parameters will satisfy the seed train process success criteria involves performing Monte Carlo simulations using historical cell growth data.

[0012] In some embodiments, the success criteria for the seed train process comprise, for each stage of the multiple stages of the seed train process: a first criterion that an expected duration required to cultivate the crop during the particular stage does not exceed a limit duration for cultivating the crop during the particular stage; and a second criterion that an expected viable cell density resulting from the growth of the crop during the particular stage does not exceed a limit viable cell density resulting from the growth of the crop during the particular stage.

[0013] In some embodiments, determining the respective set of parameters for each particular stage of the multiple stages comprises: determining, using the respective set of parameters, the expected duration required to grow the cell culture during the particular stage; and determining whether the expected duration exceeds the respective time limit for the particular stage.

[0014] In some modalities, determining the respective set of parameters for each particular stage of the multiple stages comprises: determining, using the Petition 870250082199, dated 12 / 09 / 2025, page 23 / 156 8 / 107 respective set of parameters, the expected viable cell density resulting from the growth of the cell culture during the particular stage; and determine whether the expected viable cell density exceeds the respective limit viable cell density for the particular stage.

[0015] In some embodiments, issuing the set of seed train process parameters for each stage of the multiple stages of the seed train process and the indicative probability of the possibility that the execution of the seed train process will satisfy the success criteria of the seed train process comprises: generating a graphical user interface (GUI) using the common interface module; and displaying the set of seed train process parameters and / or the probability through the generated GUI.

[0016] In some embodiments, displaying the seed train process parameter set via the common interface module comprises: displaying a visual indication of whether a seed train process parameter from the seed train process parameter set violates a seed train process constraint from the seed train process constraints.

[0017] In some embodiments, determining the seed train process parameters comprises: determining the seed train process parameters based on a set of estimated crop doubling times included in the seed train process constraints, wherein the set of estimated crop doubling times includes an estimated crop doubling time. Petition 870250082199, dated 12 / 09 / 2025, page 24 / 156 9 / 107 for each of the multiple stages of the seed train process; and determine whether performing the seed train process using the determined seed train process parameters will satisfy the seed train process success criteria. Some embodiments further comprise displaying, through a graphical user interface (GUI) generated by the common interface module, a visual indication of a result of the determination of whether performing the seed train process using the seed train process parameters will satisfy the seed train process success criteria.

[0018] Some modalities also include: comparing the determined probability to a probability limit, wherein the output of the set of seed train process parameters for each stage of the multiple stages of the seed train process includes the determined output of the seed train process parameters when it is determined that the probability exceeds the probability limit.

[0019] In some embodiments, issuing the seed train process parameter set for each stage of the multiple stages of the seed train process comprises: issuing, when the indicative probability of the possibility that carrying out the seed train process using the determined seed train process parameters will satisfy the seed train process success criteria satisfies a probability threshold, a recommendation to carry out the seed train process using the seed train process parameter set for each stage of the multiple stages of the seed train process. Petition 870250082199, dated 12 / 09 / 2025, page 25 / 156 10 / 107

[0020] In some embodiments, at least one software application program further comprises a seed train process automation module, and emitting the seed train process parameter set for each stage of the multiple stages of the seed train process and the indicative probability of the likelihood that the execution of the seed train process will satisfy the seed train process success criteria comprises: transmitting the seed train process parameter set and / or the probability to the seed train process automation module; and using the seed train process automation module to cause a seed train process automation system to execute a particular stage of the multiple stages of the seed train process in accordance with the respective seed train process parameter set.

[0021] Some embodiments also include: obtaining, using the common interface module, indicative input of a cell culture growth result during a first stage of the multiple stages of the seed train process; determining, using the parameter optimization module, the seed train process input and constraints, updated seed train process parameters for subsequent stages of the seed train process; and emitting the updated seed train process parameters.

[0022] Some embodiments also include: obtaining, using the common interface module, a specification of second seed train process constraints for multiple stages of a second seed train process. Petition 870250082199, dated 12 / 09 / 2025, page 26 / 156 11 / 107 seeds; determine, using the parameter optimization module and the second seed train process constraints, the second seed train process parameters for the second seed train process; determine, using the simulation module, the historical cell growth data and the second seed train process parameters, a second indicative probability of the likelihood that performing the second seed train process, using the second seed train process parameters, will satisfy the second seed train process criteria; and output the second seed train process parameters and the second indicative probability of the likelihood that performing the second seed train process, using the second seed process parameters, will satisfy the second seed train process criteria.

[0023] In some embodiments, determining seed train process parameters using the parameter optimization module comprises: determining, using an objective function, a first score for the first candidate seed train process parameters; determining, using the objective function, a second score for the second candidate seed train process parameters; comparing the first score and the second score; and selecting, based on the result of the comparison, the seed train process parameters from among the first candidate seed train process parameters and the second candidate seed train process parameters.

[0024] In some modes, the process parameters Petition 870250082199, dated 12 / 09 / 2025, page 27 / 156 12 / 107 of seed train comprise a first set of seed train process parameters for a first stage of the multiple stages of the seed train process, and determining the indicative probability of the possibility that carrying out the seed train process using the seed train process parameters will satisfy the success criteria of the seed train process comprises: determining, for the first stage of the seed train process, a first indicative probability that cultivating the cell culture during the first stage, using the first set of seed train parameters for the first stage, will satisfy the first criteria of the success criteria of the seed train process.

[0025] In some embodiments, determining the indicative probability that performing the seed train process using the seed train process parameters will satisfy the seed train process success criteria comprises: simulating a first set of cell growth values ​​using historical cell growth data, wherein the first set of cell growth values ​​includes a first historical cell growth value for each of the multiple stages of the seed train process; predicting, using the seed train process parameters and the first set of cell growth values, a first outcome of performing the seed train process, wherein the first outcome is indicative that performing the seed train process using the determined seed train process parameters will satisfy the seed train process success criteria. Petition 870250082199, dated 12 / 09 / 2025, page 28 / 156 13 / 107 will satisfy the success criteria of the seed train process; simulate a second set of cell growth values ​​using historical cell growth data, wherein the second set of cell growth values ​​includes a second historical cell growth value for each of the multiple stages of the seed train process; predict, using the seed train process parameters and the second set of cell growth values, a second outcome of performing the seed train process, wherein the second outcome is indicative that performing the seed train process using the seed train process parameters will satisfy the success criteria of the seed train process; and determine the probability based on the first and second predicted outcomes.

[0026] In some embodiments, determining probability based on the first and second predicted outcomes comprises: determining several predicted outcomes that indicate that performing the seed train process using the seed train process parameters and respective historical cell growth data will satisfy the seed train process success criteria; and determining a ratio of the number of predicted outcomes to a total number of predicted outcomes.

[0027] In some embodiments, the success criteria for the seed train process comprise, for each stage of the multiple stages of the seed train process: a first criterion that an expected duration required to cultivate the crop during the particular stage does not exceed a limit duration for cultivating the crop during the Petition 870250082199, dated 12 / 09 / 2025, page 29 / 156 14 / 107 particular stage, predicting the first outcome comprises, for each particular stage of the multiple stages: determining, using the respective set of seed train process parameters for the particular stage and a cell growth history value from the first set of cell growth history values, an expected first duration required to grow the cell culture during the particular stage of the seed train process;and compare the first expected duration with the limit duration, and predict the second outcome, comprising, for each particular stage of the multiple stages: determining, using the respective set of seed train process parameters for the particular stage and a cell growth history value from the second set of cell growth history values, a second expected duration required to grow the cell culture during the particular stage of the seed train process; and comparing the second expected duration with the limit duration.

[0028] In some embodiments, the seed train process success criteria comprise, for each stage of the multiple stages of the seed train process: a second criterion that an expected viable cell density resulting from crop growth during the particular stage does not exceed a limiting viable cell density resulting from crop growth during the particular stage; predicting the second outcome comprises, for each particular stage of the multiple stages: determining, using the respective set of seed train process parameters for the particular stage and a Petition 870250082199, dated 12 / 09 / 2025, page 30 / 156 15 / 107 cell growth history value from the first set of cell growth history values, an expected first viable cell density resulting from cell culture growth during the particular stage of the seed train process; and compare the expected first viable cell density with the limit viable cell density, and predict the second outcome comprises, for each particular stage of the multiple stages: determining, using the respective set of seed train process parameters for the particular stage and a cell growth history value from the second set of cell growth history values, an expected second viable cell density resulting from cell culture growth during the particular stage of the seed train process; and comparing the expected second viable cell density with the limit viable cell density. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The attached drawings are not intended to be drawn to scale. In the drawings, each identical or nearly identical component that is illustrated in several figures is represented by a similar number. For clarity, not all components can be identified in every drawing. In the drawings:

[0030] Figure 1A is a diagram representing an illustrative technique 100 for determining seed train process parameters for a seed train process, according to some embodiments of the technology described in this document.

[0031] Figure 1B is a block diagram of a system Petition 870250082199, dated 12 / 09 / 2025, p. 31 / 156 16 / 107 exemplary 160 to determine seed train process parameters for a seed train process, according to some embodiments of the technology described in this document.

[0032] Figure 2 is a flowchart of an illustrative process 200 for determining seed train process parameters for a seed train process, according to some embodiments of the technology described in this document.

[0033] Figure 3A is a flowchart of an illustrative process 300 for determining seed train process parameters using a seed train process parameter and constraint optimization module, according to some embodiments of the technology described in this document.

[0034] Figure 3B shows an illustrative objective function used to determine seed train process parameters, according to some embodiments of the technology described in this document.

[0035] Figure 4 is a flowchart of an illustrative process 400 to determine an indicative probability of the likelihood that carrying out the seed train process using the seed train process parameters will satisfy the success criteria of the seed train process, according to some embodiments of the technology described in this document.

[0036] Figure 5A-1, Figure 5A-2, Figure 5B-1, Figure 5B-2, Figure 5C-1 and Figure 5C-2 show an exemplary interface representing seed train process parameters and process constraints. Petition 870250082199, dated 12 / 09 / 2025, page 32 / 156 17 / 107 seed train, according to some modalities of the technology described in this document.

[0037] Figure 5D shows an exemplary interface representing predictions that carrying out the seed train process using the seed train process parameters will satisfy the success criteria of the seed train process, according to some embodiments of the technology described in this document.

[0038] Figure 6 shows illustrative historical cell growth data for doubling times.

[0039] Figure 7A and Figure 7B show exemplary doubling time data, from which historical cell growth data are derived, according to some embodiments of the technology described in this document.

[0040] Figure 8A and Figure 8B show a higher overall success rate of performing a seed train process, according to embodiments of the technology described in this document, compared to performing a manually designed seed train process.

[0041] Figure 8C shows that carrying out a 500 l seed train process, according to the technology modalities described in this document, results in a success rate greater than 80% for each stage of the seed train process.

[0042] Figure 8D shows that carrying out a 2000 l seed train process, according to the technology modalities described in this document, results in a success rate greater than 70% for each stage of the seed train process.

[0043] Figure 9 is a schematic diagram of a Petition 870250082199, dated 12 / 09 / 2025, page 33 / 156 18 / 107 Illustrative computing device with which the aspects described in this document can be implemented. DETAILED DESCRIPTION

[0044] The inventors developed techniques for designing and implementing an optimized seed train process for cultivating a cell culture. In some embodiments, the techniques include determining the seed train process parameters for the seed train process. For example, a set of seed train process parameters can be determined for each of the multiple stages of the seed train process. In some embodiments, the seed train process parameters are determined using a seed train process parameter and constraint optimization module for each of the multiple stages of the seed train process.After determining the seed train process parameters, the techniques include determining, using a simulation module and historical cell growth data, an indicative probability of the likelihood that carrying out the seed train process using the determined seed train process parameters will satisfy several seed train process success criteria, such as, for example, a success criterion that the estimated implementation duration of a seed train process stage does not exceed the limit duration and a success criterion that an estimated viable cell density resulting from crop growth during a seed train process stage does not exceed a limit viable cell density.

[0045] Although a small vial of cells can be used Petition 870250082199, dated 12 / 09 / 2025, page 34 / 156 19 / 107 to produce a biological product, this would be highly inefficient for producing industrial-scale quantities of such a product. Consequently, cell culture is grown to a volume that can support industrial-scale production. For example, cell culture can be grown to a volume that can be used to inoculate a bioreactor, which is a large system that supports a biologically active environment for large-scale bioproduction. Growing the cell culture to such a volume involves progressively scaling up the culture from the initial volume to the target volume. Depending on the target volume, this process can take up to days or weeks.

[0046] A seed train process refers to the process used to progressively scale up a culture from a small cell volume to a larger cell volume. A seed train process typically includes multiple stages (multi-stage seed train process), each of which corresponds to a culture system used to grow the culture during that particular stage. For example, the first stage of a seed train process might utilize a 250 ml shaker flask. During the first stage, cells are grown in the 250 ml shaker flask until the culture meets particular criteria, such as reaching a target viable cell density. The culture is then transferred to a different container, such as a 1000 ml shaker flask, for growth during the second stage.As the seed train process progresses, the cell culture continues to expand until the target volume is reached. Petition 870250082199, dated 12 / 09 / 2025, p. 35 / 156 20 / 107

[0047] Several factors affect the quality of a seed train process. Examples of such factors include the vessels selected, the volume of culture medium used to fill the selected vessels, the ratio of fresh medium to past cell culture, the duration of the culture, and the evident growth rate. When designing a seed train process, many of these factors can be adjusted to control culture growth. For example, the designer of the seed train process (e.g., a user or an automated system) can select the vessel for each stage of the seed train process and the volume of culture medium to fill each vessel. However, other factors cannot be controlled. For example, the cell growth rate varies between different stages and different cultures.Furthermore, the rate of cell growth is complex insofar as it depends on several external factors, such as process scales, vessel selection, seeding densities, substrate, and metabolite concentrations.

[0048] Because the cell growth rate directly impacts the time required to complete a stage of the seed train process, and due to variations between different stages and crops, it is challenging to predict how long it will take to complete the seed train process or a particular stage of a seed train process. For example, cells growing at an unexpectedly low cell growth rate will require more time to reach the target volume, leading to delays. Such delays can cause scheduling problems for the manufacturing facility where the seed train process is being performed. Conversely, cells Petition 870250082199, dated 12 / 09 / 2025, page 36 / 156 21 / 107 cells growing at an unexpectedly high cell growth rate will require less time to reach the target volume, but may lead to excessive cell growth if the culture is not being monitored. When a culture grows excessively, this leads to cell death and, in many cases, the culture must be discarded. This not only wastes resources but also results in significant delays incurred by regrouping the culture.

[0049] Conventional techniques for designing seed train processes are empirical. They involve designing later stages of the seed train process based on observations made during earlier stages of the seed train process. For example, such techniques involve growing the culture during a particular stage of the seed train process, recording observations on culture growth (e.g., cell growth rate) during that stage, and using those observations to design the next stage of the seed train process. While such techniques may facilitate the successful expansion of a high-quality cell culture by preventing cell overgrowth, they do not allow for an estimation of the duration of the seed train process.Firstly, such techniques are still subject to delays introduced by unexpected changes in the rate of cell growth at different stages of the seed train process. Secondly, many parameters that affect the duration of the seed train process, such as the number of stages, vessel selection, and division ratio, for example, are selected during the middle of the seed train process, making it difficult to predict in advance how long it will take. Petition 870250082199, dated 12 / 09 / 2025, page 37 / 156 22 / 107 time different stages of the seed train process will take to be completed.

[0050] Consequently, the inventors have developed techniques that solve the above-described limitations of conventional seed train process design techniques. In some embodiments, the techniques include: (a) obtaining seed train process constraints for multiple stages of a seed train process, (b) determining seed train process parameters using the seed train process constraints and an optimization module, (c) determining, using a simulation module and historical cell growth data, the indicative probability that performing the seed train process using the seed train process parameters will satisfy the seed train process success criteria, and (d) outputting the determined seed train process parameters and the determined probability.

[0051] The term seed train process constraints refers to aspects of the seed train process that cannot be varied. In some embodiments, these are defined by the ease with which the seed train process is performed. For example, the duration of the seed train process may be limited by the schedule of the facility where the seed train process is performed. Additionally or alternatively, in some embodiments, seed train process constraints are defined by the equipment available to perform the seed train process. For example, the type, number, and / or volume of available containers may restrict, at each stage, the volume of media that can be used. Petition 870250082199, dated 12 / 09 / 2025, p. 38 / 156 23 / 107 to be supplied to the cell culture. Additionally or alternatively, in some embodiments, the seed train process constraints are defined by the cell culture itself. For example, different cell lines grow at different rates. The growth rate can be a constraint on the seed train process, since it cannot be altered. In some embodiments, the growth rate and / or doubling time of a particular cell line can be estimated based on historical cell growth data. Exemplary seed train process constraints are listed in Table 1.

[0052] The term seed train process parameters can refer to aspects of the seed train process that can be varied. Seed train process parameters can be selected from within the seed train process constraints. For example, seed train process constraints can define the upper and lower thresholds of a particular aspect of the seed train process (e.g., average volume), and the seed train process parameter can be selected from among these thresholds. Exemplary seed train process parameters are listed in Table 2.

[0053] The term success criteria may refer to one or more criteria that define one or more successful outcomes of implementing the seed train process. In some embodiments, the success criteria include a criterion that an expected duration is required to cultivate the crop during a particular stage of the train process. Petition 870250082199, dated 12 / 09 / 2025, p. 39 / 156 24 / 107 of seeds does not exceed a limiting duration for cultivating the culture during the particular stage. If the duration criterion is met, in some embodiments, this may indicate that no more than a limiting amount of extra time is needed to cultivate the cell culture during the particular stage. If the duration criterion is not met, this may indicate that performing the seed train process according to the seed train process constraints and selected parameters may potentially disrupt the facility schedule and / or downstream process timing. Additionally or alternatively, in some embodiments, the success criteria include a criterion that an expected viable cell density resulting from culture growth during a particular stage of the seed train process does not exceed a limiting viable cell density.In some embodiments, if the expected viable cell density does not exceed the threshold (i.e., the criterion is met), this may indicate that growing the culture during the particular stage according to the seed train process constraints and selected parameters will not result in excessive cell culture growth. Conversely, if the expected viable cell density exceeds the threshold viable cell density, this may indicate that growing the culture during the particular stage, according to the seed train process constraints and selected parameters, may result in excessive cell culture growth.

[0054] The techniques developed by the inventors improve upon conventional design techniques of Petition 870250082199, dated 12 / 09 / 2025, page 40 / 156 25 / 107 seed train process due to the fact that they involve determining seed train process parameters which, when used to implement one or more stages of the seed train process, are estimated to result in the successful completion of the seed train process under average cell growth conditions. Such techniques allow the design of the seed train process before the seed train process begins, thus allowing manufacturing facilities to accurately estimate the time required to complete the seed train process and develop schedules accordingly. This is far more efficient than conventional techniques that design the seed train process during the implementation of the seed train process, thus avoiding accurate estimates of how long it will take to complete the seed train process.

[0055] Furthermore, the techniques developed by the inventors improve upon conventional techniques because they account for variability in historical cell growth rates to predict the probability of successfully completing the seed train process using the determined seed train process parameters. Therefore, before implementing the seed train process, it is possible to estimate whether the seed train process will be successful by considering whether cell growth rates are slower or faster than average. This information can be used to determine whether to proceed with implementing the seed train process using the determined parameters, determine updated parameters, or cancel the process. Petition 870250082199, dated 12 / 09 / 2025, page 41 / 156 26 / 107 seed train completely. Thus, such techniques help ensure adherence to the plant schedule and reduce waste of resources and time.

[0056] It should be understood that the techniques described in this document can be implemented in any of numerous ways, as the techniques are not limited to any specific method of implementation. The examples of implementation details are provided in this document for illustrative purposes only. Furthermore, the techniques disclosed in this document can be used individually or in any suitable combination, as aspects of the technology described in this document are not limited to the use of any particular technique or combination of techniques.

[0057] Figure 1A is a diagram representing an illustrative technique 100 for designing a seed train process, according to some embodiments of the technology described in this document. In some embodiments, the technique 100 includes (a) determining, in act 110, seed train process parameters based on seed train process constraints 102, (b) determining, in act 120, based on historical cell growth data 104, the probability of satisfying seed train process success criteria as a result of carrying out one or more stages of the seed train process according to the determined parameters, and (c) determining, in act 130, based on the probability of satisfying the success criteria, whether to implement one or more stages of the seed train process. In some embodiments, the technique 100 terminates if it is determined in act 130 that one or more Petition 870250082199, dated 12 / 09 / 2025, page 42 / 156 27 / 107 stages of the seed train process should not be implemented. In some embodiments, when determining that one or more stages should be implemented, technique 100 includes implementing the one or more stages of the seed train process in act 140. Based on the result of implementing the one or more stages of the seed train process, act 145 includes determining whether the result satisfies the success criteria. If the result satisfies the success criteria, act 150 includes determining whether there are additional stages of the seed train process. If there are additional stages of the seed train process, then one or more acts of technique 100 may be repeated for the next stage. Otherwise, technique 100 ends.

[0058] As described in this document, a seed train process is used to scale up a culture from a small cell volume to a larger cell volume. For example, at the beginning of the seed train process, the cell culture may occupy a container having a volume of 25 ml, 50 ml, 100 ml, 150 ml, 200 ml, 250 ml, 300 ml, 400 ml, 500 ml, between 10 ml and 1000 ml, between 25 ml and 500 ml, between 200 ml and 400 ml, or any other suitable volume, as the aspects of the technology described in this document are not limited in this respect. During the seed train process, the cell culture may be expanded to reach any suitable volume. For example, at the end of the seed train process, the cell culture may occupy a container that is a factor of 2, 5, 10, 25, 50, 75, 100, 150, 200, 300, 500, 1000, 2000, 5000, 10000, between 1.5 and 50000, between 2 and 10000, between 25 and Petition 870250082199, dated 12 / 09 / 2025, page 43 / 156 28 / 107 5000, or any other suitable factor times the size of the initial container, since the aspects of the technology described in this document are not limited in this respect.

[0059] In some embodiments, the seed train process includes multiple stages. For example, as shown in Act 140 of technique 100, the seed train process includes stage 1 through stage N-1, where N is any suitable number, as aspects of the technology described herein are not limited to any particular number of seed train process stages. As described herein, in some embodiments, the number of stages is a seed train process parameter determined in Act 110 of technique 100.

[0060] In some embodiments, performing the seed train process involves cultivating the cell culture according to certain seed train process parameters. In some embodiments, each stage of the seed train process is associated with a respective set of seed train process constraints and / or seed train parameters. For example, the first stage of the seed train process may be associated with a first set of constraints, and during the first stage, the cell culture may be cultivated using a first set of parameters. The Nth stage of the seed train process may be associated with an Nth set of constraints, and during the Nth stage, the cell culture may be cultivated using an Nth set of parameters. The first set of constraints and Petition 870250082199, dated 12 / 09 / 2025, p. 44 / 156 29 / 107 The Nth set of constraints can be the same (for example, include all the same constraints) or different (for example, include some or none of the same constraints). The first set of parameters and the Nth set of parameters can be the same (for example, include all the same parameters) or different (for example, include some or none of the same parameters).

[0061] In some embodiments, the seed train process constraints 102 are aspects of the seed train process that cannot be varied. In some embodiments, they are defined by the ease with which the seed train process is performed. For example, the duration of the seed train process may be limited by the schedule of the facility where the seed train process is performed. Additionally or alternatively, in some embodiments, the seed train process constraints 102 are defined by the equipment available to perform the seed train process. For example, the type, number, and / or volume of available containers may restrict, at each stage, the volume of media that can be supplied to the cell culture. Additionally or alternatively, in some embodiments, the seed train process constraints 102 are defined by the cell culture itself.For example, different cell lines grow at different rates. The growth rate can be a constraint on the seed train process, since it cannot be altered. In some embodiments, the growth rate and / or doubling time of a particular cell line can be estimated based on historical cell growth data obtained from the... Petition 870250082199, dated 12 / 09 / 2025, page 45 / 156 30 / 107 storage of cell growth data history 172.

[0062] Non-limiting examples of seed train process constraints are listed in Table 1. It should be understood that the seed train process may be associated with additional, or alternative, seed train process constraints, as aspects of the technology described herein are not limited in this regard. In some embodiments, an exemplary seed train process constraint listed in Table 1 may apply to one, some, or all stages of the seed train process. In some embodiments, after one or more stages of the seed train process have been performed, the seed train process constraints 102 may be updated to include real-time crop growth data 142, such as doubling time and cell growth constant during the completed stages of the seed train process.Additionally or alternatively, examples of seed train process constraints are described in this document, including at least those relating to Figure 5A-1 through Figure 5C-2. Seed Train Process Restriction Description Average Standard Batch Volume The recommended volume of cell culture medium that is added to the vessel before inoculation. Lower Batch Volume Limit The lower limit of the average batch volume. Upper Batch Limit The upper limit of the average volume. Petition 870250082199, dated 12 / 09 / 2025, page 46 / 156 31 / 107 Batch volume. Standard working volume. The total recommended cell culture volume in the vessel. Lower limit of working volume. The lower limit of the working volume. Upper limit of working volume. The upper limit of the working volume. Standard initial viable cell density (VCD). The recommended VCD of the cell culture immediately after inoculation. Lower limit of initial VCD. The lower limit of the initial VCD. Upper limit of initial VCD. The upper limit of the initial VCD. Fraction of evaporative loss. The theoretical volume loss due to evaporation. For example, 10% evaporative volume loss in shake flasks; 5% evaporative volume loss in bags and Wave-type bioreactors. Growth constant - normal. The cell growth rate assuming normal doubling time. Equal to: Em(2) Normal doubling time. Growth constant - worst. The cell growth rate assuming the longest doubling time.Equal to: Em(2) Worst doubling time. Growth constant - Best. The cell growth rate assuming the shortest time. Petition 870250082199, dated 12 / 09 / 2025, page 47 / 156 32 / 107 Replication. Equal to: Em(2) Best Replication Time ' Standard Culture Time The duration of the cell culture. Normal Replication Time The replication time under normal growth conditions for the cell line used. Worst Replication Time The replication time if the cell line is under the worst growth conditions. Best Replication Time The replication time if the cell line is under the best growth conditions. Transition Date The date on which inoculation occurs for the stage. Binary value indicating whether a stage should be included or skipped. Whether the stage is included in the seed train. Certain stages may be skipped for a shorter seed train. Average Batch Volume The average batch volume. Working Volume per Vessel The working volume per vessel. Number of Vessels The number of vessels used to grow the culture during the stage of the seed train process. Initial VCD The VCD of the cell culture immediately after inoculation. Table 1. Illustrative constraints on seed train process. Petition 870250082199, dated 12 / 09 / 2025, page 48 / 156 33 / 107

[0063] In some embodiments, seed train process parameters are aspects of the seed train process that can be varied. Non-limiting examples of seed train process parameters are listed in Table 2. However, it should be understood that the seed train process may be associated with additional, or alternative, seed train process parameters, as aspects of the technology described herein are not limited in this sense. In some embodiments, an exemplary seed train process parameter listed in Table 2 may apply to one, some, or all stages of the seed train process. In some embodiments, one or more seed train process parameters listed in Table 2 may be calculated based on one or more other seed train process parameters and / or seed train process constraints.For example, Table 3 lists equations for calculating at least some of the seed train process parameters in Table 2. Additionally or alternatively, examples of seed train process parameters are described in this document, including at least those relating to Figure 5B-1 through Figure 5C-2. Seed train process parameter Description VCD of the vial The VCD of the cell bank that is used to inoculate the vial (or vials) in the first pass of the seed train process. Binary value that determines if the stage is included in the train. Petition 870250082199, dated 12 / 09 / 2025, page 49 / 156 34 / 107 Indicates whether a seed train stage should be included or skipped. Certain stages can be skipped for a shorter seed train. Average batch volume: The average volume of the batch. Working volume per vessel: The working volume per vessel. Number of vessels: The number of vessels used to grow the culture during a stage of the seed train process. Initial VCD: The VCD of the cell culture immediately after inoculation. Total volume after loss: The volume of cell culture after accounting for evaporative loss and sampling loss. For example, sampling loss can be assumed to be 5 ml in shake flasks, 30 ml in Wave bags, and 500 ml in bioreactors. For a 60 ml shake flask, a total loss of 10 ml, including evaporation and sampling, can be assumed. Minimum final VCD required: The minimum VCD at the end of the stage that is required to inoculate the next stage.Determined by taking the larger of: (A) the minimum cell mass to achieve the target VCD and the working volume of the next pass; and (b) the cell mass. Petition 870250082199, dated 12 / 09 / 2025, page 50 / 156 35 / 107 Minimum to achieve a split ratio of at least 2 (e.g., the volume of fresh media is at least 2 times the volume of cell culture). Predicted final VCD (normal) The predicted VCD at the end of the stage using the normal growth rate. Split ratio The ratio between the volume of fresh media and the volume of cell culture used for passage. Normal time required The time required for the cell culture to reach the minimum VCD for passage. Calculated assuming exponential cell growth using the defined normal growth rate. Table 2. Exemplary seed train process parameters.

[0064] In some embodiments, varying the seed train process parameters affects the outcome of the seed train process. For example, varying the seed train process parameters can affect the time required to complete a stage of the seed train process. Additionally or alternatively, varying the seed train process parameters can affect the density of viable cells resulting from crop growth according to the seed train process parameters.

[0065] In some embodiments, designing a seed train process includes estimating the outcome of carrying out a Petition 870250082199, dated 12 / 09 / 2025, page 51 / 156 36 / 107 seed train process according to seed train process parameters and seed train process constraints. This may include, for example, estimating the duration required to cultivate the crop during a particular stage of the seed train process. In some embodiments, the duration of a stage of a seed train process may depend on one or multiple parameters and / or constraints of the seed train process. For example, the doubling time of a cell line, which can be estimated based on historical cell growth data (e.g., historical cell growth data 104), may affect the duration of a particular stage of the seed train process.Given the variation in doubling time, it may be beneficial to (a) estimate a duration based on the normal doubling time for the cell line (e.g., the average doubling time determined from historical cell growth data 104) and (b) estimate a duration based on the worst longest doubling time for the cell line based on historical cell growth data 104. In some embodiments, the estimated durations may provide an indication as to whether the seed train process will meet the facility schedule and / or whether extra time will be required to perform the seed train process.

[0066] Additionally or alternatively, estimating the outcome of carrying out a seed train process according to seed train process parameters and seed train process constraints may include estimating the final viable cell density resulting from crop growth during a particular stage. In some Petition 870250082199, dated 12 / 09 / 2025, page 52 / 156 In some embodiments, the estimated final viable cell density provides an indication of whether cell culture growth during the particular stage will result in excessive cell culture growth, which could lead to cell death. In some embodiments, the final viable cell density also depends on the doubling time of the cell line. Consequently, in some embodiments, the final viable cell density can be estimated based on the normal doubling time for the cell line (e.g., the average doubling time determined from historical cell growth data) and / or based on the shortest doubling time. In some embodiments, the estimated results of the seed train process are evaluated to determine whether they satisfy one or more success criteria.In some embodiments, the success criteria include a criterion that an expected duration required to cultivate the crop during a particular stage does not exceed a limit duration for cultivating the crop during that particular stage. In some embodiments, the limit duration includes any suitable duration, as the aspects of the technology are not limited in this respect. For example, the limit duration may be defined by a seed train process constraint (e.g., standard cultivation time). Additionally or alternatively, in some embodiments, the success criteria include a criterion that the extra time required (e.g., relative to a designated time) to cultivate the crop during a particular stage does not exceed a limit value. For example, the limit value may be 0, 2, 4, 8, 12, 20, 40, or any other suitable number of hours, as seen. Petition 870250082199, dated 12 / 09 / 2025, p. 53 / 156 38 / 107 that the aspects of the technology are not limited in this sense. If the duration criterion is not met, this may indicate that carrying out the seed train process according to the seed train process constraints and the selected parameters may potentially disrupt the plant schedule and / or the timing of downstream processes.

[0068] Additionally or alternatively, in some embodiments, the success criteria include a criterion that an expected viable cell density resulting from crop growth during a particular stage of the seed train process does not exceed a limit viable cell density.In some embodiments, if the expected viable cell density does not exceed the threshold (i.e., the criterion is met), this may indicate that growing the culture during the particular stage according to the seed train process constraints and selected parameters will not result in excessive cell culture growth. Conversely, if the expected viable cell density exceeds the threshold viable cell density, this may indicate that growing the culture during the particular stage, according to the seed train process constraints and selected parameters, may result in excessive cell culture growth. In some embodiments, the threshold viable cell density may depend on the container being used to grow the culture during the particular stage.

[0069] In some embodiments, technique 100 includes determining, in act 110, seed train process parameters to implement one or more stages of the process of Petition 870250082199, dated 12 / 09 / 2025, p. 54 / 156 39 / 107 Seed train. In some embodiments, the seed train process parameters are determined so that the estimated results of carrying out the seed train process according to the determined parameters satisfy one or more success criteria. By determining the seed train process parameters that are expected to result in results that satisfy the success criteria, technique 100 can be used to design a seed train process that avoids excessive cell culture growth and that meets time constraints (e.g., setup time if the seed train process is carried out).Additionally or alternatively, in some embodiments, the seed train process parameters are determined so that the estimated results of carrying out the seed train process according to the determined parameters are more desirable than the estimated results of carrying out the seed train process according to other seed train process parameters. For example, a more desirable estimated result might include a relatively short duration for carrying out one or more stages of the seed train process and / or a relatively low viable cell density, compared to other expected durations and / or viable cell densities.

[0070] In some embodiments, determining the seed train process parameters in act 110 includes (a) estimating, for each of the multiple sets of candidate seed train process parameters, results of carrying out the seed train process according to the candidate set of parameters, (b) comparing the results Petition 870250082199, dated 12 / 09 / 2025, page 55 / 156 40 / 107 estimated and (c) determine the seed train process parameters based on a comparison result. Determining the seed train process parameters may include selecting candidate parameters that result in estimated results that satisfy one or more success criteria and / or selecting candidate parameters that result in a more desirable result (or results) than the other candidate parameters. In some embodiments, the seed train process parameters are determined using a parameter optimization module, such as the parameter optimization module 182 described herein, including at least with respect to Figure 1B. In some embodiments, the parameter optimization module uses an optimization technique to determine the seed train process parameters.For example, the genetic algorithm can test candidate seed train process parameters against an objective function to determine the seed train process parameters. Techniques for determining seed train process parameters are described in more detail in this document, including at least with regard to Figures 1B, 2, and 3A to 3B.

[0071] In some embodiments, as explained above, the estimated results of carrying out a particular stage of a seed train process depend on the doubling time of the particular cell line being cultured. However, the doubling time varies between different cell cultures – some cultures may have relatively long doubling times, while other cultures may have relatively short doubling times. Petition 870250082199, dated 12 / 09 / 2025, page 56 / 156 41 / 107 short. Figure 6 shows an illustrative distribution of doubling times for multiple cultures of the same cell line. Consequently, although the result of performing the seed train process using the determined parameters may satisfy the success criteria when the culture doubling time is equivalent to the historical average, this may not be the case when the culture has a doubling time that is greater or less than the historical average. For example, if the true doubling time is less than the average, then performing the seed train process according to the determined parameters may result in overgrowth. If the true doubling time is greater than the average, then the duration of the seed train process may exceed a designated duration for performing the seed train process.In other words, the outcome (or outcomes) (e.g., duration, viable cell density) of the seed train process may not meet the success criteria.

[0072] Consequently, in some embodiments, technique 100 includes determining, in act 120, the probability of satisfying the success criteria of the seed train process using the parameters determined in act 110. In some embodiments, this includes evaluating multiple input scenarios. An input scenario may include the parameters determined in act 110, and a set of doubling times randomly sampled from normal distributions calculated from historical cell growth data 104. For example, the set of doubling times may include a doubling time for each stage of the seed train process. Petition 870250082199, dated 12 / 09 / 2025, page 57 / 156 42 / 107 An input scenario can represent a potential set of conditions under which the seed train process could be carried out. In some embodiments, at least 10, at least 25, at least 75, at least 100, at least 150, at least 200, at least 250, at least 300, at least 350, at least 400, at least 450, at least 500, at least 550, at least 600, at least 650, at least 700, at least 800, at least 900, at least 1000, at least 1500, at least 2000, or any other suitable number of input scenarios are evaluated, since aspects of the technology are not limited in this respect.

[0073] In some embodiments, historical cell growth data 104 include cell doubling time data. For example, cell doubling time data may include cell doubling time data from one or more previous seed train processes. Cell doubling time data may include cell doubling times for each of one or more stages of previous seed train processes. In some embodiments, the mean and standard deviation of doubling time at each stage is calculated from the data. In some embodiments, the normal distribution of doubling time at each stage is calculated by fitting the mean and standard deviation.

[0074] In some embodiments, determining the probability of satisfying the success criteria of the seed train process includes, for each of multiple input scenarios, (a) estimating the outcome (or outcomes) (e.g., duration, final viable cell density, etc.) of carrying out the seed train process. Petition 870250082199, dated 12 / 09 / 2025, page 58 / 156 43 / 107 seeds according to the input scenario and (b) determine whether the estimated outcome (or outcomes) satisfies the success criteria of the seed train process. For example, one or more outcomes may be estimated for each stage of the seed train process, and then compared with the success criteria of the seed train process for that particular stage. In some embodiments, if at least a limit number of outcomes satisfy their respective success criteria, that input scenario may be considered a successful scenario. For example, if all outcomes satisfy their respective success criteria at each stage, the input scenario may be considered a successful scenario. In some embodiments, the probability of satisfying the success criteria of the seed train process is the ratio between the number of successful scenarios and the total number of input scenarios evaluated.

[0075] In some embodiments, a simulation module is used to determine the probability of satisfying the success criteria of the seed train process. For example, simulation module 184, described in this document including at least in relation to Figure 1B, can be used to determine the probability of satisfying the success criteria of the seed train process. In some embodiments, the simulation module performs Monte Carlo simulations to evaluate different input scenarios. Monte Carlo simulations can be repeated any appropriate number of times for a given input scenario. For example, Monte Carlo simulations can be repeated at least 5 times, at least 10 times, by Petition 870250082199, dated 12 / 09 / 2025, page 59 / 156 44 / 107 minus 25 times, at least 50 times, at least 75 times, at least 100 times, at least 125 times, at least 150 times, at least 175 times, at least 200 times, or any other suitable number of times, as the aspects of the technology described herein are not limited in this respect.

[0076] In some embodiments, technique 100 includes determining, in act 130, whether to implement one or more stages of the seed train process. The determination may be based on the determined probability of meeting the success criteria of the seed train process. For example, in some embodiments, act 130 includes determining whether the probability of meeting the success criteria of the seed train process exceeds a threshold. The threshold may be any suitable threshold, such as at least 40%, at least 50%, at least 60%, at least 70%, at least 80%, at least 85%, at least 90%, at least 95%, at least 98%, or any other suitable threshold, since the aspects of the technology described herein are not limited to any particular threshold. In some modalities, if the probability satisfies the limit, then technique 100 proceeds to act 140 to implement one or more stages of the seed train process.If the probability does not meet the threshold, then technique 100 terminates or optionally, although not shown, returns to act 110 to determine seed train process parameters.

[0077] In some modalities, act 130 is performed by a processor and / or an individual. When act 130 is performed by an individual, the probability of satisfying the Petition 870250082199, dated 12 / 09 / 2025, page 60 / 156 45 / 107 success criteria for the seed train process can be issued to the user. For example, the probability can be issued through a user interface display. Additionally or alternatively, a processor can issue a recommendation on whether to implement one or more stages of the seed train process (e.g., through a user interface). In some embodiments, the individual manually determines whether to implement the stage(s) of the seed train process based on the probability of the output satisfying the success criteria and / or based on the processor's recommendation. When act 130 is performed by the processor, the processor can automatically determine, based on the probability of satisfying the success criteria, whether to implement one or more stages of the seed train process.

[0078] In some embodiments, if it is determined that one or more stages of the seed train process must be implemented, technique 100 moves to act 140, during which the stage (or stages) is implemented. In some embodiments, the one or more stages include one, some, or all stages of the seed train process. The one or more stages may be implemented in accordance with the seed train process parameters determined in act 110 and the seed train process constraints 102. For example, a first stage of the seed train process may be carried out in accordance with the parameters determined for the first stage and the constraints specified for the first stage. Additionally or alternatively, multiple stages of the seed train process may be carried out in accordance with their respective Petition 870250082199, dated 12 / 09 / 2025, page 61 / 156 46 / 107 Seed train process parameters and constraints.

[0079] In some embodiments, one or more stages of the seed train process are implemented automatically or semi-automatically. For example, an automation module, such as the seed train process automation module 188 described herein, including at least in relation to Figure 1B, may be used to control an automation system configured to implement one or more stages of the seed train process. The automation may include any components suitable for automating one or more stages of the seed train process. For example, the automation system may include an environmental control system configured to control the environment (e.g., temperature, gas, pressure, pH, etc.).) in which the cell culture is being cultivated, an imaging system, one or more robotic components configured to administer fluids (e.g., culture medium), and / or any other components suitable for automatically or semi-automatically implementing one or more stages of the seed train process.

[0080] In some embodiments, real-time crop growth data 142 are generated during the implementation of one or more stages of the seed train process. In some embodiments, the real-time crop growth data 142 include data that may affect one or more subsequent stages of the seed train process and / or one or more subsequent seed train processes. For example, the data 142 may include indicative data (e.g., specifying or otherwise indicating) of the duration Petition 870250082199, dated 12 / 09 / 2025, page 62 / 156 47 / 107 of the stage (or stages) of the seed train process, cell doubling time, final viable cell density and / or any other suitable data, as the aspects of the technology described herein are not limited in this respect. In some embodiments, real-time crop growth data 142 may be used to update the constraints 102 and / or may be used to determine the seed train process parameters for subsequent stages of the seed train process.

[0081] In some embodiments, after the implementation of one or more stages of the seed train process in act 140, technique 100 moves to act 145 to determine whether an implementation result of the stage (or stages) of the seed train process satisfies one or more success criteria. For example, this may include determining whether the duration of cell growth during the particular stage (or stages) exceeds a limit duration. When the duration exceeds the limit, this may introduce delays that cause the total duration of the seed train process to exceed the time planned for it. Consequently, technique 100 may terminate when the duration exceeds the limit duration; otherwise, technique 100 proceeds to act 150.As another example, determining whether an outcome of implementing a stage (or stages) of the seed train process satisfies one or more success criteria includes determining whether a final viable cell density exceeds a viable cell threshold. When the final viable cell density exceeds the threshold, this may indicate excessive cell growth, which can cause cell death and decrease the... Petition 870250082199, dated 12 / 09 / 2025, page 63 / 156 48 / 107 culture quality. Consequently, technique 100 can end when the final viable cell density exceeds the limit; otherwise, technique 100 proceeds to act 150.

[0082] Act 150 includes determining if there are additional stages of the seed train process. If there are no additional stages and the seed train process is complete, then technique 100 ends. If the seed train process is incomplete, meaning there is a stage (or stages) of the seed train process that has not yet been implemented, then the seed train process may be continued.

[0083] In some embodiments, if the seed train process is continued, technique 100 returns to act 110 to determine updated seed train process parameters for subsequent stages of the seed train process. For example, seed train process parameters may be updated to account for real-time crop growth data 142. In some embodiments, after determining the updated seed train process parameters, one or more of acts 120, 130, 140 of technique 100 may be repeated based on the updated seed train process parameters.

[0084] Figure 1B is a block diagram of an exemplary system 160 for determining seed train process parameters for a seed train process, according to some embodiments of the technology described in this document. The system 160 includes the computing device 170 which is configured so that the software 180 runs on it to perform various functions in Petition 870250082199, dated 12 / 09 / 2025, page 64 / 156 49 / 107 connection with the design and / or implementation of one or more stages of a seed train process. In some embodiments, the software includes a plurality of modules. A module may include processor-executable instructions that, when executed by at least one computer hardware processor, cause that at least one computer hardware processor to perform the function (or functions) of the module. Such modules are sometimes referred to herein as software modules. Each of which includes processor-executable instructions configured to perform one or more processes, such as the processes described herein, including at least those relating to Figures 2, 3A, and 4.

[0085] Computing device 170 may be one or more computing devices of any suitable type. For example, computing device 170 may be operated by one or more users 176, such as one or more individuals who are designing and / or implementing a seed train process. Additionally or alternatively, user (or users) 176 may include one or more individuals associated with the facility where a seed train process is performed. For example, user (or users) 176 may provide, as input to computing device 170 (e.g., uploading one or more files), seed train process constraints, initial seed train process parameters, seed train process success criteria, historical cell growth data, and / or any other suitable input as aspects of the technology described herein are not limited to any type. Petition 870250082199, dated 12 / 09 / 2025, page 65 / 156 50 / 107 particular input. Additionally or alternatively, in some embodiments, the user (or users) 176 may provide user input specifying processing or other methods to be performed on seed train process constraints, seed train process initial parameters, seed train process success criteria, historical cell growth data and / or other suitable data.

[0086] As shown in Figure 1B, the software 180 includes several software modules for designing and / or implementing a seed train process, such as a parameter optimization module 182, a simulation module 184, a common interface module 186, and a seed train process automation module 188.

[0087] In some embodiments, the common interface module 186 is configured to generate a graphical user interface (GUI) to display user (or users) seed train process constraints 176, seed train process parameters, estimated seed train process result(s), an indication of whether the estimated result(s) satisfy seed train process success criteria, and / or any other suitable information, as aspects of the technology described herein are not limited to displaying any particular type. In some embodiments, the common interface module 186 allows multiple different users 176 to view, enter, and / or update such information in a consistent manner. Examples of a display by the common interface module 186 are shown in Figure 5A-1 to Figure 5D. Petition 870250082199, dated 12 / 09 / 2025, page 66 / 156 51 / 107

[0088] In some embodiments, user (or users) 176 may provide user input via a GUI (e.g., generated by the common interface module 186) to update one or more seed train process constraints and / or seed train process parameters. For example, user (or users) 176 may provide input to update a constraint that reflects a change in the installation schedule, thus allowing a longer or shorter duration to perform one or more stages of the seed train process. Additionally or alternatively, user (or users) 176 may provide input indicating initial and / or preferred seed train process parameters (e.g., before or instead of determining seed train process parameters). In some embodiments, the common interface module 186 automatically updates the display to reflect the user changes.For example, the common interface module 186 can automatically generate a GUI to display the updated constraint and / or parameter, as well as the updated estimated result(s) resulting from these changes.

[0089] In some embodiments, the parameter optimization module 182 is configured to determine seed train process parameters to perform one or more stages of a seed train process. For example, this may include determining, for each particular stage of the seed train process, a respective set of seed train process parameters. Exemplary seed train process parameters are listed in Table 2. In some embodiments, the parameter optimization module 182 is configured to determine the parameters Petition 870250082199, dated 12 / 09 / 2025, page 67 / 156 52 / 107 of seed train process automatically, or in response to a user instruction (or users) 176 (for example, by providing input through a GUI generated by the common interface module 186).

[0090] In some embodiments, the parameter optimization module 182 determines the seed train process parameters by performing act 110 in Figure 1A, act 204 in Figure 2, and / or one or more acts of the process 300 described herein, including at least those related to Figure 3A. For example, in some embodiments, the parameter optimization module 182 uses a genetic algorithm to determine the seed train process parameters. For example, the genetic algorithm may test candidate seed train process parameters against an objective function, such as the objective function in Figure 3B, to determine the seed train process parameters.

[0091] In some embodiments, the parameter optimization module 182 is configured to determine seed train process parameters using seed train process constraints. As described in this document, in some embodiments, seed train process constraints are aspects of the seed train process that cannot be varied. Seed train process constraints can define ranges from which seed train process parameters must be selected (e.g., upper and lower limits of initial viable cell density). Additionally or alternatively, seed train process constraints can be used to define seed train process success criteria (e.g., time Petition 870250082199, dated 12 / 09 / 2025, page 68 / 156 53 / 107 assigned to perform one stage of the seed train process). Examples of seed train process constraints are listed in Table 2.

[0092] The parameter optimization module 182 can obtain (e.g., pull or receive) seed train process constraints from the seed train process constraint data store 174 and / or from the user (or users) 176 (e.g., by the user (or users) uploading the seed train process constraints). For example, the user (or users) 176 can upload seed train process constraints using the common interface module 186.

[0093] In some embodiments, the parameter optimization module 182 is additionally or alternatively configured to determine seed train process parameters using historical cell growth data. For example, historical cell growth data may include cell doubling time data from one or more previous seed trains. Cell doubling time data may include cell doubling times for each of one or more stages of previous seed train processes. In some embodiments, the mean and standard deviation of the doubling time at each stage is calculated from the data. In some embodiments, the parameter optimization module 182 may assume an average doubling time for each stage of the seed train process in determining the seed train process parameters.

[0094] The parameter optimization module 182 can obtain (e.g., pull or receive) average duplication times Petition 870250082199, dated 12 / 09 / 2025, page 69 / 156 54 / 107 of the storage of historical cell growth data 172 and / or of the user (or users) 176 (for example, by the user (or users) uploading the historical cell growth data).

[0095] In some embodiments, the common interface module 186 is configured to generate a GUI to display the parameters determined using the parameter optimization module 182. For example, the common interface module 186 may populate a display with the determined parameters, or it may replace the initial parameters of the seed train process, in an existing display, with the determined parameters. The GUI may additionally or alternatively display the estimated result(s) (e.g., duration, final viable cell density, etc.) of implementing one or more stages of the seed train process using the determined parameters. The GUI may additionally or alternatively display an indication of whether the estimated result(s) satisfy one or more success criteria of the seed train process.In some embodiments, user (or users) 176 can interact with the GUI to update the seed train process parameter (or parameters) even after it has been determined using the parameter optimization module 182. This can allow user (or users) 176 flexibility in designing the seed train process according to user preferences or expectations.

[0096] In some embodiments, simulation module 184 is configured to determine an indicative probability that the seed train process will be carried out using the determined seed train process parameters. Petition 870250082199, dated 12 / 09 / 2025, page 70 / 156 55 / 107 using parameter optimization module 182 will satisfy at least one seed train process success criterion. This may include performing act 120 in Figure 1A, act 206 in Figure 2, and / or one or more acts of the 400 process described herein, including at least those relating to Figure 4. For example, simulation module 184 can be configured to evaluate multiple input scenarios using Monte Carlo simulation(s). In some embodiments, as described herein, Monte Carlo simulations are used to predict whether a particular input scenario will produce an outcome (or outcomes) that satisfies one or more success criteria. If the outcome (or outcomes) is predicted to satisfy at least a threshold number of success criteria (e.g., all success criteria for all stages of the seed train process), then the input scenario can be considered a successful scenario.In some modalities, the probability of satisfying the success criteria of the seed train process is the ratio between the number of successful scenarios and the total number of input scenarios evaluated.

[0097] In some embodiments, simulation module 184 is configured to use historical cell growth data to determine the probability of meeting the success criteria of the seed train process. For example, an input scenario evaluated by simulation module 184 might include a set of doubling times randomly sampled from normal distributions calculated from historical cell data. For example, the set of times of Petition 870250082199, dated 12 / 09 / 2025, page 71 / 156 56 / 107 duplication may include a duplication time for each stage of the seed train process.

[0098] Simulation module 184 can obtain (e.g., pull or receive) historical cell growth data from historical cell growth data storage 172 and / or from the user (or users) 176 (e.g., by the user (or users) uploading historical cell growth data). For example, user (or users) 176 can upload historical cell growth data using common interface module 186.

[0099] In some embodiments, common interface module 186 is configured to output the probability of satisfying the success criteria of the seed train process as determined by simulation module 184. For example, common interface module 186 can output probability-indicative information, such as a ratio, a percentage, a graph, or any other suitable probability-indicative information.Additionally or alternatively, in some embodiments, the common interface module is configured to output a probability of meeting the success criteria for the individual stage (or stages) of the seed train process.

[00100] In some embodiments, user (or users) 176 can determine, based on the probabilities issued of satisfying the success criteria, whether it is necessary to implement one or more stages of the seed train process. If user (or users) 176 determines the implementation of one or more stages of the seed train process, in some embodiments, they can instruct the seed train process automation module 188 to Petition 870250082199, dated 12 / 09 / 2025, page 72 / 156 57 / 107 implement one or more stages automatically or semi-automatically. Additionally or alternatively, the user (or users) 17 6 ​​can manually implement one or more stages of the seed train process.

[00101] In some embodiments, the seed train process automation module 188 receives from the simulation module 184 the probability of satisfying the success criteria of the seed train process. The seed train process automation module 188 can be configured to determine, based on the received probability, whether to implement one or more stages of the seed train process. For example, if the probability equals or exceeds a threshold (i.e., there is a relatively high probability of satisfying the success criteria of the seed train process using the determined seed train process parameters), the seed train process automation module 188 can determine the implementation of one or more stages of the seed train process.Conversely, if the probability does not equal or exceeds the threshold (that is, there is a relatively low probability of satisfying the success criteria of the seed train process using the determined seed train process parameters), the seed train process automation module 188 may determine not to implement one or more stages of the seed train process.

[00102] In some embodiments, the seed train process automation module 188 can be further configured to control a seed train process automation system 190. For example, the module of Petition 870250082199, dated 12 / 09 / 2025, page 73 / 156 58 / 107 Seed train automation can control the 190 seed train process automation system to implement one or more stages of the seed train process. In some embodiments, the 190 seed train process automation system includes any components suitable for automating one or more stages of the seed train process. For example, the 190 automation system may include an environmental control system configured to control the environment (e.g., temperature, gas, pressure, pH, etc.) in which the cell culture is being grown, an imaging system, one or more robotic components configured to administer fluids (e.g., culture medium), and / or any other components suitable for automatically or semi-automatically implementing one or more stages of the seed train process.

[00103] The common interface module 186 can be configured to generate a graphical user interface (GUI), a text-based user interface, and / or any other suitable type of interface through which a user can provide input and display information generated by the software 180. For example, in some embodiments, the common interface may be a web page or web application accessible through an internet browser. In some embodiments, the user interface may be a graphical user interface (GUI) of an application running on the user's mobile device. In some embodiments, the user interface may include various selectable elements through which a user can interact. For example, the user interface may Petition 870250082199, dated 12 / 09 / 2025, page 74 / 156 59 / 107 include drop-down lists, checkboxes, text fields, or any other suitable element.

[00104] Figure 2 is a flowchart of an illustrative process 200 for determining seed train process parameters for a seed train process that has multiple stages, according to some embodiments of the technology described in this document. One or more process acts 200 may be performed automatically by any suitable computing device (or devices). For example, the act (or acts) may be performed by a laptop computer, a desktop computer, one or more servers, in a cloud computing environment, a computing device 900 as described in this document in relation to Figure 9, and / or in any other suitable manner. For example, in some embodiments, act 202 may be performed automatically by any suitable computing device (or devices). As another example, act 204 may be performed automatically by any suitable computing device (or devices).

[00105] In Act 202, the processor obtains a seed train process constraint specification for multiple stages of a seed train process. As described in this document, seed train process constraints are aspects of the seed train process that cannot be varied. For example, seed train process constraints may include at least some of the seed train process constraints listed in Table 1 and / or the seed train process constraints described in this document, including at least some of them. Petition 870250082199, dated 12 / 09 / 2025, p. 75 / 156 60 / 107 in relation to Figure 1A.

[00106] In some embodiments, seed train process constraints include one or more constraints for each of the multiple stages of the seed train process. For example, the first seed train process constraints may be specified for a first stage in the seed train process, and the second seed train process constraints may be specified for a second stage in the seed train process. In some embodiments, different seed train process constraints are specified for different stages (e.g., the first and second stages). For example, some seed train process constraints, such as the upper and lower working volume thresholds, may depend on the volume of container (or containers) used to grow a cell culture during a particular stage.If the volume of containers increases with each stage, the upper and lower thresholds of the working volume range will also increase. Figures 5A-1 to 5C-2 show exemplary seed train process constraints specified for each of stages N8 to N-0 of an exemplary seed train process.

[00107] In some embodiments, the processor obtains the specification of seed train process constraints using a common interface module. For example, the common interface module may include the common interface module 186 described herein, including at least as to Figure 1B. In some embodiments, the common interface module indicates relevant seed train process constraints and includes fields that store values. Petition 870250082199, dated 12 / 09 / 2025, page 76 / 156 61 / 107 for each of the seed train process constraints. The user (or users) can interact with a GUI generated by the common interface module to provide and / or update values ​​for the seed train process constraints (e.g., using text boxes or other selectable elements of a graphical user interface). As an example, when a seed train process is implemented in a facility where several other processes (e.g., other seed train processes) are implemented, facility users can interact with the GUI to update facility-related constraints such as available equipment (e.g., containers), schedule constraints, and any other appropriate constraints, as the technology aspects are not limited in this regard.Consequently, by obtaining the seed train process constraints through the common interface module, the processor can obtain updated seed train process constraints, which can be used to design a seed train process that conforms to the plant schedule. Additionally or alternatively, the processor obtains the seed train process constraint specification from a data store (e.g., seed train process constraint data store 174 in Figure 1B), or using any other suitable techniques, as the aspects of the technology described herein are not limited in this respect.

[00108] In act 204, the processor determines seed train process parameters using process constraints. Petition 870250082199, dated 12 / 09 / 2025, page 77 / 156 62 / 107 of seed train. As described in this document, seed train process parameters are aspects of the seed train process that can be varied. For example, seed train process parameters may include at least some of the seed train process parameters listed in Table 2 and / or the seed train process parameters described in this document, including at least those related to Figure 1A.

[00109] In some embodiments, the processor determines seed train process parameters for each particular stage of the multiple stages of the seed train process, a respective set of seed train process parameters for cultivating the crop during the particular stage. For example, this might include determining a first set of seed train process parameters for a first stage in the seed train process and a second set of seed train process parameters for a second stage in the seed train process. Figures 5B-1 to 5C-2 show exemplary seed train process parameters determined for each of stages N-8 to N-0.

[00110] In some embodiments, the processor determines the seed train process parameters using a parameter optimization module, such as the parameter optimization module 182 described in this document, including at least with respect to Figure 1B.

[00111] In some embodiments, the determination of seed train process parameters includes determining the seed train process parameters using an optimization technique. Any optimization technique Petition 870250082199, dated 12 / 09 / 2025, page 78 / 156 63 / 107 suitable embodiments may be used, including, for example, a genetic algorithm, a random search, or a grid search, as the embodiments of the technology described in this document are not limited in this respect. In some embodiments, a genetic algorithm may take as input an objective function and thresholds for each variable in the function. For example, the thresholds may be defined by seed train process constraints, and the objective function may include the exemplary objective function in Figure 3B. In some embodiments, the genetic algorithm generates a set of values ​​for variables in the objective function, randomly chosen from the ranges defined by the specified thresholds, which change through iterations until a set of values ​​is found that yield a global minimum (or maximum). In some embodiments, a genetic algorithm is a differential evolution algorithm. For example, Storn, R. and Price, K.(Differential Evolution - A simple and efficient adaptive scheme for global optimization over continuous spaces), which is incorporated by reference into this document in its entirety, describes an exemplary differential evolution algorithm. Exemplary techniques for determining seed train process parameters are described in this document, including at least in relation to Figure 3A.

[00112] In some embodiments, in determining the parameters of the seed train processor, the processor can estimate one or more implementation outcomes of one or more stages of the seed train process using a prospective set of parameters and constraints. Petition 870250082199, dated 12 / 09 / 2025, page 79 / 156 64 / 107 specified. For example, this might include estimating the outcome (or outcomes) of implementing a first stage of the seed train process using a first set of prospective parameters for that stage and the constraints that were specified for the first stage. In some embodiments, the estimated outcomes include an estimated duration required to grow the cell culture during a particular stage of the seed train process and / or an estimated final viable cell density resulting from the growth of the cell culture during the particular stage.

[00113] In some embodiments, the seed train process parameters are determined in Act 204 such that one or more (e.g., one, some, or all) of the estimated implementation results of one or more stages of the seed train process satisfy the success criteria of the seed train process. As described herein, including at least with regard to Figure 1A, the success criteria may include: (a) a criterion that an expected duration required to cultivate the crop during a particular stage does not exceed a limit duration for crop growth during the particular stage, and / or (b) a criterion that an expected viable cell density resulting from crop growth during a particular stage of the seed train process does not exceed a limit viable cell density.

[00114] In some modalities, when multiple prospective sets of parameters result in outcomes that satisfy the success criteria, only one set of prospective parameters can be identified as the. Petition 870250082199, dated 12 / 09 / 2025, page 80 / 156 65 / 107 seed train process parameters. For example, when testing prospective seed train process parameters against an objective function, the processor can select the parameters that result in an objective function value that meets at least one criterion. For example, prospective parameters that result in the highest or lowest objective function value can be selected as seed train process parameters.

[00115] In act 206, the processor determines an indicative probability of the likelihood that performing the seed train process using the determined seed train process parameters will satisfy the seed train process success criteria. In some embodiments, the processor determines the probability using a simulation module, historical cell growth data, and the determined seed train process parameters. The simulation module may include simulation module 184 in Figure 1B.

[00116] In some embodiments, as described in this document, historical cell growth data include data related to the cell growth rate from past seed train experiments. For example, historical cell growth data may include culture doubling time data from past seed train processes. Doubling time data may include a distribution of doubling time values ​​for each of the multiple stages of past seed train processes.

[00117] Since, in some embodiments, the results of carrying out a seed train process using the Petition 870250082199, dated 12 / 09 / 2025, page 81 / 156 66 / 107 determined seed train process parameters are estimated based on only one or a few doubling times (e.g., an average historical doubling time, a shorter historical doubling time, and / or a longer historical doubling time). The estimated results do not represent other potential doubling times (e.g., those included in doubling time distributions). Consequently, the estimated results may not accurately reflect the actual results of performing the seed train process. For example, cultivating a crop during a particular stage of the seed train process takes longer than an estimated duration when the estimated duration is based on the average historical doubling time, and the actual doubling time is longer than the historical average.In this example, although the estimated duration may meet the success criteria, the actual duration may not meet the success criteria.

[00118] Consequently, in some embodiments, it may be beneficial to determine the probability of meeting the success criteria before implementing one or more stages of the seed train process using the determined seed train process parameters. If the probability of meeting the success criteria is relatively low, this may indicate that implementing the seed train process using the determined seed train process parameters is likely to violate the success criteria (for example, it may result in excessive cell growth or take longer than the designated time to implement a particular stage). Petition 870250082199, dated 12 / 09 / 2025, page 82 / 156 6Ί / 10Ί

[00119] In some embodiments, determining the probability of meeting the success criteria of the seed train process includes (a) sampling historical cell growth data, (b) simulating the results of performing the seed train process using the sampled historical cell growth data and the seed train process parameters determined in Act 204, and (c) determining whether the simulated results meet the success criteria. The techniques for determining the probability of meeting the success criteria are described herein, including at least with respect to Figure 4.

[00120] In act 208, the processor outputs (a) the set of seed train process parameters determined for each stage of the multiple stages of the seed train process and (b) the indicative probability of the likelihood that performing the seed train process using the determined seed train parameters will satisfy the seed train process success criteria.

[00121] In some embodiments, the processor outputs the seed train process parameter sets and / or probabilities via a common interface module. For example, Figures 5B-1 to 5D show a set of seed train process parameters determined for each of stages N-8 to N-1. In some embodiments, the parameters and / or probability are displayed via a user interface, such as a GUI generated by the common interface module 186 in Figure 1B. Additionally or alternatively, in some embodiments, the parameters and / or probability are output (e.g., transmitted) to a seed train process automation module. Petition 870250082199, dated 12 / 09 / 2025, p. 83 / 156 68 / 107 as well as the seed train process automation system 190 in Figure 1B. Additionally or alternatively, in some embodiments, the parameters and / or probability are output to a data storage, or output using any other suitable output technique, since the aspects of the technology described in this document are not limited in this respect.

[00122] In some embodiments, the indicative probability of the likelihood that performing the seed train process using the determined seed train process parameters will satisfy the success criteria is used to determine whether to implement one or more stages of the seed train process. For example, this might include determining the implementation of one or more stages of the seed train process when the exit probability exceeds a threshold probability. In some embodiments, a user, such as a scientist, manually determines whether to implement one or more stages of the seed train process. In some embodiments, the decision to implement one or more stages is made automatically or semi-automatically using a processor. For example, the seed train process automation module 188 in Figure 1B can automatically determine whether to implement one or more stages of the seed train process.

[00123] Although not shown, in some embodiments, process 200 may include the implementation of one or more stages of the seed train process using the determined seed train process parameters. For example, one or more stages of the seed train process may be implemented manually (e.g., by a user), from Petition 870250082199, dated 12 / 09 / 2025, page 84 / 156 69 / 107 automatic or semi-automatic form. For example, a seed train process automation system, such as the seed train process automation system 190 in Figure 1B, can implement one or more stages of the seed train process automatically or semi-automatically.

[00124] It should be understood that process 200 may include one or more additional or alternative acts not shown in Figure 2A. For example, process 200 may include an act to determine whether to implement one or more stages of a seed train process and / or an act to implement one or more stages of the seed train process.

[00125] Figure 3A is a flowchart of an illustrative process 300 for determining seed train process parameters using the parameter optimization module and seed train process constraints, according to some embodiments of the technology described in this document. In some embodiments, process 300 is an exemplary implementation of act 204 of process 200 in Figure 2.

[00126] One or more process acts 300 may be performed automatically by any suitable computing device (or devices). For example, the act (or acts) may be performed by a laptop computer, a desktop computer, one or more servers, in a cloud computing environment, a computing device 900 as described herein in relation to Figure 9, and / or in any other suitable manner. In some embodiments, a software module, such as the parameter optimization module 182 in Figure 1B, is configured. Petition 870250082199, dated 12 / 09 / 2025, page 85 / 156 70 / 107 to perform process 300.

[00127] In some embodiments, process 300 includes evaluating multiple prospective sets of seed train process parameters to identify seed train process parameters that, when used to implement a seed train process, will result in a seed train process outcome that satisfies seed train process success criteria. For example, the multiple prospective sets of parameters may include any appropriate number of prospective sets of parameters, including first prospective parameters and second prospective parameters. The first prospective parameters and the second parameters may not include any of the same parameters or may include one or more of the same parameters.

[00128] In some embodiments, a prospective set of parameters includes prospective seed train process parameters for each stage in the seed train process. In some embodiments, each prospective parameter for a particular stage in the seed train process is selected to meet the seed train process constraints for that stage. For example, a prospective work volume for a first stage of the seed train process may be selected such that it is within (i.e., equivalent to) the upper work volume limit and lower work volume limit for the first stage, both of which are seed train process constraints for the first stage.

[00129] In act 302, the processor determines an initial score for the first train process parameter of Petition 870250082199, dated 12 / 09 / 2025, page 86 / 156 71 / 107 prospective seeds. For example, determining an initial score may involve using the initial prospective seed train process parameters to determine a value for an objective function, such as the objective shown in Figure 3B. In some embodiments, as described in more detail in this document, the objective function accounts for each of the success criteria. Consequently, in some embodiments, the resulting value of the objective function reflects whether the use of the initial prospective parameters to implement one or more stages of the seed train process will result in outcomes that satisfy the success criteria for each stage. Additionally or alternatively, in some embodiments, the resulting value of the objective function reflects the degree to which the success criteria will be met.For example, a relatively large (or relatively small) value for the objective function may indicate that using the first prospective parameters to implement one or more stages of the seed train process will result in outcomes that satisfy the success criteria to a greater degree.

[00130] In some embodiments, determining the degree to which the estimated outcomes satisfy the success criteria of the seed train process includes determining a series of estimated outcomes that satisfy the success criteria of the seed train process. For example, multiple outcomes may be estimated for each stage in the seed train process. In some embodiments, the value of the objective function is higher (or lower) when more of the estimated outcomes satisfy the success criteria at each stage. For example, the value of the objective function may Petition 870250082199, dated 12 / 09 / 2025, page 87 / 156 72 / 107 is greater when the estimated results meet all criteria for each stage, compared to the objective function value when the estimated results meet only some of the success criteria for one or more stages.

[00131] Additionally or alternatively, in some embodiments, determining the degree to which the estimated results satisfy the seed train process criteria includes determining the degree to which each individual estimated result satisfies a respective success criterion. Consider, for example, an estimated duration of 5 hours to cultivate cells during the first stage of the seed train process and a threshold value of 10 hours. In the example, the success criterion is satisfied when the estimated duration does not exceed the threshold. Here, the success criterion is satisfied since the estimated duration of 5 hours does not exceed 10 hours. The estimated duration of 5 hours also satisfies the success criterion to a greater degree than an estimated duration of 8 hours, since the estimated duration of 8 hours is closer to the threshold duration.

[00132] In Act 304, a second score is determined for the second prospective seed train process parameters. As described in relation to Act 302, determining a score for prospective seed train process parameters, in some embodiments, includes determining a value for an objective function, such as the objective function shown in Figure 3B.

[00133] In act 306, the processor compares the first score determined for the first prospective parameters and the second score determined for the Petition 870250082199, dated 12 / 09 / 2025, page 88 / 156 73 / 107 second prospective parameters. For example, this might include determining the relative value of the first score in relation to the second score.

[00134] In Act 308, based on a comparison result, the processor selects the seed train process parameters from among the first prospective parameters and the second prospective parameters. In some embodiments, this includes selecting the set of prospective parameters for which the highest score (or lowest score) was determined. For example, if it is determined that the first score is higher than the second score in Act 306, then the first prospective parameters may be selected as the seed train process parameters. In some embodiments, a higher (or lower) score may indicate that using the prospective parameters, for which that score was determined, to implement a stage of the seed train process will result in an outcome that satisfies the success criteria to a greater degree.

[00135] It should be understood that one or more additional prospective sets of parameters may be evaluated during process 300, and that process 300 is not limited to evaluating only first prospective parameters and second prospective parameters. Instead, any appropriate number of prospective parameter sets may be evaluated and used to determine the seed train process parameters. For example, process 300 may include determining a third score for the third prospective parameters, comparing the first, second, and third scores, and selecting the parameters. Petition 870250082199, dated 12 / 09 / 2025, page 89 / 156 74 / 107 of the seed train process among the first, second, and third prospective parameters. Additionally or alternatively, the processor may search for prospective seed train process parameters within the seed train process constraints until a combination of prospective seed train process parameters is found resulting in a boundary value of the objective function.

[00136] Figure 3B shows an exemplary objective function used to determine seed train process parameters according to some embodiments of the technology described in this document. In some embodiments, the objective function is used to determine seed train process parameters as part of process 300 in Figure 3A. For example, seed train process parameters can be searched to identify a combination of seed train process parameters that maximize equation 352.

[00137] As shown, the exemplary objective function, fobj, 352 can be calculated as a sum of functions f1, f2, f3, f4 and f5.

[00138] In some embodiments, the function, f1, 354 represents at least one success criterion. For example, the function 354 may consider as a success criterion that the extra time required to grow cells during each stage (e.g., stages 1 and 2) of the seed training process is less than a time limit for that particular stage, where the extra time required to grow the cells is based on the normal doubling time for the stage. As described in this document, the time of Petition 870250082199, dated 12 / 09 / 2025, page 90 / 156 75 / 107 normal doubling time for a particular stage may be the average doubling time for that stage based on historical cell growth data. n may be equal to the total number of stages in the seed train process. Alternatively, n may be less than the total number of stages in the seed train process. For example, Figures 5A-1 to 5D show that stages N-3 and N-2 each use the same container. In this example, the two stages can only count as a single stage, as opposed to two different stages.

[00139] In relation to function 354, tnormal,1 refers to the extra time that is needed to cultivate the crop at a stage, i, of the seed train process, where the extra time is determined based on a normal doubling time.

[00140] As shown, the function 354 is determined by comparing the extra time required at each stage (e.g., stages 1 and 2) of the seed train process with a limiting extra time. In this case, the limiting extra time is 0. If the extra time required exceeds the limiting time for a particular stage, then the addendum for that particular stage is calculated by multiplying tnormal,i by -10. If the extra time required is below the limit, then the addendum for the particular stage is tnormal,i.

[00141] In some embodiments, the 356 function represents at least one other success criterion. For example, the 356 function may consider a success criterion to be that the extra time required to grow cells during each stage (e.g., stages 1 and 2) of the seed training process is less than a time limit for that particular stage, where the extra time required to grow the cells is Petition 870250082199, dated 12 / 09 / 2025, page 91 / 156 76 / 107 based on the worst doubling time for the stage. As described in this document, the worst doubling time for a particular stage may be the longest doubling time that is included in historical cell growth data for that stage.

[00142] In relation to function 356, tworse,1 refers to the extra time that is needed to cultivate the crop at a stage, i, of the seed train process, where the extra time is determined based on a worst doubling time for that stage.

[00143] As shown, the function 356 is determined by comparing the normal extra time required tnormal,i at each stage (e.g., stages 1 and an) of the seed train process with a limiting extra time. In this case, the limiting extra time is -12. If the extra time required exceeds the limiting time for a particular stage, then the addendum for that particular stage is calculated by multiplying tworst,i by 0.5. If the extra time required is below the limit, then the addendum for the particular stage is tworst,i multiplied by -5.

[00144] In some embodiments, the 358 function represents at least one other success criterion. For example, the 358 function may represent a success criterion that the final viable cell density resulting from cell growth during each stage (e.g., stages 1 and 2) of the seed train process is less than a limiting final viable cell density.

[00145] With respect to function 358, Cvcd,i refers to the final viable cell density resulting from growing cells during a stage, i, of the train process. Petition 870250082199, dated 12 / 09 / 2025, page 92 / 156 77 / 107 seeds. In some embodiments, the final viable cell density for each stage is calculated based on the best doubling time for the stage. For example, the best doubling time may refer to the shortest doubling time for the stage, as obtained from historical cell growth data. Climit refers to the limiting viable cell density for the particular stage. For example, the limiting viable cell density may include the upper limit of the final viable cell density for a particular stage of the seed train process, which is specified as part of obtaining seed train process constraints for the seed train process.

[00146] As shown, function 358 is determined by comparing the final viable cell density, Cvcd,i, with the limiting viable cell density, Climite,i, for each stage (e.g., stages 1 and 2) of the seed train process. If Cvcd,i is less than Climite,i, then the sum for that particular stage is calculated by multiplying the difference between Climite,i and Cvcd,i by 0.5. If Cvcd,i exceeds Climite,i, then the sum for that particular stage is calculated by multiplying the difference between Climite,i and Cvcd,i by -10.

[00147] In some embodiments, the 360 ​​function represents a safety margin to complete the seed train process within the time constraints for completing the seed train process. For example, if a stage has unexpected slow cell growth, the seed train process parameters should allow a sufficient safety margin to complete the seed train process. Petition 870250082199, dated 12 / 09 / 2025, page 93 / 156 78 / 107 seeds within the designated time. Having a sufficient safety margin regarding cell growth time during the next stage can help keep the seed train process duration in line with the facility schedule and / or the time designated to complete the seed train process. In some embodiments, this is done by balancing the extra time needed for cell growth during each stage.

[00148] In some embodiments, the 360 ​​function is determined by multiplying fstandard(tnormal,i) by -3, where fstandard(tnormal,i) is a function to determine the standard deviation of the extra time required (tnormal,i) for different stages of the seed train process, where the extra time required is based on the normal doubling time for each stage.

[00149] In some embodiments, the function 362 represents variations in the initial viable cell density. For example, it may not be desirable to vary from a recommended value of the initial viable cell density.

[00150] In some embodiments, the 362 function is determined based on the final viable cell density, Cvcd,n-1, for an earlier stage, n-1, of the seed train process, where Cvcd,n-1 is determined based on the normal doubling time for stage n-1. For example, the 362 function can be calculated by multiplying the absolute value of the difference between Cvcd,n-1 and 6 by -30.

[00151] Figure 4 is a flowchart of an illustrative process 400 to determine an indicative probability of the possibility of carrying out the seed train process using the seed train process parameters. Petition 870250082199, dated 12 / 09 / 2025, page 94 / 156 79 / 107 will satisfy the success criteria of the seed train process, according to some embodiments of the technology described in this document. In some embodiments, process 400 is an exemplary implementation of act 206 of process 200 in Figure 2.

[00152] One or more acts of process 400 may be performed automatically by any suitable computing device (or devices). For example, the act (or acts) may be performed by a laptop computer, a desktop computer, one or more servers, in a cloud computing environment, a computing device 900 as described herein in relation to Figure 9, and / or in any other suitable manner. In some embodiments, a software module, such as simulation module 184 in Figure 1B, is configured to perform process 400.

[00153] As described in this document, in some embodiments, the results of the seed train process depend on seed train process constraints and seed train process parameters used to implement each stage of the seed train process. Doubling time is one such seed train process constraint. However, doubling time varies in different seed train processes. Consequently, a result (e.g., duration of each stage, final viable cell density resulting from each stage, etc.) of the seed train process may depend on the particular doubling time for the crop being cultivated. Since this value is not known until after the implementation of one or more of the process stages Petition 870250082199, dated 12 / 09 / 2025, page 95 / 156 80 / 107 of seed train, it can be challenging to predict whether implementing the seed train process using specific seed train process parameters will result in an outcome that satisfies the seed train process success criteria. As described in this document, seed train process parameters can be determined by assuming a particular value for the doubling time for each stage, such as the average doubling time for each stage of one or more past seed train processes.

[00154] Consequently, it may be beneficial to predict, using process 400, based on historical cell growth data (e.g., historical doubling times), the probability that implementing the seed train process using the determined seed train process parameters will result in an outcome that satisfies the success criteria.

[00155] In act 402, the processor simulates an initial set of cell growth values ​​using historical cell growth data. In some embodiments, the historical cell growth data includes a distribution of historical cell growth values ​​for each stage of the seed train process. As described in this document, the historical cell growth values ​​for a particular stage of the seed train process may include historical doubling times for that particular stage in the seed train process.

[00156] In some modes, simulate the first set of cell growth history values. Petition 870250082199, dated 12 / 09 / 2025, page 96 / 156 81 / 107 includes random sampling of historical cell growth values ​​from historical cell growth data. This may include, for each stage, random sampling of a cell growth value from the distribution of cell growth values ​​for that particular stage. For example, simulating the first set of historical cell growth values ​​may include, for a first stage, random sampling of a doubling time from a distribution of historical doubling times for that stage.

[00157] In act 404, the processor predicts a first outcome of the seed train process using the seed train process parameters and the first set of cell growth parameters. In some embodiments, this includes predicting a respective outcome for each stage of the seed train process based on the historical sampled cell value (e.g., doubling time) for that stage. For example, predicting an outcome for a particular stage of the seed train process might include predicting a duration for crop growth during that particular stage. The duration for a particular stage might be predicted using Equation 1, for example. Expected Duration = Em^· / Minimum Final W (VolTotal \ VVCD required' \Ppop loss / ' (Working volume per vessel)*(Number of vessels)*(Initial VCD) Growth constant (Equation 1) where: Growth constant = In(2) Sampled doubling time (Equation 2)

[00158] Additionally or alternatively in some Petition 870250082199, dated 12 / 09 / 2025, page 97 / 156 82 / 107 methods, predicting an outcome for a particular stage includes predicting an amount of extra time that will be needed to grow the crop during that particular stage relative to a standard growing time (e.g., 72 hours). The extra time needed can be predicted using Equation 3, for example. Additional time required = Expected duration — Standard Culture Time (Equation 3)

[00159] Additionally or alternatively, in some embodiments, predicting an outcome for a particular stage includes predicting the final viable cell density resulting from the growth of the culture during that particular stage. The final viable cell density (VCD) resulting from VCD Final = In what way Equation 2

[00160] Equations predicting crop growth during a particular stage can be used, such as Equation 3, for example. (Initial VCD) * e((Normal growth constant)*(Standard culture time)) (Equation 4) normal doubling time is determined using the . In some embodiments, the variables included in 1-4 may correspond to the seed train process parameters and / or the seed train process constraints listed in Tables 1-2.

[00161] In act 406, the processor simulates a second set of cell growth values ​​using historical cell growth data. Techniques for simulating a set of cell growth values ​​are described in relation to act 402. In some embodiments, the first set of cell growth values ​​and the second set of cell growth values ​​are different. Petition 870250082199, dated 12 / 09 / 2025, page 98 / 156 83 / 107 For example, the first and second sets of cell growth values ​​may include only some of the same cell growth values, or they may include none of the same cell growth values.

[00162] In act 408, the processor predicts an initial outcome of the seed train process using the seed train process parameters and the first set of cell growth parameters. In some embodiments, this includes predicting a respective outcome for each stage of the seed train process based on the historical value of sampled cells (e.g., doubling time) for that stage. For example, this might include determining an expected duration for each stage, the extra time required for each stage, and / or the final viable cell density for each stage. Such outcomes can be determined using Equations 1-4.

[00163] In act 410, the processor determines the indicative probability that performing the seed train process using the seed train process parameters will satisfy the seed train process success criteria. The probability is determined using the first and second results. For example, the first and second results may each include one or more predicted results for each stage of the seed train process.

[00164] In some embodiments, determining probability includes, in 410-1, determining a series of predicted outcomes that indicate that the seed train process will be carried out using the seed train process parameters and the respective sets of historical values. Petition 870250082199, dated 12 / 09 / 2025, page 99 / 156 84 / 107 of cell growth will satisfy the success criteria of the seed train process. In some embodiments, this includes determining whether the first outcome and the second outcome satisfy the success criteria of the seed train process. Additionally or alternatively, this includes determining whether one or more other predicted outcomes satisfy the success criteria of the seed train process. For example, the process may predict at least 5, at least 10, at least 15, at least 25, at least 50, at least 75, at least 100, at least 150, at least 200, or any other suitable number of outcomes to perform the seed train process, using the determined seed train process parameters and respective sets of cell growth history values.

[00165] As described in this document, the first result may include one or more predicted results for each of the multiple stages of the seed train process. Consequently, in some embodiments, determining whether the first result satisfies the success criteria includes determining whether each of the one or more predicted results satisfies a respective success criterion. For example, the first result may include a predicted duration for growing cells during the first stage of the seed train process. The processor may determine whether the predicted duration satisfies a respective success criterion. For example, the processor may determine whether the predicted duration is below a threshold duration, thus satisfying a first success criterion.

[00166] In some modalities, if at least one Petition 870250082199, dated 12 / 09 / 2025, pp. 100 / 156 If the 85 / 107 threshold ratio (e.g., at least 60%, at least 70%, at least 80%, at least 90%, or 100%) of the predicted results satisfy a respective success criterion, then it is determined that the first result satisfies the success criteria of the seed train process. For example, if each of the predicted results for each stage of the seed process satisfies its respective success criterion, then it is determined that the first result satisfies the success criteria of the seed train process. In other words, implementing the seed train process using the determined parameters of the seed train process, when the crop doubling times are those included in the first set of cell growth history values, will likely result in results that satisfy the criteria of the seed train process.

[00167] In some embodiments, this is repeated for each predicted outcome, including second outcomes. For example, the processor can determine whether the second outcome satisfies the success criteria. Determining whether the second outcome satisfies the success criteria can be implemented according to the techniques described above for determining whether the first outcome satisfies the success criteria.

[00168] In Act 410-2, the processor determines a ratio between the number of predicted outcomes that meet the success criteria and a total number of predicted outcomes. For example, if the predicted outcomes include the first outcome and the second outcome, and it is determined Petition 870250082199, dated 12 / 09 / 2025, pp. 101 / 156 86 / 107, where only the first result satisfies the success criteria, then the processor determines a ratio of ½ or 50%. As another example, if it is determined that 75 out of every 100 predicted results satisfy the success criteria, then the processor determines a ratio of 75 / 100 or 75%. In some embodiments, the ratio determined in act 410-2 is the indicative probability of the likelihood that carrying out the seed train process using the seed train process parameters will satisfy the success criteria of the seed train process.

[00169] Figures 5A-1 to 5D show an exemplary user interface for specifying and viewing seed train process information. Figures 5A-1 to 5D additionally include exemplary seed train process constraints and exemplary seed train process parameters for a 5000 ml seed train process.

[00170] As shown in Figures 5A-1 to 5D, the user interface displays seed train process stage information. Seed train process stage information is indicated by its shading as shown in legend 510. In some embodiments, the seed train process information specifies the stage, pass number, and container.

[00171] As shown in Figures 5A-1, 5A-2, 5B-1, 5B-2, 5C-1, and 5C-2, the user interface displays seed train process constraints. Seed train process constraints are indicated by their shading, as shown in legends 510, 520, and 530. In some Petition 870250082199, dated 12 / 09 / 2025, page 102 / 156 In 87 / 107 modes, a user can specify values ​​for seed train process constraints by interacting with the user interface. For example, text boxes can be selectable elements of a graphical user interface. Additionally or alternatively, seed train process constraints can be automatically populated based on data and / or stored data that has been uploaded (e.g., uploaded from another device, such as another computing device, a seed train process automation module, or any other suitable method). Exemplary seed train process constraints are shown in Figures 5A-1 to 5C-2 and may also include any other seed train process constraints described herein, including those listed in Table 1.

[00172] As shown in Figure 5B-1 and Figure 5B-2, the user interface also displays seed train process parameters. The seed train process parameters are indicated by their shading, as represented in legend 520. In some embodiments, the seed train process parameters are populated based on a result of the determination of the seed train process parameters according to the techniques described in this document. For example, the seed train process parameters can be determined by performing processes 200 in Figure 2, process 300 in Figure 3A, and / or process 400 in Figure 4. Additionally or alternatively, a user can specify values ​​for seed train process parameters by interacting with Petition 870250082199, dated 12 / 09 / 2025, pp. 103 / 156 88 / 107 the user interface or obtained by any other suitable means. Exemplary seed train process parameters are shown in Figure 5A-1, Figure 5A2, Figure 5B-1, Figure 5B-2, Figure 5C-1 and Figure 5C-2 and may also include any other seed train process parameters described herein, including those listed in Table 2.

[00173] As shown in Figure 5A-1, Figure 5A-2, Figure 5B-1, Figure 5B-2, Figure 5C-1, and Figure 5C-2, seed train process parameters and seed train process constraints can be specified and / or determined for each stage in the seed train process. Some of the variables are considered seed train process constraints for some stages, but seed train process parameters for other stages. For example, the working volume per vessel is a parameter that can be varied for the first five stages, but a seed train process constraint for the remaining four stages.

[00174] Figure 5C-1 and Figure 5C-2 also show calculated seed train process parameters. In some embodiments, the calculated seed train process parameters are those that are calculated based on at least one seed train process parameter (e.g., those that are determined according to the seed train process parameter determination techniques described in this document). The calculated seed train process parameters are indicated by the shading shown in legend 530. The illustrative equations for calculating the process parameters of Petition 870250082199, dated 12 / 09 / 2025, pp. 104 / 156 The calculated 89 / 107 seed trains are listed in Table 3.

[00175] Figure 5D shows exemplary predictions of the likelihood that performing the seed train process using the seed train process parameters will satisfy the seed train process success criteria, according to some embodiments of the technology described in this document. At least three columns in Figure 5D correspond to exemplary predicted results of performing the seed train process using the seed train process parameters and the calculated seed train process parameters. For example, the predicted results include, for each stage, Normal Extra Time Required, Worst Extra Time Required, and Final VCD. In some embodiments, Normal Extra Time Required refers to the extra time required to grow cells during a particular stage of the seed train process when the culture has an average doubling time.The Worst-Qualified Extra Time Needed may refer to the extra time required to grow cells during a particular stage of the seed train process when the culture has the longest doubling time determined from historical cell growth data. The Best-Qualified Final VCD may refer to the resulting viable cell density of growing cells during the particular stage when the culture has the shortest doubling time determined from historical cell growth data. Illustrative equations for determining Normal Extra Time Needed, Worst-Qualified Extra Time Needed, and Best-Qualified Final VCD are listed in Table 4. Petition 870250082199, dated 12 / 09 / 2025, pp. 105 / 156 90 / 107

[00176] As described in this document, in some embodiments, the predicted results are evaluated to determine whether they meet the respective success criteria. In some embodiments, the user interface provides an indication of whether the exemplary results meet the respective success criteria. As indicated by legend 540, the shading of the user interface indicates when the success criteria have been met or not. For example, as shown, the Worst Predicted Extra Time Required for stage N-1 and the Best Predicted Final VCD for stage N-2 do not meet the success criteria for the seed train process.

[00177] In the example, each predicted outcome is compared to a threshold value to determine if a success criterion is met. For example, predicted values ​​for Worst Extra Time Required can be compared to a threshold of 12 hours, predicted values ​​for Normal Extra Time Required can be compared to a threshold of 0, predicted values ​​for Final VCD - Best for stages N-8 and N-2 can be compared to a threshold of 100, and predicted values ​​for Final VCD - Best for stages N-1 can be compared to N-3 can be compared to a threshold of 50.

[00178] In some modes, as the constraints and parameters are changed, the shading indicating whether the success criteria have been met updates automatically, allowing a user to easily visualize and estimate the outcome of different stages of the seed train process. Petition 870250082199, dated 12 / 09 / 2025, pp. 106 / 156 91 / 107 Seed train process parameter calculated Stage Equation Total volume after loss N-8 (Work Vol. Per Vessel — 10) * (number of Vessels) N-7 to N-1 (Work Vol. Per Vessel) * (1 — Evaporative Loss Fraction) — Sampling Loss Minimum final VCD required N-8 to N-4 Vo Working volume.Per \ \ V V Vase of the next stage] z „ , , . . . „ . „ If: :-----: *-------;— * (number of vessels of the next stage) < 3, 1 Total volume after loss K gg JV 7 ) then: (Initial VCD of the next stage) * 3, _ / Initial VCD of the next stage\ / Working volume per \ or en a°: ^ Total volume after loss J * Vessel for the next stage) * (number of vessels for the next stage) Petition 870250082199, dated 12 / 09 / 2025, pp. 107 / 156 92 / 107 N-3 N-2 If: Minimum final VCD required for the next stage (Standard culture) / Normal growth) and \Next stage time) \Next stage constant) (Working volume per pot) * (Number of pots) / V Volume of work per \(Vessel of the next stage) \ (n- of vessels of the next stage x * --------m;---------- <3, Initial VCCD of the next stage / then: (Initial VCD of the next stage) * 3, ' VI Minimum required final VCCD of the next stage or else: (Standard culture)* / Normal growth) .çKNext stage time)\ Next stage constant) (Working volume per pot) * (Number of pots) * (Working volume per pot) * (Number of pots in the next stage) If ((Average batch volume) (Total volume of next stage) \Apss loss)) *(number of vessels in the next stage) Total volume after loss) < 3 then: (Initial CVD of the next stage) * 3, Initial HDCD volume of batch average or: II _ . * * , (next stage) \of next stage) fn- of vessels of the next stage \ Total volume after loss Petition 870250082199, dated 12 / 09 / 2025, pp. 108 / 156 93 / 107 N-1 / / Working volume per \ \ „ 1 Vessel of the next stage 1 z „ , , ., . . . „ . „ If: :-----: -------;— * (number of vessels in the next stage) < 3, 1 Total volume after loss K gg JV 7 ) then: (Initial VCD of the next stage) * 3, V / Initial VCD of \ tã 1 next stage j / Working volume per \ : 1 Total volume after loss 1 \Vessel of the next stage) / * (number of vessels in the next stage) / Predicted final VCD N-8 to N-1 (Initial VCD) * eí.(Normal Constant Growth~)*(Culture Time Standard)) Division ratio N-8 to N-1 í{ Minimum Final \ - ( Initial VCD of \\ \VVCD Required / \Next Stage)] Initial VCD of Next Stage Time required normal N-8 to N-1 / / Minimum Final \J Total Vol. A , 5) \ ,. | VvCD Required) * DJAfter Loss) + 1 1 (Work Vol. Per Vessel) * (number of Vessels) * (Initial VCD) 1 Normal Constant Growth Table 3. Equations for determining the calculated seed train parameters. Expected Result Equation Extra time needed - normal Time needed Normal — Standard Culture Time Petition 870250082199, dated 12 / 09 / 2025, pp. 109 / 156 94 / 107 Extra time required - worst / / Minimum final VV Total Vol. \ _ \ ,. _______WCD Required / * (\After Loss) + )_______ 1 1 (Work Vol. Per Vessel) * (Number of Vessels) * (Required VCD) 1 Worst Constant Growth — Culture Standard Time Final VCD - Best g(Worst Constant Growth*Best Constant Growth) * Required VCD Table 4. Equations for estimating the results of one or more stages of a seed train process.

[00179] Figure 6 shows exemplary historical cell growth data according to some embodiments of the technology described in this document. The historical cell growth data includes data from multiple seed train processes that have already been implemented. The data includes, for each past seed train process, the doubling time of cultured cells during a particular stage in the seed train process.

[00180] As described in this document, in some embodiments, the doubling times for a particular stage of a seed train process can be randomly sampled from a distribution of doubling times, such as that shown in Figure 6. For example, doubling times can be sampled by determining an indicative probability of the likelihood that performing the seed train process using determined seed train process parameters will satisfy the seed train process success criteria as described in this document, including at least with respect to Figure 2.

[00181] Figure 7A shows exemplary doubling time data measured from crops that were Petition 870250082199, dated 12 / 09 / 2025, page 110 / 156 95 / 107 cultures were cultivated to produce different molecules including monoclonal antibodies (mAbs), bispecific T-cell couplers (BiTEs), and bispecific antibodies. Doubling time data include an average doubling time and standard deviation for each multi-stage seed train process for each culture type (e.g., mAb, BiTE, bispecific). Figure 7B shows exemplary doubling time data measured from cultures that were expanded into 500 l containers and cultures that were grown into 2000 l containers. Doubling time data include an average doubling time and standard deviation for each multi-stage seed train process for each container size (e.g., 500 l, 2000 l).

[00182] In some embodiments, doubling time data can be stored and / or used as historical cell growth data. For example, as described in this document, doubling time data can be sampled for each stage and used to predict the probability of satisfying at least one success criterion if the crop had the sampled doubling time. Additionally or alternatively, doubling time data can be used to specify seed train process constraints. For example, the average doubling time for a particular stage can be specified as the normal doubling time constraint for that stage in the seed train process.

[00183] Figure 8A and Figure 8B show a higher overall success rate for carrying out a train process. Petition 870250082199, dated 12 / 09 / 2025, page 111 / 156 96 / 107 seeds, according to the technology embodiments described in this document, compared to performing a manually designed seed train process. Consequently, using the techniques described in this document to design a seed train process will help reduce facility scheduling problems caused by seed train processes that take too long or need to be repeated. Furthermore, it will help limit waste caused by failed seed train processes that need to be discarded and repeated.

[00184] Figure 8A is a graph comparing the overall success rate of seed train processes that were manually designed to those that were designed according to the techniques described in this document. In particular, the seed train processes were designed for growing crops for the production of three different molecules (e.g., molecule 1, molecule 2, and molecule 3). As shown, for each of the molecules, the seed train processes that were designed according to the techniques described in this document had a higher overall success rate than those that were manually designed.

[00185] Figure 8B is a graph comparing the overall implementation success rate of each stage of seed train processes that were manually designed to those that were designed according to the techniques described in this document. As shown, at least seven out of eight of the stages of the seed train processes designed according to the techniques described in this document had an equal or greater overall success rate. Petition 870250082199, dated 12 / 09 / 2025, page 112 / 156 97 / 107 of those that were designed manually.

[00186] Figure 8C shows that carrying out a 500 l seed train process, according to the technology modalities described in this document, results in an average success rate greater than 80% for each stage of the seed train process. As shown, the successful implementation of a stage means that there were sufficient cells and no overgrowth.

[00187] Figure 8D shows that carrying out a 2000 l seed train process, according to the technology embodiments described in this document, results in a success rate greater than 70% for each stage of the seed train process. As shown, successful implementation of a stage means that there were sufficient cells and no overgrowth.

[00188] An illustrative implementation of a computer system 900 that can be used in connection with any of the embodiments of the technology described herein (for example, such as the processes in Figures 2 to 3 and 4) is shown in Figure 9. The computer system 900 includes one or more processors 910 and one or more articles of manufacture comprising computer-readable non-transient storage media (for example, memory 920 and one or more non-volatile storage media 930). The processor 910 can control data writing and data reading from memory 920 and the non-volatile storage device 930 in any suitable manner, as aspects of the technology described herein are not limited to any particular techniques for writing or reading data. To carry out any of the Petition 870250082199, dated 12 / 09 / 2025, page 113 / 156 98 / 107 functionalities described in this document, the 910 processor can execute one or more processor-executable instructions stored in one or more computer-readable non-transient storage media (for example, memory 920), which can serve as computer-readable non-transient storage media that store processor-executable instructions for execution by the 910 processor.

[00189] The computing device 900 may also include a network input / output (I / O) interface 940 through which the computing device can communicate with other computing devices (e.g., via a network), and may also include one or more user I / O interfaces 950 through which the computing device can provide output and receive input from a user. User I / O interfaces may include devices such as a keyboard, a mouse, a microphone, a display device (e.g., a monitor or touch screen), speakers, a camera, and / or various other types of I / O devices.

[00190] The modalities described above can be implemented in any of several ways. For example, the modalities can be implemented using hardware, software, or a combination thereof. When implemented in software, the software code can run on any suitable processor (e.g., a microprocessor) or collection of processors, whether provided on a single computing device or distributed across multiple computing devices. It should be understood that any component or collection of components that perform Petition 870250082199, dated 12 / 09 / 2025, pp. 114 / 156 99 / 107 The functions described above can be generically considered as one or more controllers that control the functions described above. The one or more controllers can be implemented in various ways, such as with dedicated hardware, or with general-purpose hardware (e.g., one or more processors) that is programmed using microcode or software to perform the functions mentioned above.

[00191] In this sense, it should be understood that an implementation of the embodiments described in this document comprises at least one computer-readable storage medium (for example, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile discs (DVDs) or other optical disc storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or other tangible and non-transient computer-readable storage medium) encoded with a computer program (i.e., a plurality of executable instructions) that, when executed on one or more processors, performs the functions described above in one or more embodiments. The computer-readable medium may be portable so that the program stored therein can be loaded onto any computing device to implement aspects of the techniques described in this document.Furthermore, it should be understood that the reference to a computer program that, when executed, performs any of the functions described above, is not limited to an application program running on a host computer. Instead, the terms computer program and software are used in this document in one sense. Petition 870250082199, dated 12 / 09 / 2025, pp. 115 / 156 100 / 107 generic to refer to any type of computer code (e.g., application software, firmware, microcode, or any other form of computer instruction) that can be used to program one or more processors to implement aspects of the techniques described in this document.

[00192] The aforementioned description of implementations provides illustration and description, but is not intended to be exhaustive or to limit the implementations to the precise form revealed. Modifications and variations are possible in light of the above teachings or may be acquired from practice of the implementations. In other implementations, the methods represented in these figures may include fewer operations, different operations, operations ordered differently, and / or additional operations. Furthermore, non-dependent blocks may be performed in parallel.

[00193] It will be evident that exemplary aspects, as described above, can be implemented in many different forms of software, firmware, and hardware in the implementations illustrated in the figures. Furthermore, certain portions of the implementations can be implemented as a module that performs one or more functions. This module may include hardware, such as a processor, an application-specific integrated circuit (ASIC), or a field-programmable gate array (FPGA), or a combination of hardware and software.

[00194] Having thus described various aspects and modalities of the technology presented in the disclosure, it should be understood that various alterations, modifications and improvements will readily occur to those versed in the technique. Such alterations, Petition 870250082199, dated 12 / 09 / 2025, pp. 116 / 156 101 / 107 Modifications and improvements must be within the spirit and scope of the technology described herein. For example, those of ordinary skill in the art will readily visualize a variety of other means and / or structures for performing the function and / or obtaining the results and / or one or more of the advantages described herein, and each of these variations and / or modifications is considered to be within the scope of the embodiments described herein. Those skilled in the art will recognize, or be able to ascertain, using no more than routine experimentation, many equivalents to the specific embodiments described herein. It is therefore to be understood that the foregoing embodiments are presented only by way of example and that, within the scope of the appended and equivalent claims, inventive embodiments may be practiced in a manner other than as specifically described.Additionally, any combination of two or more resources, systems, articles, materials, kits, and / or methods described herein, provided that such resources, systems, articles, materials, kits, and / or methods are not mutually inconsistent, is included within the scope of this disclosure.

[00195] The embodiments described above can be implemented in any of several ways. One or more aspects and embodiments of the present disclosure involving the performance of processes or methods may utilize program instructions executable by a device (e.g., a computer, a processor, or other device) to perform or control the performance of the processes or methods. In this regard, various inventive concepts may Petition 870250082199, dated 12 / 09 / 2025, pp. 117 / 156 102 / 107 being incorporated as a computer-readable storage medium (or multiple computer-readable storage media) (for example, a computer memory, one or more floppy disks, compact discs, optical discs, magnetic tapes, flash memories, field-programmable gate array circuit configurations or other semiconductor devices, or other tangible computer storage medium) encoded with one or more programs that, when executed on one or more computers or other processors, perform methods that implement one or more of the various embodiments described above. The computer-readable media may be transportable, so that the program or programs stored therein may be loaded onto one or more different computers or other processors to implement various aspects described above. In some embodiments, computer-readable media may be non-transient media.

[00196] The terms program or software are used in this document in a generic sense to refer to any type of computer code or set of computer-executable instructions that can be employed to program a computer or other processor to implement various aspects as described above. Additionally, it should be understood that, according to an aspect, one or more computer programs that, when executed, accomplish methods of this disclosure, need not reside on a single computer or processor, but may be distributed in a modular manner among several different computers or processors to implement various aspects of this disclosure. Petition 870250082199, dated 12 / 09 / 2025, pp. 118 / 156 103 / 107

[00197] Computer executable instructions can take many forms, such as program modules, executed by one or more computers or other devices. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. Typically, the functionality of program modules can be combined or distributed as desired in various modalities.

[00198] Furthermore, data structures can be stored on computer-readable media in any suitable form. To simplify the illustration, data structures can be shown to have fields that are related through location within the data structure. Such relationships can equally be achieved by assigning storage to the fields with locations on a computer-readable medium that conveys the relationship between the fields. However, any suitable mechanism can be used to establish a relationship between information in fields of a data structure, including through the use of pointers, tags, or other mechanisms that establish relationships between data elements.

[00199] When implemented in software, software code can be executed on any suitable processor or collection of processors, whether provided on a single computer or distributed across multiple computers.

[00200] In addition, a computer may have one or more input and output devices. These devices may be used, among other things, to present a user interface. Examples of output devices that Petition 870250082199, dated 12 / 09 / 2025, page 119 / 156 104 / 107 devices that can be used to provide a user interface include printers or display screens for visual output presentation and speakers or other sound-generating devices for audible output presentation. Examples of input devices that can be used for a user interface include keyboards and pointing devices such as mice, touch-sensitive elements, and digitizing tablets. As another example, a computer can receive input information through voice recognition or in other audible formats.

[00201] Such computers may be interconnected by one or more networks in any suitable manner, including a local area network or a wide area network, such as a corporate network and intelligent network (IN) or the Internet. Such networks may be based on any suitable technology and may operate according to any suitable protocol and may include wireless networks, wired networks or fiber optic networks.

[00202] Furthermore, as described, some aspects may be incorporated as one or more methods. The acts performed as part of the method may be ordered in any suitable manner. Consequently, embodiments may be constructed in which the acts are performed in a different order than illustrated, which may include performing some acts simultaneously, although shown as sequential acts in illustrative embodiments.

[00203] All definitions, as defined and used in this document, should be understood as control over dictionary definitions, definitions in documents incorporated by reference and / or common meanings of the defined terms. Petition 870250082199, dated 12 / 09 / 2025, pp. 120 / 156 105 / 107

[00204] The indefinite articles "a" and "an," as used in this document in the descriptive report and claims, unless clearly indicated otherwise, shall be understood as meaning "at least one."

[00205] The phrase "and / or," as used in this document in the descriptive report and claims, should be understood as meaning one or both of the elements thus joined together, that is, elements that are conjuncturally present in some cases and disjuncturally present in other cases. Multiple elements listed with "and / or" should be interpreted in the same way, that is, one or more of the elements thus joined together. Other elements may optionally be present in addition to the elements specifically identified by the "and / or" clause, whether or not related to those specifically identified elements.Thus, as a non-limiting example, a reference to A and / or B, when used in conjunction with open language, such as "that which includes," may refer, in one modality, to only A (optionally including elements other than B); in another modality, to only B (optionally including elements other than A); in yet another modality, to A and B (optionally including other elements); etc.

[00206] As used in this document in the descriptive report and claims, the phrase "at least one," in reference to a list of one or more elements, should be understood as meaning at least one element selected from any one or more of the elements in the list of elements, but not necessarily including at least one of each and every element specifically listed in the list of Petition 870250082199, dated 12 / 09 / 2025, pp. 121 / 156 106 / 107 elements and not excluding any combinations of elements in the list of elements. This definition also allows elements to optionally be present in addition to the elements specifically identified within the list of elements to which the phrase "at least one" refers, whether or not it is related to those specifically identified elements.Thus, as a non-limiting example, at least one of A and B (or, equivalently, at least one of A or B, or, equivalently, at least one of A and / or B) may refer, in one embodiment, to at least one, optionally including more than one, A, without B present (and optionally including elements other than B); in another embodiment, to at least one, optionally including more than one, B, without any A present (and optionally including elements other than A); in yet another embodiment, to at least one, optionally including more than one, A, and at least one, optionally including more than one, B (and optionally including other elements); etc.

[00207] In the claims, as well as in the descriptive report above, all transitional phrases, such as comprising, including, bearing, having, containing, involving, holding, composed of and the like, should be understood as being open, that is, to mean including, but without limitation. Only transitional sentences consisting of and consisting essentially of should be closed or semi-closed transitional sentences, respectively.

[00208] The terms “approximately”, “substantially” and “about” may be used to mean within ±20% of a target value, in some embodiments, within ±10% of Petition 870250082199, dated 12 / 09 / 2025, pp. 122 / 156 107 / 107 a target value, in some modalities, within ±5 target value, in some modalities, within ±2 % target value, in some modalities. The approximately, substantially and about include the target value. % of one of one terms can Petition 870250082199, dated 12 / 09 / 2025, pp. 123 / 156

Claims

1 / 14 CLAIMS 1. A method for determining seed train process parameters for a seed train process for growing a cell culture, wherein the method is carried out using at least one software application program comprising a common interface module, a parameter optimization module and a simulation module, wherein the method is characterized in that it comprises: using at least one computer hardware processor to: obtain, using the common interface module, a specification of seed train process constraints for multiple stages of the seed train process;Determine the seed train process parameters using the parameter optimization module and the seed train process constraints, wherein the seed train process parameters comprise a respective set of parameters for each particular stage of the multiple stages of the seed train process, wherein the determination comprises: determining, for each particular stage of the multiple stages and using the parameter optimization module and the seed train process constraints, the respective set of parameters for cultivating the crop during the particular stage;To determine, using the simulation module, historical cell growth data and the determined parameters of the seed train process, an indicative probability of the possibility that carrying out the seed train process using the determined parameters of the seed train process will satisfy the success criteria of the seed train process; and to issue the respective set of determined seed train process parameters for each stage of the multiple stages of the seed train process and the indicative probability of the possibility that carrying out the seed train process using the determined seed train process parameters will satisfy the success criteria of the seed train process.

2. A method according to claim 1, characterized in that determining, for each particular stage of the multiple stages, the respective set of parameters for cultivating the crop during the particular stage comprises: determining at least one seed train process parameter selected from the group consisting of: a viable cell density, an indication of the inclusion of the particular stage in the seed train process, a targeted duration of the seed train process, a batch medium volume, a working volume per pot, and a number of pots.

3. Method, according to any one of claims 1 or 2, characterized in that obtaining the specification of seed train process constraints comprises: Petition 870250082199, dated 12 / 09 / 2025, page 125 / 156 3 / 14 obtaining a specification of at least one seed train process constraint selected from the group consisting of: a target viable cell density range, a target working volume range, a split ratio crop growth process constraint and a batch medium volume range.

4. A method according to claim 1, characterized in that the historical cell growth data comprise indicative data of a doubling time associated with the growth of one or more cell cultures.

5. A method, according to any one of claims 1, 2, 3 or 4, characterized in that determining the seed train process parameters using the parameter optimization module comprises determining the seed train process parameters using a genetic algorithm.

6. A method according to any one of claims 1, 2, 3, 4 or 5, characterized in that determining the indicative probability that carrying out the seed train process using the determined seed train process parameters will satisfy the seed train process success criteria comprises carrying out Monte Carlo simulations using historical cell growth data.

7. A method according to any one of claims 1, 2, 3, 4, 5 or 6, characterized in that the success criteria of the seed train process comprise, for each stage of the multiple stages of the seed train process: a first criterion that an expected duration required to cultivate the crop during the particular stage does not exceed a limit duration for cultivating the crop during the particular stage; and a second criterion that an expected viable cell density resulting from the growth of the crop during the particular stage does not exceed a limit viable cell density resulting from the growth of the crop during the particular stage.

8. A method according to claim 7, characterized in that determining the respective set of parameters for each particular stage of the multiple stages comprises: determining, using the respective set of parameters, the expected duration required to cultivate the cell culture during the particular stage; and determining whether the expected duration exceeds the respective time limit for the particular stage.

9. A method according to claim 7, characterized in that determining the respective set of parameters for each particular stage of the multiple stages comprises: determining, using the respective set of parameters, the expected viable cell density resulting from the growth of the cell culture during the particular stage; and determining whether the expected viable cell density exceeds the respective limiting viable cell density for the particular stage.

10. Method, according to any one of claims 1, 2, 3, 4, 5, 6, 7, 8 or 9, characterized in that Petition 870250082199, dated 12 / 09 / 2025, page 127 / 156 5 / 14, issuing the set of seed train process parameters for each stage of the multiple stages of the seed train process and the indicative probability of whether the realization of the seed train process will satisfy the success criteria of the seed train process comprises: generating a graphical user interface (GUI) using the common interface module; and displaying the set of seed train process parameters and / or the probability through the generated GUI.

11. Method, according to claim 10, characterized in that the display of the seed train process parameter set through the common interface module comprises: displaying a visual indication of whether a seed train process parameter of the seed train process parameter set violates a seed train process constraint of the seed train process constraints.

12. Method, according to any one of claims 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 or 11, characterized in that determining the seed train process parameters comprises: determining the seed train process parameters based on a set of estimated crop doubling times included in the seed train process constraints, wherein the set of estimated crop doubling times includes an estimated crop doubling time for each of the multiple stages of the seed train process; Petition 870250082199, dated 12 / 09 / 2025, p.128 / 156 6 / 14 and determine whether performing the seed train process using the determined seed train process parameters will satisfy the success criteria of the seed train process, and the method further comprises: displaying, through a graphical user interface (GUI) generated by the common interface module, a visual indication of a result of the determination of whether performing the seed train process using the seed train process parameters will satisfy the success criteria of the seed train process.

13. A method according to any one of claims 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 or 12, characterized in that it further comprises: comparing the determined probability to a probability threshold, wherein outputting the set of seed train process parameters for each stage of the multiple stages of the seed train process comprises outputting the determined seed train process parameters upon determining that the probability exceeds the probability threshold.

14. A method according to any one of claims 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12 or 13, characterized in that issuing the set of seed train process parameters for each stage of the multiple stages of the seed train process comprises: issuing, when the indicative probability of the possibility that carrying out the seed train process using the determined seed train process parameters will satisfy the success criteria of the seed train process satisfies a probability threshold, a recommendation to carry out the seed train process using the set of seed train process parameters for each stage of the multiple stages of the seed train process.

15. A method according to any one of claims 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13 or 14, characterized in that at least one software application program further comprises a seed train process automation module and outputs the set of seed train process parameters for each stage of the multiple stages of the seed train process and the indicative probability of whether the realization of the seed train process will satisfy the success criteria of the seed train process, comprising: transmitting the set of seed train process parameters and / or the probability to the seed train process automation module;and use the seed train process automation module to make a seed train process automation system perform a particular stage of the multiple stages of the seed train process according to the respective set of seed train process parameters.

16. Method, according to any one of claims 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14 or 15, characterized in that it further comprises: obtaining, using the common interface module, input indicative of a result of the growth of the crop cells during a first stage of the multiple stages of the seed train process; determining, using the parameter optimization module, the seed train process input and constraints, updated seed train process parameters for subsequent stages of the seed train process; and outputting the updated seed train process parameters.

17. A method according to any one of claims 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 or 16, characterized in that it further comprises: obtaining, using the common interface module, a specification of the second seed train process constraints for multiple stages of a second seed train process; determining, using the parameter optimization module and the second seed train process constraints, the second seed train process parameters for the second seed train process; determining, using the simulation module, the historical cell growth data and the second seed train process parameters, a second probability indicative of the possibility that carrying out the second seed train process, using the second seed train process parameters, will satisfy the second seed train process criteria;and to issue the second seed train process parameters and the second indicative probability of the possibility that carrying out the second seed train process, using the second seed train process parameters, will satisfy the second seed train process criteria.

18. A method according to any one of claims 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16 or 17, characterized in that determining the seed train process parameters using the parameter optimization module comprises: determining, using an objective function, a first score for the first candidate seed train process parameters; determining, using the objective function, a second score for the candidate seed train process parameters; comparing the first score and the second score; and selecting, based on a result of the comparison, the seed train process parameters from among the first candidate seed train process parameters and the second candidate seed train process parameters.

19. Method, according to any one of claims 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17 or 18, characterized in that the seed train process parameters comprise a first set of seed train process parameters for a first stage of the multiple stages of the seed train process, and determining the indicative probability of the possibility that the realization of the seed train process using Petition 870250082199, dated 12 / 09 / 2025, p.132 / 156 10 / 14 The seed train process parameters will satisfy the success criteria of the seed train process, comprising: determining, for the first stage of the seed train process, a first indicative probability of cultivating the cell culture during the first stage, using the first set of seed train parameters for the first stage, will satisfy the first criteria of the success criteria of the seed train process.

20. A method according to any one of claims 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18 or 19, characterized in that determining the indicative probability that carrying out the seed train process using the seed train process parameters will satisfy the seed train process success criteria comprises: simulating a first set of cell growth values ​​using historical cell growth data, wherein the first set of cell growth values ​​includes a first historical cell growth value for each of the multiple stages of the seed train process;Predict, using the seed train process parameters and the first set of cell growth values, a first outcome of performing the seed train process, wherein the first outcome is indicative that performing the seed train process using the seed train process parameters determined in Petition 870250082199, dated 12 / 09 / 2025, p. 133 / 156 11 / 14 will satisfy the success criteria of the seed train process; simulate a second set of cell growth values ​​using historical cell growth data, wherein the second set of cell growth values ​​includes a second historical cell growth value for each of the multiple stages of the seed train process;Predict, using the seed train process parameters and the second set of cell growth values, a second outcome of performing the seed train process, wherein the second outcome is indicative that performing the seed train process using the seed train process parameters will satisfy the seed train process success criteria; and determine the probability based on the first and second predicted outcomes.

21. A method according to claim 20, characterized in that determining the probability based on the first and second predicted outcomes comprises: determining a series of predicted outcomes that indicate that carrying out the seed train process using the seed train process parameters and the respective historical cell growth data will satisfy the success criteria of the seed train process; and determining a ratio between the number of predicted outcomes and a total number of predicted outcomes.

22. Method according to claim 20, characterized in that the success criteria of the seed train process comprise, for each stage of the multiple stages of the seed train process: a first criterion that an expected duration required to cultivate the culture during the particular stage does not exceed a limit duration for cultivating the culture during the particular stage, wherein predicting the first outcome comprises, for each particular stage of the multiple stages: determining, using the respective set of seed train process parameters for the particular stage and a cell growth history value from the first set of cell growth history values, a first expected duration required to cultivate the cell culture during the particular stage of the seed train process;and compare the first expected duration with the limit duration, and whereby predicting the second outcome comprises, for each particular stage of the multiple stages: determining, using the respective set of seed train process parameters for the particular stage and a cell growth history value from the second set of cell growth history values, a second expected duration required to grow the cell culture during the particular stage of the seed train process; and comparing the second expected duration with the limit duration.

23. Method according to claim 20, characterized in that the success criteria of the seed train process comprise, for each stage of the multiple stages of the seed train process: a second criterion that an expected viable cell density resulting from the growth of the culture during the particular stage does not exceed a limiting viable cell density resulting from the growth of the culture during the particular stage, wherein predicting the second outcome comprises, for each particular stage of the multiple stages: determining, using the respective set of seed train process parameters for the particular stage and a cell growth history value from the first set of cell growth history values, a first expected viable cell density resulting from the growth of the cell culture during the particular stage of the seed train process;and compare the first expected viable cell density with the threshold viable cell density, and whereby predicting the second outcome comprises, for each particular stage of the multiple stages: determining, using the respective set of seed train process parameters for the particular stage and a cell growth history value from the second set of cell growth history values, a second expected viable cell density resulting from cell culture growth during the particular stage of the seed train process; and comparing the second expected viable cell density with the threshold viable cell density.

24. A system characterized in that it comprises: at least one computer hardware processor; and at least one computer-readable non-transient storage medium that stores processor-executable instructions which, when executed by the at least one computer hardware processor, cause the at least one computer hardware processor to perform the method as defined in any one of claims 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22 or 23.

25. At least one non-transient, computer-readable storage medium characterized in that it stores processor-executable instructions that, when executed by at least one computer hardware processor, cause that at least one computer hardware processor to perform the method as defined in any one of claims 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, or 23. Petition 870250082199, dated September 12, 2025, pp. 137-156

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