Automating asynchronous branched passaging

Abstract

The present disclosure provides methods and systems for automating the artificial selection of microorganisms.

Description

AUTOMATING ASYNCHRONOUS BRANCHED PASSAGING1. CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of US provisional application no. 63 / 779,531, filed March 28, 2025, which is hereby incorporated in its entirety by reference.2. BACKGROUND OF THE INVENTION

[0002] The ecological functions of modem living agents have evolved over billions of years as the earliest life forms replicated, mutated, and replicated again with sustained selective pressure yielding the diverse biochemical and ecological activities of organisms alive today. According to the modem synthesis, modem biologists define evolution strictly as the change in frequency of alleles in a population over time. In a useful mathematical abstraction of evolutionary processes, Lewontin defined axioms of evolution allowing the empirical process of evolution to connect clearly to mathematical formalizations. Lewontin’ s axioms of evolution were: (1) heredity or replication, (2) variation, and (3) differential fitness. From one generation to the next, the presence of Lewontin’ s axioms in a population will lead to evolution or change in the frequencies of alleles in a population due to their hereditary nature, variation, and differential fitness.

[0003] However, the formal definitions and axioms of evolution apply only to single generations and a pre-existing set of alleles. This short-term process of evolution defined as changes in preexisting allele sets is akin to genetic screening that changes the frequencies of pre-existing alleles but doesn’t connect to the gradual innovation of new alleles, sustained evolution across multiple generations, and the formation of complex traits that require an accumulation of mutations over many generations while organisms are under sustained selective pressures to improve complex traits.3. SUMMARY OF THE INVENTION

[0004] The present disclosure provides systems and methods for the automated asynchronous branched propagation of microorganisms. In particular, the present disclosure provides systems and methods of automating the evolution of microorganisms with desirable properties in a manner that is quicker, less manually intensive, more efficient, and more effective than existingmethods and systems through both the asynchronous propagation and the tunable degrees of branched propagation. The system and methods accomplish this by manipulating and / or cultivating and comparing isolated subpopulations of microorganisms which are continuously monitored for a desired activity, converting measures of the desired activity to propagation event logic triggering asynchronous serial and / or branched passaging of subpopulations, and inputting heritability estimates using data from prior subpopulations and desired activity data of currently active subpopulations to devise automated propagation plans. Cultivating and inoculating are used interchangeably throughout the specification.

[0005] In one aspect, automated systems for preparing and / or cultivating microbiological mixtures are provided. The system having:i. a housing;ii. a vessel set containing a plurality of vessels arranged in a format within the housing and configured to hold the microbiological mixtures;iii. at least one resource dispenser configured for delivering reagents and / or mixtures;iv. a fluid handler configured to be adjacent to the vessels to transfer reagents and / or mixtures into and out of the vessels;v. at least one sensor module configured to be adjacent to the vessels to acquire indicator measurements from the microbiological mixtures in the vessels;vi. at least one manipulator module moveably coupled to and configured to move one or more of the vessel set, the resource dispenser, the fluid handler, and the sensor module;vii. a processor in communication with the resource dispenser, the fluid handler, the sensor module and the manipulator module, the processor instructing the sensor to iteratively collect measurements against which propagation event logic is applied; andviii. a computer-readable storage medium storing instructions that, when executed by the processor, cause the processor to execute a propagation plan for automated artificial selection and to control operation of the resource dispenser, the fluid handler, the sensor module and the manipulator module based on sensor feedback and the propagation plan to inoculate vessels within the vessel set with the microbiological mixtures and log inoculations over time.

[0006] In another aspect, methods are provided for automatically preparing and / or cultivating a microbiological mixture. The method comprising:i. generating a propagation plan for the microbiological mixture in an automated system; andii. executing the propagation plan by:dispensing from a fluid handler the microbiological mixture into vessels in a vessel set to inoculate the vessels to initiate automated artificial selection;applying a sensor module to take indicator measurements of the vessels representing a condition within the vessels;recursively logging the indicator measurements and times for the vessels until a propagation event is triggered or the method is terminated; andadjusting one or more subsequent manipulations of components of the automated system in accordance with the indicator measurements received from the sensor module.

[0007] In some embodiments, the passaging of the subpopulations is automated, allowing sustained selection and propagation determined by longer-term evolutionary forces. The automation of passaging can be based on indicator thresholds such as optical density or estimated agent growth rates during exponential phase which may allow users to reduce the time between agent inoculation and later propagation across iterations and thereby accelerate evolution by propagating new iterations in less time as organisms evolve to grow faster and their ecological activity is enhanced over many generation. Smaller population sizes, shorter timescales between iterations, and less-selective propagation plans can allow a tunable degree of drift or selection. The number of active vessels turned over at a given propagation event can range from a singlevessel used to serially passage its inhabitant agents, allowing genetic drift and heritability estimation without loss of diversity, to the entire set of vessels turned over in selective sweeps. Automating asynchronous branched propagations over long periods of time enables users to consider longer-term evolutionary trends, such as the rate of change of desired traits over time, to inform the nature and strength of selection, the propagation plans, or the termination of passaging once trait evolution reaches a plateau.4. BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

[0008] These and other features, aspects, and advantages of the present invention will become better understood with regard to the following description, and accompanying drawings, where:

[0009] FIG. 1 depicts a schematic drawing of an illustrative embodiment of a system for performing automated branch passaging according to aspects of the present disclosure.

[0010] FIG. 2 depicts an exemplary implementation for performing automated branch passaging in accordance with the illustrative embodiment.

[0011] FIG. 3 depicts a state diagram of a vessel and related transition events in accordance with the illustrative embodiment.

[0012] FIG. 4 depicts an example of a representative fitness progression of three vessels, as measured with a sensor such as optical density, during a period of cultivating in accordance with the present disclosure.

[0013] FIG. 5 depicts an example of a representative model fitting for the predicted fitness of vessels, as measured by a sensor such as optical density, in accordance with the illustrative embodiment.

[0014] FIG. 6 depicts a graphical user interface for monitoring and controlling a system for performing automated branch passaging in accordance with the present disclosure.

[0015] FIG. 7 depicts an example fitness legend (left) for visualizing relative vessel fitness and an example propagation plan (right) based on ranked fitness in accordance with the present disclosure.

[0016] FIG. 8 depicts an example propagation plan between Parent and Offspring wherein the breeding plan for the Offspring incorporates a blending of mixtures from at least two vessels of the Parent vessel set, in accordance with the present disclosure.

[0017] FIG. 9 depicts an example incubation wherein a vessel subset is utilized to expose an agent to several different environments simultaneously, in accordance with the present disclosure.

[0018] FIG. 10 depicts an illustrative embodiment of a breeding cabinet for performing automated branch passaging, in accordance with aspects of the present disclosure.

[0019] FIG. 11 depicts an illustrative embodiment of several components of a system for performing automated branch passaging, in accordance with aspects of the present disclosure.

[0020] FIG. 12 depicts an isometric illustrative embodiment of the internal assembly of a system for performing automated branch passaging, in accordance with aspects of the present disclosure.

[0021] FIG. 13 depicts an illustrative embodiment of the internal assembly of a system for performing automated branch passaging, in accordance with aspects of the present disclosure.

[0022] FIG. 14 depicts an illustrative embodiment of the rotational platform assembly with square cuvettes as part of a system for performing automated branch passaging, in accordance with aspects of the present disclosure.

[0023] FIG. 15 depicts the results of a simulation of an asynchronous branched propagation process that results in trait evolution.

[0024] FIG. 16 depicts a representative phylogenetic tree tracking the process of five vessels undergoing asynchronous branched passaging with the claimed system and methods, indicating serial passage events in red circles, as well as branched-passage events with variable branch multiplicity.

[0025] FIG. 17 depicts experimental data acquired with the system performing asynchronous branched passaging with 40 parallel vessels containing yeast over 73 hours.

[0026] FIG. 18 depicts a subset of the experimental data depicted in FIG 17.5. DETAILED DESCRIPTION OF THE INVENTION

[0027] Across many generations, organisms that replicate imperfectly (with non-zero mutation rate) will generate many new alleles. Transient selective pressures - non-hereditary differential fitness in the context of Lewontin’s axioms - may be forgotten as organisms continually mutate away from common ancestors with a desired trait and changes in the frequencies of alleles from one generation are counterbalanced with randomly changing, and at times opposing selective pressures causing opposing changes in frequencies of alleles across generations. Sustained selection across many generations can generate robust biological agents with enhanced, complex, heritable traits capable of complex ecological activities. In addition to phenotypic traits of individual agents, the ecological activities determined by interactions between agents can be under selection with processes such as kin-selection allowing the evolution of cooperative or social behaviors that modify the ecological activities of groups of organisms sharing the same trait. Examples of cooperative behaviors that may be under kin selection include quorum-sensing in cells, cooperation between algae and bacterial cells in lichens, and social dynamics such as hunting coordination among pack animals. Sustained selection across generations can modify the traits in populations of organisms and the ecological activities of organisms determining their ability to acquire resources and reproduce under environmental conditions that persist for multiple generations.

[0028] Artificial selection has been used for thousands of years to change useful complex traits of organisms. Artificial selection exploits the general principles of evolution and sustained natural selection to create novel organisms. Artificial selection uses sustained selective pressures designed by breeders, such as sustained selection for larger kernels of com, to permit changing complex traits and improving ecological activities of beneficial organisms. Artificial selection has enabled the breeding of small grasses into giant stalks of com, wild bovines into docile domestic cows, and wolves into many variations of domestic dogs with useful behavioral and physiological traits, such as Dachshunds bred to chase small mammals in burrows, border collies bred to herd sheep, rottweilers bred for defense, labradors retrieving fowl during a hunt, huskies capable of pulling sleds long distances in cold temperatures, and more, all without needing to know the specific genetic determinants of the traits of interest.

[0029] Artificial selection of macroscopic, multicellular organisms has an extensive history, but the artificial selection of microorganisms is more difficult due to the difficulty of measuring microbial phenotypes, propagating microbes with the corresponding genotypes, and sustaining selective pressures over timescales long enough to allow the evolution of complex traits. The in vitro evolution of microbes over many generations has been monitored and microbes have been under sustained selection in some cases, such as the selection of yeast or bacteria to make desirable fermentation products in beer, mead, wine, cheese, and other products. However, artificial selection of microbes is often manual and there is a need to automate artificial selection over long timescales with an ability to incorporate information about heritability and easily tune evolutionary forces such as the various forms of selection (directional, diversifying, purifying, etc.) and intensities of selection from genetic drift to strong selection.

[0030] Serial passaging has been used to artificially select microorganisms by passing the microorganisms through a series of new environments to select for adaptations to new environments or, for serial passage of phage, adaptation to new hosts. However, serial passaging has transmitted microorganisms along a series of populations without utilizing relative fitness values or trait heritability across populations to inform higher-order evolutionary dynamics -such as tuning drift to allow diversification or sustaining purifying selection to select for the stability of traits - and serial passage protocols have often involved tedious manual transmission from one generation to the next.

[0031] Laboratory methods for screening and selecting one generation of microorganisms at scale exist, but such methods are limited to focus on evaluating individual genetic elements and their biochemical activities using techniques such as phage display or microfluidic approaches to assay desired activity of the products of genetic elements. Laboratory methods for screening and selecting one generation of genetic elements displayed on phage or in microcapsules require manual interventions between generations to sustain selection across many generations, such as generating novel genetic libraries between steps of sorting and evaluating the activity of individual genetic elements. These manual interventions are slow, tedious, and have ultimately limited the scale and throughput of procedures implementing artificial selection ofmicroorganisms.

[0032] Over the past half century, modem molecular biology has developed methods for intentionally modifying the genetics of organisms and screening large numbers of genetic elements to find those with desired biochemical activities. In parallel, and at larger scales of space, biological complexity, and time, the fields of ecology and evolutionary biology have developed mathematical understandings of evolution and the longer-term evolutionaiy dynamics of populations of organisms competing in natural environments while novel traits emerge during the normal course of replication and continuously change the ecological traits of organisms in populations. Sustained selection over evolutionary timescales in ecology requires consideration of different dynamics, tradeoffs, and strategies or conditions driving differential fitness compared to those considered in single-generation screening, sorting, and manual serial passaging.

[0033] Longer-term evolutionary dynamics can involve gradual changes in phenotype as well as punctuated equilibria or short changes in phenotype over evolutionary time. Longer-term evolutionary dynamics may involve strong selective pressure allowing populations to ascend local fitness peaks, and genetic drift allowing organisms to traverse saddles and valleys to find distant - and possibly higher or more advantageous - fitness peaks that otherwise would be difficult to evolve under strong selection.

[0034] In addition to the measurable complex traits of individual organisms in an otherwise abiotic environment, evolutionary ecologists have studied conditions for mutualistic or parasitic interactions between species to evolve. Altruistic or cooperative behaviors among kin can occur and are a subject of interest in ecology given the adaptive significance of cooperativity from multi-species lichen and biofilm-forming bacteria, to ant colonies and highly effective top predators such as wolves and lions. However, useful interactions between species or between individuals of the same species are unlikely to emerge from modem molecular biological screening, sorting, and serial passaging of individual agents in abiotic environments. The cooperative behavior in ants searching efficiently for food or the seemingly altruistic behavior of bees sacrificing themselves to defend a hive are believed to occur thanks to a process called “kin selection”, and cooperative behaviors allow for enhanced ecological activities, such as the ability of ants to search cooperatively for resources or bees to sacrifice themselves to increase the survivability of their hives. The shared genetic material among kin, such as ants in a colony, beesin a hive, or humans in a tribe, means that a cooperative or apparently altruistic genotype may be detrimental to the fitness of the individual but offset by a larger benefit to kind sharing the same genotype.

[0035] Another example of a complex trait necessarily governed by longer-term ecological and evolutionary dynamics is agent faith, or the ability of a biological agent to maintain a desired trait in a natural environment. In epidemiological systems, pathogens infect hosts in a population and the selective pressures on a pathogen may vary over time, for example, from selection for increased transmissibility early in an outbreak as variants with enhanced transmissibility replicate faster and increase in frequency, to immunoevasion later in an outbreak as the susceptible population declines and replication may be determined by the ability to evade host immune recognition. Some host pathogens used for ecological control or phage therapy may be passaged to have desirable traits in a lab but, in the field, may rapidly revert to less desirable traits, limiting the long-term utility of the biological agent due to loss of agent faith.

[0036] A classic example of the agent faith challenge is the transmission-rate virulence tradeoff observed with the Myxoma virus. Myxoma virus strains were serially passaged to achieve high virulence among rabbits in laboratory experiments and were then introduced into wild rabbit populations with the goal of eradicating non-native rabbit populations in Australia. However, after introducing Myxoma virus into wild rabbit populations, the agent evolved, virulence rapidly declined as less-virulent strains were more transmissible and highly virulent strains are believed to have killed their hosts before they could transmit. The tradeoff between transmission rate and virulence leads pathogens to intermediate virulence over multiple generations; the heritability and multi-generational stability of ecological traits are not selected for in short-term bursts of screening, sorting, and serial passage. Where virulent pathogens like bacteriophage are desired for their ability to eradicate bacteria that infect humans, such as multi-drug-resistant Staphylococcus aureus or Clostridium difficile, the risk of phage evolving intermediate virulence after administration to target a clinical bacterial population within a host may dampen the efficacy of phage therapy. Virulence of therapeutic phage is not a simple trait, but often a complex trait mediated by many genetic factors affecting many parts of the viral life cycle including environmental persistence, receptor binding, evasion of intracellular defenses, burst size, and more.

[0037] Sustaining selective pressures on organisms over longer periods of evolutionary time, and selecting organisms with both desirable traits and high heritability in the desirable trait, may increase the stability of complex traits such as virulence and reduce the risk of evolutionary reversion and decreases in desired ecological activity (e.g. decreased bacterial virulence of a bacteriophage) and increase the evolutionary robustness of heritable traits in the organism’s ecological context of competing for limiting resources (e.g. phage competing for hosts). A domestic cow gives birth to a domestic cow, and not the ancestral form of a wild bull, because it has been selected for generations, accumulating an insurmountable genetic distance between itself and less-desirable phenotypes, yielding a biological agent with highly heritable desirable traits not easily reverted by the mutations encountered in one generation. Thus, prolonged artificial selection with explicit attention to heritability and trait persistence over many generations may solve the problem of agent faith by yielding novel agents unlikely to revert to less-fit variants in the course of evolutionary changes over relevant short-term ecological timescales.

[0038] The utility of artificial selection derives from its abstraction and repurposing of evolutionary processes to meet current needs, enabling the selection for and enhancement of complex traits over long evolutionary timescales. Molecular and microbiological methods exist to screen and sort genetic elements, and manually serially passage microorganisms, but these methods don’t allow for the automation of artificial selection over longer evolutionary timescales to select for stable complex traits, nor do they enable users to explicitly tune the intensity of drift and selection, nor do they allow users to create conditions for the evolution of cooperative traits such as those that arise through kin selection, nor do they allow for persistent selection that selects for higher heritability and evolutionarily stable complex traits. There is a need for systems and methods capable of abstracting evolutionary processes and automating the artificial selection of microbial agents indefinitely, connecting the propagation of microbial agents to long-timescale evolutionary processes for improved user control of experimental microbial evolution over timescales long enough for microbial lineages to evolve novel and stable complex traits.

[0039] Artificial selection has been used for centuries on visible, multicellular organisms such as plants and animals. However, the difficulties of observing traits in microorganisms andpropagating individual microorganisms or their relatives has prevented the widespread artificial selection of microorganisms. Additionally, artificial selection in multicellular organisms and serial passaging of microorganisms has been manual, requiring user interventions in every iteration of multicellular agent replication or microorganism passaging. These limitations prevent the application of artificial selection to microorganisms and the lack of automation limits the ability of artificial selection experiments to scale to larger sets of organisms being monitored and propagated.

[0040] Another challenge of artificial selection arises due to the complexities of microorganisms’ ecologic behaviors being coupled with more than one genetic element; cells and organisms are not comprised of single genetic elements, and the desirable biochemical activities of gene products, such as enzyme catalysis, binding, or substrate specificity, are often just small components contributing to the replication, fitness, and overall ecological activity of the organism. Additionally, the replication and fitness of an organism is also impacted by how the organism interacts with biotic and abiotic factors of their environment. For example, a whole phage genome contained within a capsid may replicate due to the binding affinity of surface proteins, the ability of surface proteins to catalyze cell entry, inhibitory activities of expressed immunosuppressive proteins, hairpin loops and other secondary structures in phage RNA regulating RNA stability and gene expression, enzymes involved in capsid formation, and capsid properties that enable environmental persistence and subsequent binding, cell entry, and continuation of the viral life cycle. Where entire organisms are required to perform important biological functions, it’s necessary to automate the selection of entire organisms and not individual genetic elements.

[0041] There is still a need for methods to automate artificial selection of whole organisms such as viruses, cells, and other self-contained systems of parts whose replication is enabled by complex life histories. Additionally, cooperative ecological activities of organisms require multiple agents to interact with one another to determine the fitness of the group and replicate together based on the fitness of the group. In addition to cooperativity in ecological activities, genetic recombination of diverse agents grown together may lead to cooperative interactions over evolutionary timescales, such as phage recombining through co-infections to yield novelphage or bacterial conjugation allowing the transfer of plasmids and combinatorial exploration of novel genotypes of bacteria whose desired traits are under selection.

[0042] Serial passaging aims to select for organisms capable of improved persistence in an environment, such as phage capable of targeting a host or bacteria capable of metabolizing specific sugar. Serial passaging involves creating an environment with an abundance of specific resources the agent is desired to consume, waiting several generations of agent replication under the assumption that replication is determined by consumption of the resource provided, preparing a new environment with a fresh pool of resources, aliquoting a small volume of solution from the original mixture containing the agent to the new environment without agent, and repeating the process in series. Agents capable of consuming the resource as desired are prone will replicate in the environment and achieve higher abundances, such that faster-growing agents will start then- population growth in the subsequent mixtures at a higher initial population size than variants of agents in the same population less able to consume resources and consequently slower growth within the original vessel. Serial passaging has been effective at selecting for phage capable of targeting new hosts or microbes capable of novel catalytic functions.

[0043] Screening of microorganisms is often done, and methods exist to iteratively and synchronously select among populations with horizontally transferred genetic elements by comparing populations and propagating them all at once, but discretizing replication across all populations and forcing all populations to replicate at the same time loses a critical degree of freedom gained by allowing organisms with populations on different timescales to be screened continuously and propagated asynchronously. An asynchronous approach is needed to enable the populations with clear fitness advantages over less-fit populations to propagate immediately and other slower or lagged populations continue to mature to reveal their desirable traits.Additionally, current methods of screening and propagating require organisms be transferred from one full set of containers, or “vessels”, to a separate empty set of vessels, effectively halving the biological activity that would be possible if all vessels were full of active agents screened and asynchronously populated. Finally, existing methods of screening and propagating do not consider the longer-term evolutionary dynamics and develop systems for overseeing such longer-term evolutionary processes. There is still a need to automate the artificial selection of microbes for an indefinite number of generations, utilizing all available vessels or bioreactors atany point in time to maximize the productivity and diversity of agents grown in a fixed set of vessels.

[0044] There is still a need for automated and sustained artificial selection of microbes grown continuously and propagated asynchronously, maximizing the number of active populations in a given set of vessels, enabling both serial and branched-passaging for heritability estimation, the use of heritability in propagation plans, the degree of drift, the form and intensity of selection of organisms, the evolution of cooperative traits among microorganisms, and selection over longer timescales facilitating the evolution of robust traits and improved agent faith.5.1. Definitions

[0045] Unless defined otherwise, all technical and scientific terms used herein have the meaning commonly understood by a person skilled in the art to which this invention belongs.

[0046] “Agent” used herein is used to denote a biological organism capable of replication with the assistance of resources. Agents are comprised of many genetic elements capable of expressing gene products and may additionally have genetic components conferring traits in ways other than gene expression. For example, some genetic sequences may regulate gene expression, such as promoters, methylation sites, multiple chromosomes, or plasmids. Some genetic sequences may yield structural features (e.g., hairpin loops), the ability to bind to histones or nucleosomes, the ability to evade host defenses, and more. Agents may be naturally occurring organisms, organisms derived from screening or serial passaging, or genetically modified organisms.

[0047] “Microbiological agent” or “microbial agent” includes any type of microorganism, such as bacteria, fungi, viruses, yeasts, protozoa, algae (microalgae), helminths, and archaea.Subcategories of microbiological agents may include prokaryotic or eukaryotic microbiological agents, unicellular or multicellular microbiological agents, photosynthetic or nonphotosynthetic microbiological agents, or infectious or noninfectious microbiological agents.

[0048] “Microbial mixture” as used herein includes any mixture containing at least one microbe of at least one strain or species and additional parts such as resources required for microbial growth, microbial waste products, and more.

[0049] “Desired trait” as used herein is any heritable phenotype of an organism the user wishes to evolve, such as the production of a particular chemical compound, the ability to catalyze reactions involving a target substrate, the ability to catabolize certain compounds, binding affinity to a desired substrate, the ability to consume resources, transmissibility, infectivity of novel hosts, or ability to persist in the presence of certain environmental stressors such as extremes of temperature, pH, reactive chemicals, heritability of particular features of indicator trajectories, and more.

[0050] “Ecological activity” as used herein is the interaction between an organism and the biotic and abiotic factors of their environment. Organisms may have many ecological activities, and the desired trait may be the ecological activity under selection by the user.

[0051] “Resource(s)” as used herein is the set of chemical, biochemical, and / or biological reagents required for the replication of agents and, in some cases, the expression or demonstration of desired traits. For autotrophic microorganisms, resources may be as simple as basic chemical nutrients and energy sources such as light. For heterotrophic organisms, resources may include biochemical feed such as sugars and amino acids or entire organisms such as suitable bacterial hosts of bacteriophage agents.

[0052] “Indicator” as used herein is a physical, chemical, or biochemical property of a mixture enabling sensors to detect and, in some cases, quantify the levels of an agent’s ecological activity. For example, if the agent is a bacterial species and the resource is a desired biochemical mixture for the bacteria to be able to utilize for improved replication, the indicator may be optical density at 600nm (OD600) as commonly used to monitor bacterial cell growth. For another example of bacteriophage targeting bacterial hosts, lysis dyes can serve as indicators, such as dyes that react to free-floating DNA and change their fluorescent properties and thereby permit the detection of bacterial cell lysis. For another example of agents secreting desirable compounds that change the pH of the mixture, the pH itself or chemicals that react to changes in pH can be used to indicate the amount of a desirable compound produced in a vessel. Wheresingular, “indicator” may refer to either a single chemical or physical quantity observable by the sensor, or any mathematical formula for combining multiple chemical or physical quantities observed by one or more sensors.

[0053] “Vessel” as used herein is an isolated compartment or set of compartments capable of containing agents, resources, and indicators while also permitting sensors to monitor indicator levels of individual vessels, such as a single well in a 96-well plate connected to sensors that monitor pH, spectrophotometers, and other sensors capable of well-level resolution. Vessels may also be larger than wells on 96-well plates, such as flasks or beakers, and vessels may also be smaller than wells on 96-well plates, such as visible drops on a surface. Regardless of the size or shape or material of a vessel, it must be configured to be accessible by a sensor for measuring indicators and a fluid handler which is capable of moving aliquots of the mixtures into and out of the vessel. Vessels may be comprised of a series of compartments that allow the exposure of agents to, and monitoring of agents within, different environments with optional indicators in one, several, or all compartments, such as fluids of different temperatures or pH levels, vessels with compartments at different states of matter, such as a fluid compartment for growth followed by a desiccated compartment to trigger sporulation. While resources are required for replication, multi-compartment vessels can test the ability of agents to persist in extreme conditions, such as exposing agents to extreme pH environments without resources required for replication followed by a second compartment with resources required for replication to grow whichever agents persisted in the extreme environment of another compartment. Multi-compartment vessels allow for propagation plans that incorporate indicators of multiple ecological activities across different stages of an organism’s life cycle or different anticipated environmental conditions the organism must survive.

[0054] “Holding vessel” as used herein is a vessel or other compartment capable of holding vessel contents during propagation, allowing breeder vessels to be emptied and cleaned without loss of the breeder vessel contents that can be allocated to newly initialized vessels.

[0055] “Vessel indicator trajectory” as used herein is a set of paired numbers {t_i,x_i }_(i= 1 )AT corresponding to measurement events of a vessel’s indicator level {x_i} across a set of timepoints, {t_i} when the indicator values were measured. Vessel indicator trajectories containmore information about desirable traits than indicator levels at any point in time and allow estimation of traits, and trait heritability, relevant for population dynamics such as growth rate, carrying capacity, host-switching, resource consumption, and more.

[0056] “Generation” as used herein is a single replication cycle of the agent. For cellular agents, one generation is the time between arrival at similar stages of the cell cycle, such as GO of the parent to GO of the progeny, or G1 of the parent to G1 of the progeny, having undergone mitosis once. For phage and other infectious agents reliant on living hosts as resources, one generation is the time it takes from the pathogen’s descendants to arrive at similar stages of the pathogen’s life cycle from which the parent’s life cycle is started, such as receptor binding of the parent to receptor binding of the progeny, or cell entry of the parent to cell entry of the progeny.

[0057] “Iteration” as used herein is a single cycle of automated artificial selection starting with the inoculation of a vessel with agents, resources, and indicators, sensing of indicator trajectories, estimation of fitness in a propagation plan, and implementation of the propagation plan through branched or serial propagation to a subsequent set of vessels. Iterations may end in propagation for some vessels and for other vessels they may end with the termination of their lineage by disposal of vessel contents to waste followed by the utilization of their empty vessel for another lineage’s descendants.

[0058] “Maturity” as used herein is a measure of the phase of a given vessel’s agent population dynamics. Maturity for certain populations may be multi-phasic, such as “exponential phase” and “logistic phase” for populations undergoing logistic growth, where propagating vessels at exponential growth may be desirable. Maturity may also include continuous quantitative metrics of vessel age, such as the time since inoculation, time since entering logistic phase, and more, along with multivariate functions combining mapping multiple features of vessel indicator trajectories to a discrete set of maturation values.

[0059] “ Sensor” as used herein is a device capable of measuring the indicator level of a solution in a vessel at any point in time, such as spectrophotometers, fluorescent microscopes, pH electrodes or probes, magnetometers, or voltameters.

[0060] “Propagation event logic” as used herein is an algorithm converting indicator trajectories, optionally including historical trajectories, lineal histories connecting vessels, and vessel maturities, to trigger the generation and implementation of a propagation plan for at least one vessel.

[0061] “Propagation Plan” as used herein is an algorithm converting indicator trajectories, optionally including historical trajectories, the lineal histories connecting vessels, and vessel maturities, into a mapping between “breeders”, or vessels that will be propagated, and “killset” or vessels which are emptied and cleaned and into which breeders’ vessel contents are aliquoted to initiate the iteration of breeders’ descendants.

[0062] “Propagator” as used herein is a device capable of implementing the propagation plan by extracting vessel contents, cleaning empty vessels, initializing vessels with resource and indicator mixtures, and aliquoting contents from extracted vessels into cleaned vessels. In some embodiments, a propagator can comprise a liquid handling system with two pipettes for managing vessel propagation, fluid handlers or dispensers for resource and indicators, a holding vessel, and a cleaning brush; one pipette in the liquid handling system extracts volumes for breeder aliquots to place in the holding vessel and transfers aliquots from holding vessels to any other vessel in the vessel set, the other pipette extracts contents of vessels planned for termination and moves their contents to waste, the cleaning brush that cleans empty vessels, thereby allowing breeders to either serially passage (pipette breeding aliquot to holding vessel, empty contents of breeder vessel, clean breeder vessel, initialize breeder vessel by adding resources and indicator with fluid handler or dispensers, and aliquot holding vessel contents into the cleaned vessel) or be aliquoted iteratively to a series of vessels terminated, cleaned, and initiated by inoculation of resource and indicator. In some embodiments, propagators can be systems of tubes with pumps capable of extracting and moving aliquots, sterilizing and airdrying vessels, and moving breeder aliquots, resources, and indicators through tubes to implement a propagation plan. In some embodiments, propagators are an electrical grid on a surface capable of moving charged drops or droplets along clear paths on a surface to partially fuse drop contents, passaging drop contents in accordance to a propagation plan. In some embodiments, propagators can be a physical microfluidic mechanism for splitting droplets into at least two smaller droplets (aliquots of breeder vessel contents) which can be fused with dropletscontaining resources and indicators constructed to initialize descendant iterations according to the propagation plan.

[0063] “Automated artificial selection” as used herein is a system or method of combining at least one vessel, an agent, resource, sensor, event logic propagation plan, and propagator capable of asynchronous propagation of subsets of vessels. The union of the agents, resources, indicators, sensors, event logic propagation plan, and a propagator can allow an abstraction of evolutionary dynamics and fine-tuning of breeding strategies over longer-term evolutionary timescales, such as enabling genetic drift by allowing more randomness or serial passage in propagation plans, increasing the rate of selection over evolutionary time by increasing the number of vessels turned over at a propagation event, increasing the intensity of selection by decreasing the evenness of propagations across breeders and increasing the multiplicity of breeder allocations in a propagation plan, or changing the degree of mixing of vessel set contents across in propagation plans to change the intensity of selection for cooperative traits.5.2. Other interpretational conventions

[0064] Ranges: throughout this disclosure, various aspects of the invention are presented in a range format. Ranges include the recited endpoints. It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the invention. Accordingly, the description of a range should be considered to have specifically disclosed all the possible subranges as well as individual numerical values within that range. For example, description of a range such as from 1 to 6, should be considered to have specifically disclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6, etc. as well as individual number within that range, for example, 1, 2, 3, 4, 5, 5.3, and 6. This applies regardless of the breadth of the range.

[0065] “ Such as” as used herein is used to denote a non-exhaustive list.

[0066] “A” as used herein is used to mean “at least one”.

[0067] In this disclosure, “comprises”, “comprising”, “containing”, “having”, “includes”, “including” and linguistic variants thereof have the meaning ascribed to them in U. S. Patent law, permitting the presence of additional components beyond those explicitly recited.

[0068] Unless specifically stated or apparent from context, as used herein the term “or” is understood to be inclusive.

[0069] Unless specifically stated or otherwise apparent from context, as used herein the term “about” is understood as within range of normal tolerance in the art. Unless otherwise specified, “about” intends ±10% of the stated value. Where a percentage is provided with respect to an amount of a component or material in a composition, the percentage should be understood to be a percentage based on weight, unless otherwise stated or understood from the context.5.3. Overview of automating artificial selection of icroorganis s

[0070] The present disclosure provides methods and systems for automating artificial selection of microorganisms. In one aspect, the present disclosure provides a method for automating the artificial selection of microbial agents, comprising the steps of (1) maintaining a resource pool through use of a chemostat or other equilibrium host mixture, whether in solution or solid form with hosts optionally containing genetic features allowing indication of agent activity, (2) maintaining a reservoir of chemical indicators, (3) aliquoting hosts, initial population of agents, and indicators across more than one vessel, (4) monitoring indicator activity, (5) using indicator activity to compute propagation event logic regularly and (6) when propagation event logic is triggered, computing a propagation plan, (7) serial- or branched-passaging agents from prior iterations’ compartments to the next iterations’ compartments according to the propagation plan (8) repeating steps 1-7 except step 3 (initialization of all vessels) and optionally (9) computing estimates of agent fitness using prior iterations’ indicator activities for incorporation of heritability information, (10) updating the functions computing propagation event logic and propagation plans to modulate drift and selection, and (11) allowing users to initiate propagation, modify the propagation plan, or terminate the procedure at any point.

[0071] The present disclosure provides methods and systems for automating the artificial selection of microorganisms over many generations by mixing agents, resources, and indicators of desired traits into vessels, sensing desired traits through indicator trajectories across vessels,triggering propagation events based on functions of indicator trajectories, calculating propagation plans, and propagating contents according to the propagating plan by extracting all breeding and killed vessels in the propagation plan, cleaning vessels, aliquoting breeder contents according to the propagation plan into fresh resource and indicator pools in the cleaned vessel set. An automated propagator overseeing the sensing of indicators, triggering propagation events and calculating propagation plans, and propagation across iterations allows user control of evolutionary dynamics through altering propagation event logic and propagation plans to change the intensity of selection from genetic drift, allowing agents to explore and possibly traverse saddles in fitness landscapes, to intensify directional selection limiting the diversity of agents but increasing the short term rate at which the desired trait across the population of extant agents changes across iterations, and to implement selection of trait heritability across generations stabilizing biological agents and improving agent faith.

[0072] In some embodiments, resources may be kept at equilibrium, such as agar used to grow yeast being stored in a flask at constant temperature and mixed continuously, or hosts used to grow phage being kept at a steady state by a chemostat with constant influx of new resources and efflux of chemostat contents. In some embodiments, resources may also change over time, such as agar having higher concentrations of a nutrient the user wishes the selected agent to catabolize and lower concentrations of substitutable resources the agent may otherwise try to use to complete its cell cycle as the selected agents improve their desirable metabolic activity, or host compositions may change where the user aims to select a phage capable of infecting novel hosts, decreasing the phage’s native host concentration and increasing the novel host concentration across iterations as the phage demonstrates an improved ability to replicate in novel hosts over generations.

[0073] Indicators can allow a user to measure a desired trait. In some embodiments, when selecting the ability of bacteria to grow on a novel substrate, indicators may be standard spectrophotometric assays such as the optical density at 600nm. In some embodiments, when selecting the ability of phage to target a host, users may opt for either optical density 600nm or lysis dyes which fluoresce once the contents of host cells are released into the surrounding medium. In some embodiments, when selecting the ability of a phage to infect a novel host, users may use different colored fluorescent proteins to measure the ability of phage to target specifichosts. For example, users may genetically modify the original hosts to express a particular fluorescent protein (e.g. green fluorescent protein or GFP) and modify the novel hosts to express a different colored fluorescent protein (e.g. yellow fluorescent protein or YFP) and use the intensity of different proteins’ fluorescence as an indicator of the agent’s ability to target different hosts. Sensors can allow users to detect indicators inside vessels and record the observations for input into the propagation plan. In some embodiments, sensors may be passive sensors that measure ambient conditions such as sensors measuring pH, or active sensors such as fluorescent protein assays that rely on the excitation of fluorescent proteins and subsequent measurements of fluorescent intensity.

[0074] In some embodiments, propagation event logic converts indicator trajectories obtained by the sensor into an output to the propagator triggering the computation and implementation of propagation plans. Formalizing propagation event logic and propagation plans rigorously with mathematical notation enables users to evaluate or utilize a larger set of propagation event logic and propagation plans. The indicator trajectory of vessel i can be denoted x_i (t)GA and the absolute quantitative values in the set A can depend on the specific indicator and resolution of the sensor. The set of indicator trajectories across m vessels can be denoted x(t)GAAm.Propagation event logic, L: AAm— >{0,1} is a function converting indicator trajectories into either 0 (or FALSE: proceed with growth) or 1 (or TRUE: trigger propagation). Let M be the set of vessels, propagation plans incorporate current indicator trajectories, lineage relationships represented in a tree, TGT, and historical indicator trajectories HGH, to choose a subset, V£M with v elements, that includes a non-empty breeding set, B =V with b elements, and a matrix PGR (vxb) whose element p_(k,l) indicates the volume of breeder 1 to be aliquoted to vessel k in V. With this notation, the propagation plan is defined as the mapping P:{AAm, H, T}^{V, B, P}. In some embodiments, if indicator levels allow an estimate of the relative concentration of agents across vessels, then the volumes may be chosen to aliquot constant initial agent populations by aliquoting precise volumes, such as a unit volume from one vessel and half the volume from a different vessel with twice the estimated agent concentration, and this can be seen as a special case of the definition of P above as it defines a case where the elements of P are computed based on indicator values in AAm converted into a measure of agent concentration and balanced to ensure precise amounts of agent are aliquoted when initializing descendent vessels.

[0075] In some embodiments, the mathematical formulation of propagation plans connects a physical instantiation of the system and methods of the present disclosure to a mathematical structure for studying and turning evolutionary dynamics called a Wright-Fisher Process. In some embodiments, a Wright-Fisher Process involves drawing organisms in one generation to reproduce based in part on their relative fitness and filling the next generation with descendants of the previous generation with tunable degrees of drift or selection depending on how large a role relative fitness plays in biasing the random selection of organisms to replicate. By connecting physical instantiations of the systems and methods of the present disclosure to a statistical process, users can exploit mathematical abstractions of evolutionary dynamics to control longer-term evolutionary dynamics, such as tuning drift and selection to vary the extent of exploration of novel genotypes through drift versus the exploitation of current genotypes via stronger selection and directed evolution towards a local peak in a fitness landscape.

[0076] The Wright-Fisher Process is a special case of the present system and methods as it is a case of what population genetic literature calls “non-overlapping generations” where V=M and the individuals or vessels active in one generation don’t overlap temporally with any of their contemporaries’ descendants. The added degree of freedom from asynchronous propagation and overlapping generations speeds up and allows improved user control over evolutionary dynamics, such as selecting species under logistic growth that have a higher carrying capacity irrespective of growth rate by triggering propagation once the change in vessel’s population size over a given time interval is below a threshold, allowing propagation decisions to be made based on carrying capacity and independent of growth rates.

[0077] In some embodiments, the propagator implements the propagation plan. In some embodiments, if vessels are wells in a 96 well plate, an example of a propagator may be a computer-controlled liquid handler capable of extracting the entirety of any well on the plate, cleaning any well on the plate, and drawing a precise volume of liquid from each well in a 96 well plate, such as 2pL aliquots drawn from a liquid-handling pipette, holding aliquots either in a pipette or in a separate set of vessels while wells are being cleaned, and aliquoting that volume to another well in the plate allowing users to implement any propagation plan on the 96 well plate. Other configurations of physical vessel sets are also possible with the present disclosure, such as cylindrical or circularly arranged vessel sets (As depicted in Figure. 11) enabling rotation ofvessel sets through stages such as extracting, cleaning, priming, and sensing stages, where the extracting stage obtains aliquots for breeding, cleaning stage vacates and cleans vessel sets in preparation for future iterations, the priming stage loads resources and indicators prior to inoculation of agents, returning to the extracting stage allows aliquots of breeder contents into the initialized vessel, and the sensing stage logs the time and indicator state of active vessel observed for indicator trajectories which are input to the propagation event logic and propagation plan.

[0078] In some embodiments, application of the systems and methods of the present disclosure in a single automated system can allow for users to have enhanced control over longer-term evolutionary dynamics, such as the ability to systematically change resources as organisms evolve (e.g. increasing the concentration of novel hosts and decreasing the concentration of native hosts as phage evolve the ability to target novel hosts), change propagation event logic and propagation plans to modulate the degrees of drift or selection or propagate across iterations sooner as indicators of iterations reach thresholds of desired traits faster following prolonged selection to do so, and more. In some embodiments, users may have optional manual control over the system, allowing them to trigger propagation events and devise their own propagation plans. Users may utilize dataset of historical indicator trajectories from prior iterations or trajectories logged during prior uses of the claimed system, to improve propagation plans such as by estimating the heritability of traits and using prior generations’ trait observations combined with heritability to improve estimates of current active vessel traits. Users can use historical trajectories over longer evolutionary timescales to forecast the rate of evolutionary change across iterations and modify propagation event logic and propagation plans based not only on active vessel indicator trajectories but long-term evolutionary dynamics. The system described herein can be a physical embodiment of a Wright Fisher Process enabling a wide variety of algorithms with tunable selection and drift, and allowing in silico simulation of the Wright Fisher Process to train algorithmic propagators to oversee longer-term evolutionary dynamics. In some embodiments, users may be able to design dashboards (As depicted in Figure. 6) visualizing evolutionary histories of lineages being artificially selected, and the current vessel set can include vessels inoculated across a range of past iterations, allowing users to draw on historical agent repositories for measurement, comparison, and diversification of agent pools in the active vessel set.

[0079] Optional steps can be added to obtain more data on or user control over evolutionary dynamics of agents, such as sequencing organisms within vessels (e.g., via sequencing) to determine the genotypic changes underlying observed phenotypic changes, pausing iterations to genetically modify or allow conjugation and horizontal gene transfer between agents prior to implementing propagation plans, modifying or inserting functional motifs into genes or change their rate of evolution such as through use of more or less error-prone polymerases in hosts and / or agents modulating the rate of mutation, and more. Another optional and useful variation of the methods and systems of the present disclosure can involves using vessels with multiple compartments, each with the same or different indicators, to construct multi-indicator propagation plans combining multiple stages of an organism’s life-cycle or multiple desired activities measured separately, such as mitotic growth of agents in solution followed by desiccation to trigger quiescence and sporulation, allowing users to select for agents whose ecological activities satisfy a combination of criteria across life stages like growth, dispersal and / or dormancy, or ability to avoid degradation in desiccated environments (e.g. by first exposing agents to a desiccated compartment followed by a fluid and nutrient-rich compartment allowing growth of organisms that survived desiccation).

[0080] In some embodiments, the methods and systems of the present disclosure can be used for selection of phage switching hosts. In some embodiments, the selection of phage switching hosts or organisms demonstrating ecological activity across multiple compartments are instantiations of a broader set of optional variations of the systems and methods involving multiple distinct indicators combined into multivariate propagation event logic and propagation plan. In some examples, x_l (t) can denote the indicator values of indicator 1 across active vessels at time t. The propagation event logic and propagation plan can take as inputs these multiple indicators to compute a plan as defined previously (chanting the space of m vessels’ indicator values AAm to a multidimensional one A (mxn) for m vessels and n indicators). In some embodiments, the propagation plan may still optionally change across iterations to alter the relative importance of different indicators for vessel fitness as the agent’s desired activity changes over evolutionary time.

[0081] The methods and systems described herein can be used to select many types of traits across many types of organisms, such as the ability of phage to lyse specific bacteria, to evolvephage for probiotic or pharmaceutical antibiotic use, the ability of a microbe to consume a specific resource, the ability of algae to produce biofuels with high heritability of biofuel production to obtain biofuel-producing alga with high agent faith, the ability of bacteria commonly used in biomanufacturing to produce the desired compound with increased efficiency and / or yield (e.g., E. coli with a plasmid that has the insulin gene used to mass produce insulin or, more generally, the ability of microbes to produce a desired compound), the ability of oncolytic viruses, native T-cells, or genetically modified T-cells to target cancer cells without targeting host cells (e.g., highly specific resource consumption), the ability of phage to target a wider range of hosts (e.g., less specific resource consumption), the ability of cells to grow in higher temperatures (more generally, the ability of organisms to persist in extreme environmental conditions to expand the niche & develop enzymes enabling extremophilic niches), and more. The specific desirable traits, agents, and indicators of traits may often determine the exact conditions under which agents are grown, the resource and indicator reagents used, the propagation event logic and propagation plans. In some embodiments, the same agent and indicators can be grown in different conditions to select for different desirable traits (e.g., bacterial cells with cell densities indicated by OD600 can be grown in low-pH environments to select for persistence in acidic environments, or grown in high temperature environments to select for thermophily, etc.), or grown in different resources with different reagents to select for different traits using the same indicator (e.g. growing E. coli with galactosidase knockouts in the presence of galactose will select for galactose consumption, whereas growing these same E. coli in the presence of standard broth mixed with a basic solution can select for bacteria capable of growing in basic environments).

[0082] In some embodiments, the resulting organisms from the present disclosure will be impossible to find in nature as a result of their being direct descendants of a specific genotype and enough mutations removed as products of an unnatural process of automated artificial selection. Passing organisms through a deliberate, designed, non-natural automated artificial selection process for enough iterations can result in enough mutations of adaptive significance to yield agents unlikely to be found in nature due to the number of required mutations a natural organism would have to explore in order to generate the mutations generated from the automated artificial selection, and the selective environments required to maintain those traits not found in the wild-type ancestor. For example, an artificially selected agent that differs from its naturalancestor by 132 mutations at specific sites is unlikely to ever arise in nature as there are more possible alternative nucleotides in just those 132 sites and random mutation will not just mutate those 132 sites but rather mutate the rest of the organism’s genome as well. The production of non-natural biological agents adds to the utility of the methods described herein by enabling the discovery and characterization of patentable biological agents.

[0083] The methods and systems described herein can be used to generate microorganisms with artificial enhancement of desired traits, such as phage with an artificially high ability to lyse a target bacterium, E. coli with an artificially high rate of production of a desired gene product, algae with an artificially high rate of biofuel production, and more. Automating the artificial selection of microbes to perform desired functions can allow indefinite optimization and exploration of microbial evolution over long timescales, and both naturally occurring as well as genetically modified organisms can serve as starting points of longer-term artificial selection procedures for further enhancement of microbial traits.

[0084] In some embodiments, fitness can be assessed by indicator measurements. Where an indicator measure is a measure of a condition within a vessel.5.3.1. Automated system for preparing and / or cultivating microbiological mixtures

[0085] FIG. 1 illustrates a system 100 for performing automated passaging, according to aspects of the present disclosure. FIG. 1 shows various components, but there may be more, fewer, or different components in other designs of the system 100. Also, the functions of one or more of the components may be combined into a single component instead of being separate components. Similarly, the functions of a single component may be separated into more than one component. At a high level, the system 100 includes a breeding cabinet 110, a processing circuit 140, a resource pool 120, and outputs 130.

[0086] The breeding cabinet 110 may include a housing 160 that holds or partially or fully encloses various components to perform automated passaging for microbiological mixtures. In some embodiments of the system, the housing 160 acts as an enclosure for the system 100. In some embodiments, the housing 160 has a base, a top, and at least one side wall. In some embodiments, the top further comprises a lid that is capable of opening and closing. The housing160 may take various different shapes, such as square, rectangular, round, cylindrical, domeshaped, etc. The housing is described in more detail in later figures.

[0087] The breeding cabinet 110 may include a vessel set 114 having one or more vessels for holding microbiological mixtures, reagents, sanitizing fluids, and other mixtures and fluids. In various embodiments of the system 100, the vessel set 114 comprises a rack arrangement with the vessels arranged in a rack. In some embodiments, the vessel set 114 comprises a platform where the vessels are positioned on some type of platform, such as a rotational platform. The vessels may be arranged, for example, around or along the periphery of the platform. In one example, the vessels are configured in a circular orientation. In another example, the vessels are arranged in rows in a grid-like orientation. In a further example, the vessels are arranged as microtiter plates, such as 6-well, 12-well, 24-well, 48-well, 96-well, 384-well, or 1536-well plates.

[0088] The vessels themselves may be cuvettes, test tubes, beaker, flasks, petri dishes, cryovials, PCR tubes or plates, bottles, boats, dishes, etc. In some embodiments the vessels are made out of plastic, glass, or quartz. In some embodiments of the system, the vessel set comprises 1 or more vessels. In some embodiments, the vessel set comprises 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303,304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, or more vessels.

[0089] In the housing 160, there may also be at least one manipulator 111 for moving around one or more of the components in the housing 160. There may be, for example, a manipulator associated with each moveable component in the system 100 or with some moveable components. For example, there may be a manipulator 111 configured for manipulating the vessel set. In some embodiments, the manipulator is a rotary manipulator with a motor, in which case this rotary manipulator rotates the vessel set. In some embodiments, the motor is a stepper motor. In some embodiments, the motor is a servomotor. Thus, the vessel set may sit on a rotary platform that is rotated in a circle by a motor as a manipulator 111 to position the vessels at various locations in the housing 160 for receiving fluids. In some embodiments, the manipulator is a linear manipulator, such as a motorized linear stage. In some embodiments, the manipulator is situated on the underside of the vessel set or is situated adjacent the vessel set. In some embodiments, the manipulator may be a robotic arm able to move the vessel set 114, the 115, the sensor 112, fluid handler 113, or the aspirator module 119, to different points on the mounting plate 1102, or to different components of the system such as the vessel set 114, the resource dispenser 115, the sensor 112, fluid handler 113, or the aspirator module 119. The vessel set may move linearly across a platform in the housing, based on the manipulator and its corresponding motor, the position the vessels at different locations in the housing to receive fluids. The manipulator may also be an arm with a grasping mechanism that picks up individual cuvettes / test tubes or picks up plates or racks of vessels and places them at different locations in the housing 160.

[0090] In some embodiments, the manipulator moves components in the housing 160, such as the resource dispenser, to the vessel set. In some embodiments, the manipulator moves the vessel set to the components, such as the resource dispenser. There may also be manipulators associated with components that dispense and aspirate fluids to move the tip up and down or side to side. Inthese cases, the motor of the manipulator may vertically translate the component containing the tip along a vertical track. In some embodiments, the component may horizontally translate along a horizontal track.

[0091] In various embodiments, the system comprises as agitator or agitation mechanism 118 for agitating the vessel set. In some embodiments, the agitator agitates the contents of the vessel set. The agitator may agitate all of the vessels at once or may agitate them one by one. In some embodiments, the agitator may be a vortexer.

[0092] In some embodiments, the system comprises at least one, or multiple sensor modules 112. In some embodiments, the system comprises 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, or more sensor modules. In some embodiments, each sensor module comprises 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, or more sensors. In certain embodiments of the system, the sensor is capable of taking a measurement related to the contents of a vessel. In some embodiments, the measurement is an indicator measurement, such as optical density (OD), turbidity, fluorescence, pH, temperature, pressure, dissolved oxygen (DO), carbon dioxide, ion concentration, substrate concentration, metabolite concentration, agitation or mixing speed, gas flow, microbial gas flow, mRNA abundance, protein abundance, nucleic acid sequences, gene sequencing, protein sequences, and cell viability. In some embodiments the sensor is capable of taking multiple indicator measurements. In certain embodiments of the system, the sensor is a pH meter, thermometer, spectrometer, spectrophometer, magnetometer, gene sequencing sensor or voltameter. In certain embodiments of the system, the sensor is situated or manipulated such that it can take measurements of the vessels. In some embodiments, the sensor is situated or manipulated such that it can take measurements of the contents of the vessels. In some embodiments, the sensor can take measurements and provide readouts in real time. For example, where the sensor is a nucleic acid or gene sequencing sensor, the sensor can detect specific RNA modifications in real-time.

[0093] In certain embodiments of the system 100, the sensor 112 is configured to move or be moved to interact with the vessels set 114. In some embodiments, the sensor 112 comprises a robotic arm.

[0094] In some embodiments, the vessels are manipulated to rotate by the sensor. For example, where the vessels sit on a rotary platform, and the sensor module is mounted near the platform, the vessels may be rotated such that each one can be positioned adjacent the sensor to allow the sensor to take a reading from that vessel. In some embodiments, the vessels are manipulated or moved to the sensor.

[0095] In certain embodiments, the system comprises a resource dispenser 115 (also called resource dispenser module) configured to dispense resources. The resources may be any fluids, such as any reagents or mixtures needed in the system. In some embodiments, the reservoir contains a sanitizer used to sanitize the vessels or growth media for growing bacteria, yeasts, etc. The resource dispenser 115 may have a dispensing tip. In some embodiments, the resource dispenser module 115 has an aspirating tip. These may be the same or two separate tips. In certain embodiments of the system 100, the resource dispenser 115 has a motorized linear stage as a manipulator 111 that may move the dispenser up and down put the tip into the vessel and move it out of the vessel. The resource dispenser 115 may translate up and down vertically along a track or from side-to-side horizontally along a track. In some embodiments, the dispensing tip is motorized, moving upwards and downwards. In some embodiments, the manipulator 111 is configured to move the resource dispenser through three-dimensional space. In some embodiments, the manipulator 111 is configured to move the resource dispenser 115 to interact with at least one of: the sensor module or sensor 112, the vessel set 114, the fluid handler module or fluid handler 113, the aspirator module 119. In some embodiments, the resource dispenser module 115 comprises a robotic arm.

[0096] In some embodiments, the resource dispenser 115 has at least one tube for external resource connections where it can access a resource pool 120 that contains input(s) or resources. In this resource pool may be various containers or reservoirs 121 and 122 of reagents, growth media, sanitizers. These reservoirs can be actively restocked even as the system is being operated. These reservoirs may each be connected to a tube 101-1 that delivers the resource to the dispenser 115 in the housing 160. In some embodiments the resource dispenser 115 has at least one tube for aspiration connections. In some embodiments, dispensing tip dispenses the contents of the resource pool or reservoir into, for example, one of the vessels. In someembodiments the dispensing tip is replaceable. In some embodiments, the resource pool 120 has a chemostat.

[0097] In certain embodiments, the system 100 comprises a fluid handler 113 (also called fluid handler module) for handling certain fluids. For example, this module may manage the inoculation of the vessels in the vessel set 114 with the microbiological agents. The fluid handler module 113 has a dispensing tip. In some embodiments, the fluid handler 113 as an aspirating tip. In some embodiments, the fluid handler has at least one tube for external reservoir connections. This may be a tube 101-1 to connect to input(s) to access, for example, reservoirs 121 and 122 (or other reservoirs). The fluid handler 113 may have at least one tube for aspiration connections. This may be a tube 101-2 to connect to output(s) where it can deposit wastes into a waste reservoir 131. In some embodiments, the output(s) include a storage bank 132 where materials may be stored, such as the contents of one or more of the vessels. In some embodiments, the output(s) include a sequencer 133 for performing sequencing and / or a mass spectrometer 134 for taking measures of samples from the vessels. In certain aspects, the fluid handler 113 has an internal reservoir and may dispense contents of the internal reservoir. In some embodiments, dispensing tip dispenses the contents of the external reservoir. In some embodiments, the dispensing tip is replaceable.

[0098] In certain embodiments of the system, the fluid handler module 113 is motorized or has a motorized linear stage. In some embodiments, the fluid handler 113 is linearly motorized, and so is designed for moving upwards and downwards. In some embodiments, the dispensing tip is motorized, moving upwards and downwards. In some embodiments, the manipulator 111 is configured to move the fluid handler 113 through three-dimensional space. In some embodiments, the manipulator 111 is configured to move the fluid handler 113 to interact with at least one of: the sensor module or sensor 112, the vessel set 114, the resource dispenser module or resource dispenser 115, the aspirator module 119. In some embodiments, the fluid handler module 113 comprises a robotic arm.

[0099] In some embodiments, the system 100 comprises an aspirator module 119 (also called an aspirator). The aspirator 119 may manage all of the aspirating or some or most of the aspirating that occurs in the system 100. For example, the aspirator 119 may handle removal of all of thewaste materials from the system 100. The aspirator module 119 has an aspirating tip. In some embodiments of the system, the aspirating module 119 is motorized or has a motorized linear stage. In some embodiments, the aspirating module 119 is linearly motorized, and so is designed for moving upwards and downwards. In some embodiments, the aspirating tip is motorized, moving upwards and downwards. In some embodiments, the manipulator 111 is configured to move the aspirating module 119 through three-dimensional space. In some embodiments, the manipulator 111 is configured to move the fluid handler 113 to interact with at least one of: the sensor module or sensor 112, the vessel set 114, the resource dispenser module or resource dispenser 115, the aspirator module 119. In some embodiments, the aspirating module 119 comprises a robotic arm.

[0100] In some embodiments of the system, the aspirator module at least one tube for aspiration connections that lead to an external reservoir, such as outputs 130. In some embodiments, the aspirator module has at least one tube 101-2 for aspiration connections to deliver materials aspirated from a vessel to the outputs 130 location. The outputs 130 may include a waste reservoir 131 for collecting waste material aspirated by the aspirator. In some embodiments, the aspirator module has an internal reservoir. In some embodiments, aspirator module suctions the contents of a vessel into a reservoir, such as an internal or external reservoir. The aspirating tip may replaceable.

[0101] While the fluid handler, resource dispenser and aspirator are all shown as separate modules, two or all of these functions may be combined into a single module that has multiple separate tubes for moving different materials and has different tips for dispensing and aspirating different materials. This design may also have more than one internal reservoir for storing materials inside the module. In some embodiments, this component has a multi-tip pipette type design. In other embodiments, this component has a tip added each time the tip is to be used to dispense or aspirate, after which the tip is removed.

[0102] In embodiments of the system, the breeding cabinet 110 includes an air recirculation system with a filter 116 to allow for air recirculation within the housing and associated with the contents in the vessels and all of the components in the housing. This may, for example, help reduce excess humidity or allow for control of humidity.

[0103] In some embodiments, the breeding cabinet 110 includes a heating and / or cooling element 117. It may also or alternatively include a heating and / or cooling sensor 117. The element allows for heating and cooling inside of the cabinet 110 and the sensor(s) 117 all for sensing of the temperature inside the cabinet, allowing for temperature control around the environment of the vessels. In some embodiments, the sensors / elements 117 connect via a connection 102-2 to an environmental temperature controller 150. This controller 150 may be a control pad for setting the temperature at a certain level. It may also be a component for cooling (e.g., a fan or refrigerant-based cooling element) or heating (e.g., a heating element).

[0104] In embodiments, the system includes a processing circuit 140 with a processor. This connects to the breeding cabinet via connection 102-1. The processing circuit 140 or processor can interact with a graphical user interface (GUI) 141 that allows the user to control operation of the breeding cabinet and its components. For example, the user may be able to set the number of passaging cycle, may control times, temperatures, etc. The user may be able to track progress and look at readings from the sensor on the GUI. The user may be able to review logged data collected during use of the system 100. There may be a dashboard 142 that acts as the main hub where the user goes to access content related to the system 100. There may also be an autobreeder 143 function that generates propagation plans, as well as propagation event logic 144 that triggers event actions. The processor interacts with a computer-readable storage medium (not shown) that stores instructions that, when executed, cause the system to perform a collection of steps related to automated passaging.

[0105] In summary, the system 100 may be an automated system for preparing and / or cultivating microbiological mixtures. The system may have:i. a housing;ii. a vessel set containing a plurality of vessels arranged in a format within the housing and configured to hold the microbiological mixtures;iii. at least one resource dispenser module configured for delivering reagents and / or mixtures;iv. a fluid handler module configured to be adjacent to the vessels to transfer reagents and / or mixtures into and out of the vessels;v. at least one sensor module configured to be adjacent to the vessels to acquire indicator measurements from the microbiological mixtures in the vessels; vi. at least one manipulator module coupled to and configured to move one or more of the vessel set, the resource dispenser, the fluid handler, and the sensor module; vii. a processor in communication with the modules; andviii. a computer-readable storage medium storing instructions that, when executed by the processor, cause the processor to execute a propagation plan and to control operation of the modules based on sensor feedback and the propagation plan.

[0106] The system may also include an aspirator module, an agitation mechanism, among other components. The system is configured for monitoring, manipulating, preparing and / or cultivating microbial mixtures.

[0107] In some embodiments of the system the stored instructions in the computer-readable storage medium further comprise instructions to:i. control the manipulator module and fluid handler module to initiate automated artificial selection of microbes from the microbiological mixtures by inoculating the vessels with the microbiological mixtures,ii. control the manipulator module and sensor module to measure and recursively log the indicator measurements and times for each vessel as propagation histories until a propagation event is triggered or until the system is terminated, andiii. provide the logged indicator measurements and the propagation histories for determination of the propagation plan.

[0108] An example embodiment of the system is provided in other figures described below.5.3.2. Methods are provided for automatically preparing and / or cultivating a microbiological mixture

[0109] FIG. 2 provides a flowchart illustrating the method, according to an embodiment of the disclosure. In other examples, the method may include more, fewer, or different steps than those presented in FIG. 2.

[0110] FIG. 2 first shows the method including initializing 201 at least one empty vessel with initial resource(s) from the resource pool (e.g., using the fluid handler and resource dispenser),creating at least one active vessel. For example, one or more reagents or growth media may be added to the vessel from the resource pool via the dispensing tip of the fluid handler. The individual vessel in the vessel set may be moved to each of the resource dispenser and the fluid handler to receive fluids, or the resource dispenser and the fluid handler may be moved to the individual vessel in the vessel set.

[0111] FIG. 2 also shows measuring 202 initial indicator(s) of each vessel in the set of active vessels with sensor(s). This provides information about the progress in each of the vessels with regard to the microbiological mixture inside.

[0112] The method also includes cultivating 203 at least one active vessel for a short period of time. The vessel may enter an incubation station in the housing or the entire vessel set may be cultivated in the housing.

[0113] The method then measures and logs 204 the indicator values using sensor(s) of each active vessel. This allows for recording of the progress for the microbiological mixtures in each vessel.

[0114] Optionally, the method calculates 205 fitness of each active vessel based on indicator measurements and historical logs. Optionally, the method updates 206 an intra-procedural model of fitness progression for each active vessel.

[0115] The method then reaches a decision point in which it determines whether the desired breeding condition has been met 207 or whether a time termination condition has been met 207 for at least one active vessel, based on the propagation event logic. If the desired breeding condition has not been met, the method returns to step 203 of cultivating the vessels. If the desired breading condition has been met or a time termination condition has been met, the method proceeds to the next decision point.

[0116] At the next decision point, the method determines if the post-procedural goal has been achieved 208 or if a procedure termination condition was reached 208 for at least one active vessel. If the procedure termination condition was reached, then the procedure is complete and the process ends. If the post-procedural goal has been achieved, the method continues togenerate 209 a propagation plan using the auto-breeder function for at least one active vessel that has met the breeding condition or time termination condition.

[0117] The method prepares 210 the number of vessels required to implement the optimal propagation plan, and, as needed, uses active vessels for preparation based on their fitness by emptying / cleaning the lowest fitness vessels until enough are available for propagation.Optionally, the method outputs 211 to storage or discard samples from the active vessels

[0118] In one embodiment, the method comprises a method for automatically preparing and / or cultivating a microbiological mixture. The method includes:i. generating a propagation plan for the microbiological mixture in an automated system; andii. executing the propagation plan by:dispensing from a fluid handler the microbial mixture into vessels in a vessel set to inoculate the vessels to initiate automated artificial selection;applying a sensor module to take indicator measurements of the vessels representing a condition within the vessels;recursively logging the indicator measurements and times for the vessels until a propagation event is triggered or the method is terminated; andadjusting one or more subsequent manipulations of components of the automated system in accordance with the indicator measurements received from the sensor module.

[0119] In some embodiments of the method, the method further comprises storing the indicator measurements taken by the sensor module to make them available for propagation-event logic and propagation-plan computation.

[0120] In some embodiments of the method, the method further comprising operating a propagation-event logic algorithm to convert the indicator measurements and propagation histories into a switch activating propagation events.

[0121] In some embodiments of the method, the method further comprising storing and operating a propagation-plan algorithm to activate upon a propagation event and convertindicator measurements and propagation histories into a plan to terminate reactions in a subset of vessels in the vessel set.

[0122] In some embodiments of the method, the method further comprising re-using contents of vessels used for breeding microbes in designated aliquots to initialize new reactions in recently emptied vessels.

[0123] In some embodiments of the method, executing the propagation plan further comprises:i. initializing a first iteration of the artificial selection;ii. logging a first set of indicator measurements and times;iii. logging a second set of indicator measurements and times;iv. computing propagation-event logic based on the logged first and second set of indicator measurements and times;v. upon triggering of a propagation event, computing a first propagation plan based on the logged first and second set of indicator measurements and times;vi. implementing the propagation plan;vii. logging further indicator measurements and times;viii. using logged information to inform downstream propagation-event logic and propagation plans; andix. repeating the above steps until a goal has been reached.

[0124] In some embodiments, the method further comprises cleaning emptied vessels prior to inoculation of mixtures according to the propagation plan.

[0125] In some embodiments, the method further comprises:i. generating propagation-event logic and a propagation plan after n prior propagation events based on all or any portion of prior logging of indicator measurements; andii. proceeding to populate / initialize, sense, and propagate descendant microbiological mixtures in a recursive fashion, appending iteration logs and propagation logs until a procedural termination condition is reached.In some embodiments, the method measures or tracks microbial genotypes or phenotypes. The method may propagate microbes with a selected genotype or phenotype. In some embodiments, the method sustains selective pressure long enough to allow evolution of a desired trait. In some embodiments selective pressure can be applied for directional, diversifying, purifying, and other types of selection; in these embodiments, selection intensity can be high or low, and both types of selection and the intensity of selection can change over time based on user input, trends in trait evolution, time elapsed int types of selection, and more. In some embodiments of the method, the timescale for sustained selective pressure is 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120, 121, 122, 123, 124, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 139, 140, 141, 142, 143, 144, 145, 146, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 158, 159, 160, 161, 162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232, 233, 234, 235, 236, 237, 238, 239, 240, 241, 242, 243, 244, 245, 246, 247, 248, 249, 250, 251, 252, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269, 270, 271, 272, 273, 274, 275, 276, 277, 278, 279, 280, 281, 282, 283, 284, 285, 286, 287, 288, 289, 290, 291, 292, 293, 294, 295, 296, 297, 298, 299, 300, 301, 302, 303, 304, 305, 306, 307, 308, 309, 310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393, 394, 395, 396, 397, 398, 399, 400, or more hours. The system can be run for the same amount of time.

[0126] It is also noted, that the timescale for sustained pressure can be a fraction of an hour, for example it can be 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, or 60 minutes. The system can be run for the same amount of time.

[0127] It is also noted, that the system can be run indefinitely, with sustained pressure for the same amount of time, or the system can be run until the resources available to the system run out if they are not actively restocked.

[0128] FIG. 3 depicts a state diagram of a vessel state and related state transition events and propagation event logic in accordance with an embodiment. In the diagram, it shows a vessel state initially as Empty. Following an Inoculation Event with a microbiological agent, the vessel state transitions to cultivating. This phase continues until the Propagation Event Logic determines that it should be Finished, either because it has reached the maximum incubation time, it has reached maturation, it has been assigned to be killed by another propagating vessel, or any other Propagation Event Logic. At this point, the vessel state transitions to Finished. A second Propagation Event Logic is applied to determine if the vessel has been selected for breeding. If ‘yes’, a Propagation Event is triggered; if ‘no’, a killing / cleaning event is triggered and the vessel state transitions back to Empty and the process is restarted.

[0129] FIG. 4 depicts an example of a representative fitness progression of three vessels, as measured with a sensor such as optical density, during a period of incubation in accordance with the present disclosure. It shows the progression of vessel 2 401, vessel 1 402, and vessel 3 403 with vessel 2 showing the highest fitness over time since initialization of the vessel.

[0130] FIG. 5 depicts an example of a representative model fitting for the predicted fitness of vessels, as measured by a sensor such as optical density, in accordance with the illustrative embodiment 500. In some embodiments, propagation event logic can be entirely functions of time, triggering propagation events at specific times, or functions of indicators, triggering propagation events when fitness estimates reach a threshold.

[0131] FIG. 6 depicts a graphical user interface for monitoring and controlling a system for performing automated branch passaging in accordance with the present disclosure.

[0132] The user dashboard of FIG. 6 provides an example for visualizing the propagation log according to the present disclosure for a special case where propagation event logic is based on time thresholds and propagation plans turnover all vessels in the vessel set. The dashboard shown is from a simulation of a method where the number of cells in well i at time t, N_i (t) will grow over time according to the equation(dN_i) / dt=γ_i N_i (1-N_i / K)where K is the carrying capacity and it_i the growth rate of cells in well i. Descendent well j is simulated to have a growth rate γ_j=γ_i+ε_(i,j) where c_(i,j)~N(0,σ2) are independent, identically distributed normal random variables with variance σ2. The indicator is assumed to be similar to optical density OD600 (e.g., some quantity proportional to the number of cells in the vessel). The relative fitness values are the relative indicator levels after 3 hours of growth. The dashboard shows a traitgram to track historical progress of fitness levels over many propagations, the logistic growth curves to monitor current microbial performance, optional alternative breeding strategies for user control of selection, and a user-override “BREED” button to allow users to initiate propagation plans earlier than the 3 hours programmed.

[0133] FIG. 7 depicts an example fitness legend (left) for visualizing relative vessel fitness and an example propagation plan (right) based on ranked fitness in accordance with the present disclosure.

[0134] FIG. 8 depicts an example propagation plan between Parent and Offspring wherein the breeding plan for the Offspring incorporates a blending of mixtures from at least two vessels of the Parent vessel set, in accordance with the present disclosure.

[0135] FIG. 9 depicts an example incubation wherein a vessel subset is utilized to expose an agent to several different environments simultaneously, in accordance with the present disclosure.

[0136] FIGS. 10-14 are described below as an example embodiment of the system.

[0137] FIG. 15 depicts the results of a simulation of an asynchronous branched propagation process that results in trait evolution. The in-silico proof principle of FIG. 15 provides for asynchronous branched propagation enables automated trait evolution. Populations undergo logistic growth with heritable variation in growth rate, growth rates are estimated by rolling generalized linear models, propagation event logic initiates propagation upon either vessel maturation (defined as deceleration or significant decrease in growth rates) or fitness supremacy, defined as z-scores greater than 1 between the fastest-growing and slowest-growing vessel, followed by propagation plans where the fastest-growing vessel is the breeder and the killset is the set of all vessels with growth rates greater than or equal to 1 z-score below the fastest-growing vessel. Initial doubling times are 6.9 hours, and final doubling times 3.5 hours, indicating significant automated evolution in a small number of doubling times under model assumptions. Plotted are final estimated growth rate of active vessels (left) and actual growth rate as a phenogram (right) of five vessels undergoing artificial selection with the claimed system and methods, resulting in in silico evolution of faster agent growth rates.

[0138] FIG. 16 depicts a representative phylogenetic tree tracking the process of five vessels undergoing asynchronous branched passaging with the claimed system and methods, indicating serial passage events in red circles, as well as branched-passage events with variable branch multiplicity.

[0139] FIG. 17 depicts experimental data acquired with the system performing asynchronous branched passaging with 40 parallel vessels containing yeast over 73 hours. The readout in FIG.17 provides experimental data acquired with the system. The data depicted was captured over 73 hours of asynchronous branched propagation of yeast. Resource was administered via a periodically resupplied IV bag of 48g dry malt extract in 400mL H20, boiled to sterilize then injected into IV bag. Propagation event logic initiated propagations when the 10 data point moving average of log OD trajectories crossed the horizontal line at ln(OD) > -1.0, selecting for yeast capable of growing quickly to exceed the threshold value. Propagation plans were computed based on the age of vessels: once a vessel is chosen to propagate based on propagation event logic above, it would self propagate and then branched-propagate into at most two additional vessels that had been initialized (i.e. propagated into) 15 minutes or more before the propagating vessel. For propagation events, a 200mL aliquot of the propagating vessel wasextracted, the target vessel was aspirated, filled with isopropanol, the isopropanol aspirated, new resource added, and finally the 200mL aliquot of propagating vessel contents added & newly inoculated vessel mixed briefly by sending it to a position with magnetic spinners. Vertical grey bands indicate all propagation events taking place across the 40 vessel system. Vertical solid black lines indicate propagation events of that vessel and dotted vertical lines indicate events where the vessel was propagated into from another vessel. Asynchronous branched propagation enables a continuous “race-to-threshold” propagation logic, allowing many vessels to continue growing (vessels that might be terminated or interrupted in synchronous propagation) while any vessels reaching criteria indicative of high fitness can competitively take over resources of less-fit vessels.

[0140] FIG. 18 depicts a subset of the experimental data depicted in FIG 17. Vessels propagated independently of one-another until a sequence of events triggering a branched propagation from 7 to {7, 8, 14} in the final propagation event of this figure. Prior to this branched propagation event, vessels 8 had self-propagated and vessel 14 propagated into over 15 minutes prior to vessel 7 being propagated into. When vessel 7 exceeded the threshold in the propagation event logic, the propagation plan of overwriting vessels 15 or more minutes older allowed the fastgrowing yeast in vessel 7 to eradicate the yeast lineages in vessels 8, 14 and colonize their vessels with descendants of high-performing yeast from vessel 7. Competitive logic based on initialization times and seniority are enabled by asynchronous branched propagation - in synchronous propagation systems, all vessels are initialized at the same time - and yet propagation at thresholds corresponding to microbial abundance allow better standardization of initial population sizes upon propagation.5.3.3. Automated system illustrative embodiment

[0141] FIG. 10 depicts an illustrative embodiment of a breeding cabinet 1005 for performing automated branch passaging, in accordance with aspects of the present disclosure. The breeding cabinet 1005 includes a housing 1006, a viewing window 1003 and an access panel 1002. The breeding cabinet 1005 in this example is configured as a box, though other designs are also possible. It may also not be entirely enclosed, or may have an open top. It may have more than one access panel and viewing window.

[0142] The breeding cabinet 1005 includes a temperature controller 1001 for managing the temperature inside the cabinet. In some embodiments, the temperature may be transmitted to the user’s laptop or mobile device via a wireless or Bluetooth connection so the user may monitor and adjust the temperature remotely. The breeding cabinet 1005 includes a filtered vent 1004 for providing airflow to the cabinet 1005.

[0143] FIG. 11 depicts an illustrative embodiment of several components of a system for performing automated branch passaging, in accordance with aspects of the present disclosure. This figure shows the access panel 1002 opened. It may swing, slide, or fold open. Inside, the internal assembly 1101 is visible. In some embodiments, the viewing window is made of plastic, glass or quartz. In some embodiments, the viewing window is a screen fed by a camera. In some embodiments, the camera is directed towards the contents of the housing.

[0144] The assembly 1101 is mounted on a mounting plate 1102. In other embodiments, the assembly 1101 or one or more of its components are mounted on the bottom of the housing or on a side wall or ceiling of the housing. In some embodiments of the system, at least one component of the system is attached to a mounting plate, and the mounting plate is connected to the housing. In some embodiments, the mounting plate is connected to the housing base. In some embodiments, the mounting plate interacts with the vessel set, resource dispenser module, fluid handler module, and / or sensor module. In some embodiments, it further interacts with an aspirator module. In some embodiments of the system, the mounting plate is circular. In some embodiments, the mounting plate is rectangular, square, octagonal, hexagonal, etc.

[0145] FIG. 12 depicts an isometric illustrative embodiment of the internal assembly of a system for performing automated branch passaging, in accordance with aspects of the present disclosure. This figure again shows the mounting plate 1102 with components mounted on it. In the center is a rotating vessel set or vessel rack 1204. This may rotate via a manipulator that includes a motor to turn the vessel set.

[0146] There is a resource dispenser 1201 that is mounted to the mounting plate via a motorized linear stage as a manipulator module 1203. The resource dispenser 1201 can slide up and down on the stage or track to move the tip of the dispenser 1201 into and out of the vessels in the vessel set.

[0147] There is a fluid handler 1202 that is mounted to the mounting plate via a motorized linear stage as a manipulator module 1203. The fluid handler 1202 can slide up and down on the stage or track to move the tip of the fluid handler 1202 into and out of the vessels in the vessel set.

[0148] There is also a sensor module 1205 that is lined up adjacent the vessel set. The vessel set may be rotated to position each vessel in front of the sensor module 1205 to get a reading of the vessel from the sensor as an indicator measurement.

[0149] FIG. 13 depicts an illustrative embodiment of the internal assembly of a system for performing automated branch passaging, in accordance with aspects of the present disclosure. On the left is a side view and on the right is a top view. The side view shows the resource dispenser 1201 having tubes for external resource connections 1304. The resource dispenser 1201 has a dispensing tip 1301. It also shows the fluid handler 1202 having a dispensing and aspirating tip 1307. In addition, it shows the aspirator module 1302 having tubes 1305 for external aspiration connections and an aspirating tip 1308.

[0150] This view shows the sensor module 1205 lined up near one of the vessels in the vessel set. The vessels are test tubes or cuvettes 1302. The side view also shows a stepper motor as a rotational manipulator module 1306 to rotate the vessel set. Additionally, there is a rotary magnetic drive as an agitation mechanism 1303. This agitator sits under individual vessels to agitate the contents of the vessel. As the vessels are rotated around, different vessels can be positioned over the agitation mechanism 1303.

[0151] The top view at the right shows the motorized linear stages as manipulator modules 1203 that control each of the resource dispenser 1201, the fluid handler 1202, and the aspirator module 1302. The aspirator module 1302 may be mounted to the mounting plate 1102 via a motorized linear stage as a manipulator module 1203. The aspirator module 1302 can slide up and down on the stage or track to move the tip of the aspirator module 1302 into and out of the vessels in the vessel set.

[0152] In operation, the stepper motor as a manipulator 1306 may move the vessel set to position a given vessel 1302 under the resource dispenser 1201, which may be translated down to place the dispensing tip 1301 in the vessel to dispense sanitizing material from an external resourcepool through the tubes 1304 into the vessel to sanitize. The vessel may be moved to the aspirator module 1302 which may be translated down to place the aspirating tip 1308 in the vessel for aspiration of the sanitizing material through the tubes 1305 to an external waste area. Then the vessel may be moved back to the resource dispenser 1201 to receive growth media through a different tube 1304 into the vessel. The vessel may be moved to the fluid handler 1202, which may be translated down to inoculate the vessel through the tip 1307. The vessel may be moved to the agitator 1303 to be agitated. The vessel may also be moved to the sensor module 1205 for a measurement to be taken of its contents.

[0153] The processing circuit 140 or processor, may also intermittently or continually run code instructing the hardware to move vessels to the sensor and iteratively collect measurements. Within this code, the Propagation Event Logic (PEL) may be checked each time a measurement is taken to see if a propagation event should happen based on the measurement. If NO, the processor 140 continues iteratively collecting measurements. If YES, then the PEL may trigger a second PEL decision point to determine if the vessel is to be Propagated. If YES, a Propagation Plan (PP) is generated and implemented. If NO, the vessel may be killed / cleaned. FIG. 3.

[0154] The Propagation Plan can describe which vessels 114 are to be propagated and which are to be killed.

[0155] FIG. 14 depicts an illustrative embodiment of the rotational platform assembly with square cuvettes as part of a system for performing automated branch passaging, in accordance with aspects of the present disclosure. The vessels in this diagram are cuvettes 1302 on a rotational platform 1401. The stepper motor as a rotational manipulator 1305 is visible on the bottom of the platform 1401 in the diagram to the right.5.3.4. Environmental control feature descriptions

[0156] In some embodiments, the system may optionally include one or more features to control the environmental conditions within the breeding cabinet and within the vessels. Examples of environmental conditions that may be controlled and useful in performing automated branched passaging include: air and resource contamination, temperature within the breeding cabinet andof the vessels, atmosphere gas composition, and lighting conditions within the cabinet. Such environmental conditions may be varied over time or kept constant to facilitate the desired growth and evolution of microbes. The system may include one or more of the following features to control environmental conditions:

[0157] An enclosure, such as a breeding cabinet, to allow for control of the environment in which the vessels are and the method of automated branched passaging is performed.

[0158] A temperature controller with heating and / or cooling elements to maintain desired temperatures inside of the breeding cabinet and, therefore, the mixture within the vessels enables control over the temperature at which microbial growth occurs.

[0159] An air intake filter to allow for ventilation of the air inside of the cabinet without introducing contaminants from the outside environment.

[0160] An air handler with a recirculation filter that recirculates air within the breeding cabinet removes contaminants from the air that is in contact with the vessels, thereby reducing contamination in the vessels.

[0161] An atmosphere monitoring and control system to control the chemical composition of gas within container (e.g. remove oxygen to propagate obligate anaerobes, increase nitrogen for nitrogen fixers, etc.)

[0162] A cover on one or more vessels to allow for anaerobic environments, reduce contamination, and retain gasses created by microorganisms.

[0163] A port for administering a gas (e.g. ethylene oxide, hydrogen peroxide vapor) for sterilization purposes into the breeding cabinet to control for contamination.

[0164] One or more light sources (e.g. ultraviolet light) for sanitization and sterilization of the vessels, the inside of the cabinet, or other system components to reduce contamination risks.

[0165] A resource such as growth media intended to reduce contamination risk, allow proliferation (e.g. for autologous cells requiring media with growth factors in order toproliferate), and reduce the risk of contamination from unwanted taxa (e.g. antibacterial compounds in solution to avoid bacterial contamination when propagating yeast).

[0166] An agitator, examples of the agitator being a magnetic stirrer, an acoustic mixer, a vortexer, etc. In some embodiments, the agitator maintains uniform mixture with a vessel.5.3.5. Selection of cells for biomanufacturing methods.

[0167] The present invention is suited to automated asynchronous branched passaging of cell populations used to biomanufacturer chemicals, such as key pharmaceutical ingredients or end products, and proteins, such as monoclonal antibodies, computationally designed proteins, and more.

[0168] For example, cells such as Escherichia coli (E. coll), Saccharomyces cerevisiae (S'. cerevisiae), Chinese Hamster Ovary (CHO) cells, etc., may be monitored using indicators specific to the product of interest (e.g. chemiluminescent assays reacting with the desired product or indicating the desired enzymatic activity), or a product serving as a biomarker of cell productivity for the product of interest (e.g. fluorescent protein intensity as a biomarker for recombinant protein expression). The levels of such indicators can inform propagation event logic and propagation plans for asynchronous branched passaging allowing drift, diversification, and directional selection of product yields.

[0169] Additionally, biomanufacturing applications can select for cell protein expression levels or growth rates in target environmental conditions or resources, such as selection of growth in cold temperature growth for higher yield of proteins that would otherwise denature in high temperatures or selection of productivity in minimal media aimed to reduce contamination, increase yield, or otherwise improve biomanufacturing performance.5.3.6. Autologous cell therapies

[0170] Autologous cell lines, such as T-cells used for CAR-T cell therapies, natural killer cells, dendritic cells, B-cells, and others, have a variety of therapeutic applications. When developing autologous therapies, researchers often extract a patient’s immune cell, modify it with a targetedgenetic edit (e.g. add a chimeric antigen receptor to a T-cell), proliferate cells prior to increase the amount available for therapeutic purposes, and inoculate cells back into patients. The present invention enables the ability to automate the artificial selection with asynchronous branched propagation of autologous cells based on positive selection of desired therapeutic functions that boost efficacy and negative selection against unwanted properties that reduce the safety, improving the process of producing cell lines suitable for autologous therapies.

[0171] In these applications, cell lines capable of indefinite proliferation in media can be selected based on desired ecological functions, such as targeting specific cells (e.g. cancer cells) while not targeting off-target cells. Indicators can include fluorescent or chemiluminescent assays with unique fluorescent or spectrophotometric properties of the cell types being lysed or consumed, allowing propagation event logic and propagation plans to be based on the activity and specificity of autologous cell therapies. Resources, indicators, propagation event logic and propagation plans can change prior to infusions, such as growing cells first based on target activity and specificity followed by strong negative selection against growth in media lacking mitogens to select against cancerous, uncontrolled cell proliferation.5.3.7. Additional applications for phage

[0172] In addition to the example listed for asynchronous branched propagation of phage for lytic efficiency, other applications of the present invention may include long-term diversifying selection of curated phage cocktails. Co-evolutionary selections can be implemented by having two resources, one that contains naive bacteria that enable phage proliferation and one that is sterile growth media for the bacteria, allowing propagations into sterile growth media to select for bacteria resistant to phage and subsequent addition of naive bacteria to allow phage proliferation in the presence of resistant bacteria, selecting phage to overcome resistance.

[0173] Lysogenic phage can also be selected by indicators detecting bacterial lysis and propagation event logic and propagation plans based on reductions in bacterial population densities following the addition of inducing agents.5.3.8. Applications for algae

[0174] Algae can be genetically engineered to create a variety of useful carbon compounds, including biofuels capable of powering engines without non-renewable fossil fuels. However, the long-term productivity of algal colonies is limited by mutated variants that stop producing biofuels and invading species that eat, parasitize, or otherwise disturb algal physiological functions. The present invention can be used to select for more stable algal biomanufacturing systems, including multi-species mutualistic systems, by growing algal colonies, or algal colonies with possible symbionts, in vessels filled with resources required for algal proliferation, indicators of biofuel production, and propagation logic based on the persistence of biofuel production. To further select for colony stability in real- world conditions, vessels need not be in a sterile environment, but selection can occur in the open air, enabling the selection against algal colonies that see declining biofuel production following natural invasions, selection for algal colonies that receive invading mutualistic microbes, and otherwise selection of microbial communities and coalitions capable of maintaining the microbial ecosystem function of biofuel production.5.3.9. Cell-free systems

[0175] Cell free expression systems aim to replicate useful metabolic functions of cells in liquid media. For any cell-free system based on nucleic acids and containing all the necessary machinery for indefinite replication with mutations and heritable variation, the present invention can evolve cell-free systems for optimized performance using similar indicators for selection of biomanufacturing functions in microorganism-based systems.6. EXAMPLES6.1. Example 1: wild-type agent is capable of infecting an antimicrobial resistant pathogen.

[0176] Wild-type phage as an agent is capable of infecting an antimicrobial resistant pathogen, such as multi-drug resistant Staphylococcus aureus or Pseudomonas aeruginosa. In this example, Pseudomonas aeruginosa strain PAOland its lytic phage PB1 are being used.

[0177] Lyophilized strains of PA01 are resuscitated on fresh Lennox agar. Growth from a colony of interest is transferred into a lysogeny broth (LB) kept at 37 °C. The PA01 in LB are connected to a chemostat with supply lines for air and media. Oxygen is delivered to the vessel via pumpingfilter-sterilized air, and the culture mixed by bubbling with sterile air. Medium flow to the vessel is controlled with a peristaltic pump (e.g., Watson-Marlow 505U) and the working volume fixed by the height of the central overflow tube. Fresh medium is introduced at a constant flow rate and culture waste is removed at the same rate. Dilution times are modulated for desired generation times and / or organismal generation times are used to determine the dilution rate of the chemostat.

[0178] The chemostat is maintained in the resource pool. The system has a fluid handler capable of aliquoting standard volumes of bacterial culture from the chemostat to be aliquoted into any subset of wells in a 96-well plate. The system optionally has a second fluid handling system capable of extracting all of the contents in a well (minus any residue on the well walls) from any well on the plate, putting the extracted contents in a holding vessel, and aliquoting contents from the holding vessel in specific amounts to any well on the 96 well plate.

[0179] The system further has a sensor module containing a OD600 sensor used to monitor and estimate lytic activity of PB1. The sensor is utilized to monitor the optical density of light at 600nm of each well, such as a microplate reader which measures the transmitting light above wells and the sensor is located beneath wells to detect absorbance. Blank 96-well plates are measured to establish an OD600 or other spectrophotometric baselines.

[0180] A manipulator module capable of moving the plate between the sensor and fluid handler is built to be responsive to instructions from a computer to trigger the movement of plates from one system to another.

[0181] Spectrophotometric output from the OD600 system is input to a computer programmed to compute propagation event logic, compute propagation plans, send instructions back to the manipulator module, sensor, and fluid handler, allowing the computer to serve as the propagator capable of sensing indicators, trigger propagation events, and oversee the implementation of the propagation plans.

[0182] Once the aforementioned systems are established and the host population in chemostat reaches equilibrium, 240µL of PA01 host is aliquoted to each well in a 96-well plate. Next, to initialize the system, the PB1 agent (e.g. 10µL of a concentrated solution containing PB1) isadded to each well in the first column. After initialization, the plate is moved into the sensing system for monitoring optical density and utilizing the spectrophotometric output as input for the propagation plan.

[0183] The decay in OD600 across wells is used to define propagation event logic and propagation plans, using OD600 to estimate the rate of bacterial lysis and define the propagation function. Let %i(t) be the OD600 output from the spectrophotometric system for well i at time t. For asynchronous propagation, the time each vessel was initialized is recorded, setting ti 0as the initialization time of vessel i. Spectrophotometric readings will occur every 30 seconds, and the first 5 minutes of readings will be used to estimate an initial OD600 by estimating the function%i(t) = ajXi(t) + bt+ Ewith least squares, using the estimated line of best fit from the initial OD600 decay to estimate the OD600 at the time of initialization, xi 0■= Xi(ti 0).

[0184] The maturation state of a vessel will be defined as Mt•■= H{t-ti0>T}, or vessels are considered “immature” initially until a threshold time T is passed at which point a vessel is considered “mature”. The maturation state of vessels will be used to determine which vessels are eligible for passaging and which are eligible for killing.

[0185] After the first 5 minutes of observations, the fold-change of OD600 is estimated asXj(t)yi(t) =o '

[0186] Let y(t) E IR"1be the m-vcclor of OD600 fold changes across all m = 96 active vessels in the 96-well plate. Propagation event logic will be the indicator function I{min(y(t))<a}such ^at propagation events are triggered by a first-past-the-post rule where a vessel demonstrating a-fold reduction in OD600 triggers propagation irrespective of vessel maturation status. For example, if a = 0.9, then the instant OD600 reaches 90% the estimated initial value for any vessel, a propagation event is triggered at global time tp.

[0187] The propagation plan will first define the breeders: B = (i | i active, yj(tp) < a], or all active vessels with a-fold reduction in OD600 at propagation time tpare breeders.

[0188] To enable branched passaging, the rate of exponential decay of y£(t) is estimated for all vessels by estimating the rate of linear decay for log(yi(t)) prior to tp. The estimation of exponential decay can be done with a Gaussian process, a rolling generalized linear model, a gamma-family generalized linear model, and other methods, all of which yield an estimate of the rate of exponential decay and a standard deviation for the estimate, at, at time tp.

[0189] For every pair of vessels that are either mature or in the breeding set, a two-sample t-statistic will be computed_ fj-ftZiJ^^2

[0190] An additional set of vessels to terminate will be defined as K = {j | Zy >z*, for some i G B}. The definition of a killset, K, allows for branched propagation to be tunable by a fitness threshold parameter, z*. Since exponential decay rates, rj < 0, a vessel that has a very fast rate of decay will have a more negative rate of exponential decay, such that for any given reference vessel J we have that ztj grows with increasing rates of exponential decay in i. The definition of killsets based on threshold t-statistics will force vessels to be terminated and turned over to superior lineages if the rate at which they kill host bacteria is not fast enough compared to competitors.

[0191] If K is empty, then all breeders are serially passaged. If K is not empty, then any breeder in K is removed from the breeding set: it may have reached the a threshold, but it was too slow compared to its competitors. Once disjoint sets B and K are obtained, the relative fitness of breeder i is defined aswhere w > 0. The exponent, w, allows the relative fitness values to range from genetic drift where w = 0, forcing all relative fitness values of breeders to be equal, to larger positive values of w skewing relative fitness values more in favor of the dominant breeder.

[0192] Given k vessels in the killset K, one propagation plan for branched passaging can revolve around the functionNt= min {ceil(kA.t(w)''), 23}

[0193] where cei / (. ) is the ceiling function rounding its input up to the nearest integer. The function above ensures that the species with the highest relative fitness has the highest number of allocated propagations, rounded up to an integer less than or equal to the number of vessels in K. A minimum operator is used to take the minimum of ceil k j) and 23 as we will allocate 101L of solution from 250 / iL vessel contents, so setting a maximum of 23 aliquots ensures we don’t run out of breeder contents. Allocations can fill up the mapping from breeders to vessels in the K by starting with the highest-ranking breeder, i, allocating Ntvessels to be inoculated by that breeder, taking the next ranking breeder and allocating vessels accordingly until all vessels in K have been allocated.

[0194] Once a propagation plan has been computed, the computer sends instructions to initiate propagation. The manipulator module moves the 96-well plate from the sensing system to the liquid handling system. Starting with the highest-ranking breeder, their vessel is emptied and contents moved to the holding vessel. The contents of the vessels in K to be filled by the first breeder are emptied into waste. The liquid handling apparatus connected to the chemostat will aliquot 240 / zL of steady-state PA01 from chemostat to all empty vessels. Then, the liquid handling apparatus will aliquot 10 / zL of breeder solution containing successful phage into the recently filled vessels containing PA01. The holding vessel is emptied and cleaned, and the process is repeated for all breeders, resulting in turnover of vessels in B and K and preferential branched propagation of vessels with faster rates of exponential decay of hosts, a desired trait used as a proxy for phage virulence.

[0195] When propagation is done, the manipulator module moves the 96 well plate back to the sensing system to continue OD600 sensing.

[0196] This process is repeated indefinitely so long as users maintain the resource pools and appropriate materials such as pipette tips for the liquid handling systems, holding vessel cleaning reagents and space for waste from the chemostat and terminated vessels. The indefinite iterations yield a long-timescale evolutionary history of the desired trait quantifying the desired ecological activity of lytic ability estimated the rate of OD600 decay, and propagation plans determining allocation of agents across iterations. Secondary quantities such as heritability, measured as the correlation between desired trait r or other vessel properties across generations, is also retained and calculated. The propagation plan and propagation times tpare used to build an evolutionary tree of vessels. Historical trait values, relative fitness, and propagation histories are retained and visualized on a dashboard to allow users to monitor progress with quantitative evolutionary diagrams such as traitgrams, estimates of trait heritability over time, and more.

[0197] Once desired trait has not changed over several propagations, users may wish to terminate the protocol. The resulting phage are isolated, sequenced for further study, and cultivated as candidates to control host populations in therapeutic or ecological applications.6.2. Example 2: An example scenario of the system and method.

[0198] In this scenario, all vessels in the system are filled with mixtures and are actively cultivating. The system is continuously monitoring all vessels by iteratively capturing optical density measurements of the contents of the vessels and calculating fitness.

[0199] During this process, Vessel 4 reaches maturity. Maturity being based on a calculation such as fast growth rate threshold. Reaching maturity in this scenario is an exemplary trigger for a propagation event. At the time of Vessel 4 reaching maturity, Vessel 13 for example, is the lowest performing vessel as measured by fitness (in this case growth rate). As a result, Vessel 13 is marked to be killed.

[0200] In this scenario, the system is configured to perform branched propagations from one source vessel to two destination vessels. The propagation plan generated will propagate the contents of Vessel 4 into two vessels: Vessel 4 (restart in the original vessel)and Vessel 13 (the lowest performing vessel).

[0201] In this scenario, note that there are two reservoirs within the Resource Module holding reagents, one with ‘Sanitizer’ and another with a culture media ‘Resource’ (e.g. LB Broth) for the microbes to cultivate in. Both are dispensed by the RESOURCE DISPENSER. The system will then execute this propagation plan with the following steps:6.2.1. High-level steps1) Vessel 13 will be prepared for inoculation by aspirating it, sanitizing it, aspirating the sanitizer, and finally filling it with a resource (e.g. LB Broth).2) A sample from Vessel 4 will be acquired by the fluid handler.3) Vessel 13 will be inoculated with the sample in the fluid handler.4) A second sample from Vessel 4 will be acquired by the fluid handler.5) Vessel 4 will be prepared for inoculation by aspirating it, sanitizing it, aspirating the sanitizer, and filling it with a resource (e.g. LB Broth).6) Vessel 4 will be inoculated with the sample in the fluid handler.7) Normal vessel monitoring and operating proceeds.6.2.2. Low-level stepsProcess for Preparing a Tarset Vessel (Vessel 13) for inoculation (from Vessel 4):1) The ROTATIONAL MANIPULATOR will rotate the ROTATING VESSEL RACK so that Vessel 13 is aligned with the ASPIRATOR.2) The MOTORIZED LINEAR STAGE on the ASPIRATOR will lower the ASPIRATOR into Vessel 13 and the ASPIRATOR will vacate the vessel contents (cultivating mixture), then be raised up out of the vessel by the MOTORIZED LINEAR STAGE.3) The ROTATIONAL MANIPULATOR will rotate the ROTATING VESSEL RACK so that Vessel 13 is aligned with the RESOURCE DISPENSER.4) The MOTORIZED LINEAR STAGE on the RESOURCE DISPENSER will lower the RESOURCE DISPENSER into Vessel 13 just above the anticipated sanitizer level.5) The RESOURCE DISPENSER will dispense SANITIZER into Vessel 13 and be raised up out of the vessel by the MOTORIZED LINEAR STAGE.6) The system will then wait for a predetermined period to allow the sanitizer to effectively sanitize Vessel 13.7) The ROTATIONAL MANIPULATOR will rotate the ROTATING VESSEL RACK so that Vessel 13 is aligned with the ASPIRATOR.8) The MOTORIZED LINEAR STAGE on the ASPIRATOR will lower the ASPIRATOR into Vessel 13 and the ASPIRATOR will vacate the vessel contents (sanitizer), then be raised up out of the vessel by the MOTORIZED LINEAR STAGE.9) The ROTATIONAL MANIPULATOR will rotate the ROTATING VESSEL RACK so that Vessel 13 is aligned with the RESOURCE DISPENSER.10) The MOTORIZED LINEAR STAGE on the RESOURCE DISPENSER will lower the RESOURCE DISPENSER into Vessel 13 just above the anticipated resource level.11) The RESOURCE DISPENSER will dispense RESOURCE (e.g. LB Broth) into Vessel 13 and be raised up out of the vessel by the MOTORIZED LINEAR STAGE, thereby preparing Vessel 13 for inoculation.Process for acquiring a sample from a Vessel (Vessel 4) and inoculating a Prepared Target Vessel (Vessel 13):12) The ROTATIONAL MANIPULATOR will rotate the ROTATING VESSEL RACK so that Vessel 4 is aligned with the AGITATION MECHANISM to uniformly stir the contents for a predetermined period.13) The ROTATIONAL MANIPULATOR will rotate the ROTATING VESSEL RACK so that Vessel 4 is aligned with the FLUID HANDLER.14) The MOTORIZED LINEAR STAGE on the FLUID HANDLER will lower the FLUID HANDLER into Vessel 4 and the FLUID HANDLER will acquire and retain an aliquot of the vessel contents, then be raised back up out of the vessel by the MOTORIZED LINEAR STAGE.15) The ROTATIONAL MANIPULATOR will rotate the ROTATING VESSEL RACK so that Prepared Target Vessel 13 is aligned with the FLUID HANDLER.16) The FLUID HANDLER will dispense its contents (i.e. an aliquot from Vessel 4) into Prepared Target Vessel 13, thereby inoculating it.Process for performing a propagation from a Vessel (Vessel 4) into itself (Vessel 4)16) Repeat Steps 12-14 with Vessel 4 to acquire an aliquot of Vessel 4 and retain it in the FLUID HANDLER.17) Repeat Steps 1 - 11 with Vessel 4 as the Target Vessel, preparing it for inoculation. 18) Repeat Steps 15 - 16 with Vessel 4 as the Target Vessel, thereby inoculating it.7. EQUIVALENTS AND INCORPORATION BY REFERENCE

[0202] While the invention has been particularly shown and described with reference to a preferred embodiment and various alternate embodiments, it will be understood by persons skilled in the relevant art that various changes in form and details can be made therein without departing from the spirit and scope of the invention.

[0203] All references cited herein are incorporated by reference to the same extent as if each individual publication, database entry, patent application, or patent, was specifically and individually indicated incorporated by reference in its entirety, for all purposes. This statement of incorporation by reference is intended by Applicants, pursuant to 37 C. F. R. § 1.57(b)(1), to relate to each and every individual publication, database, patent application, or patent, each of which is clearly identified in compliance with 37 C. F. R. § 1.57(b)(2), even if such citation is not immediately adjacent to a dedicated statement of incorporation by reference. The inclusion of dedicated statements of incorporation by reference, if any, within the specification does not in any way weaken this general statement of incorporation by reference. Citation of the references herein is not intended as an admission that the reference is pertinent prior art, nor does it constitute any admission as to the contents or date of these publications or documents.

[0204] US provisional application no. 63 / 779,531, filed March 28, 2025, is hereby incorporated by reference in its entirety for all purposes.

Claims

WHAT IS CLAIMED IS:

1. An automated system for preparing and / or cultivating microbiological mixture, comprising:i. a housing;ii. a vessel set containing a plurality of vessels arranged in a format within the housing and configured to hold the microbiological mixtures;iii. at least one resource dispenser configured for delivering reagents and / or mixtures; iv. a fluid handler configured to be adjacent to the vessels to transfer reagents and / or mixtures into and out of the vessels;v. at least one sensor module configured to be adjacent to the vessels to acquire indicator measurements from the microbiological mixtures in the vessels; vi. at least one manipulator module moveably coupled to and configured to move one or more of the vessel set, the resource dispenser, the fluid handler, and the sensor module;vii. a processor in communication with the resource dispenser, the fluid handler, the sensor module and the manipulator module, the processor instructing the sensor to iteratively collect measurements against which propagation event logic is applied; andviii. a computer-readable storage medium storing instructions that, when executed by the processor, cause the processor to execute a propagation plan for automated artificial selection and to control operation of the resource dispenser, the fluid handler, the sensor module and the manipulator module based on sensor feedback and the propagation plan to inoculate vessels within the vessel set with the microbiological mixtures and log inoculations over time.

2. The system of claim 1, wherein the system is further configured for monitoring microbial mixtures.

3. The system of claim 1 or claim 2, wherein the stored instructions further comprise instructions to:iv. control the manipulator module and fluid handler to initiate automated artificial selection of microbes from the microbiological mixtures by inoculating the vessels with the microbiological mixtures,v. control the manipulator module and sensor module to measure and recursively log the indicator measurements and times for each vessel as propagation histories until a propagation event is triggered or until the system is terminated, andvi. provide the logged indicator measurements and the propagation histories for generation of the propagation plan.

4. The system of any one of claims 1 to 3, wherein the stored instructions further comprise instructions to acquire the indicator measurement(s) by the sensor module across a set of timepoints.

5. The system of any one of claims 1 to 4, wherein the sensor module comprises a sensor for acquiring at least one of the following indicator measurements: optical density (OD), turbidity, fluorescence, pH, temperature, pressure, dissolved oxygen (DO), carbon dioxide, ion concentration, substrate concentration, metabolite concentration, agitation or mixing speed, gas flow, changes in RNA and / or DNA sequences or sequencing, mRNA abundance, protein abundance, and cell viability.

6. The system of any one of claims 1 to 5, wherein the sensor of the sensor module further comprises any one of the following: pH meter, thermometer, spectrometer, spectrophotometer, magnetometer, and voltameter.

7. The system of any one of claims 1 to 6, wherein the sensor module is positioned such that each of the vessels can be moved to the sensor to take the indicator measurement.

8. The system of any one of claims 1 to 7, wherein the sensor of the sensor module is an optical density sensor.

9. The system of any of claims 1 to 8, wherein the resource dispenser further comprises a dispensing tip and at least one tube for external resource connections.

10. The system of claim 9, wherein the external resource connections are connected to a resource pool configured for storing reagents and / or mixtures.

11. The system of claim 10, wherein the resource pool further comprises at least one resource reservoir, optionally wherein the resource pool further comprises a chemostat.

12. The system of claim 9 or 10, wherein the contents of the resource pool are delivered to the resource module for dispensing out of the dispensing tip via the external resourceconnections.

13. The system of any one of claims 1 to 12, wherein the resource dispenser is motorized for moving the dispensing tip into and out of the vessel.

14. The system of any one of claims 1 to 13, wherein the vessel set further comprises a microtiter plate.

15. The system of any of claims 1 to 16, wherein the vessel set further comprises a rotational platform having circularly arranged cuvettes or test tubes around a periphery of the platform.

16. The system of claim 15, wherein at least one manipulator is a rotary manipulator for rotating the vessel set.

17. The system of claim 16, wherein the rotary manipulator is a motor.

18. The system of claim 17, wherein the motor is a stepper motor.

19. The system of any of claims 1 to 18, wherein the manipulator comprises a motorized arm for moving the vessel set or an individual one of the vessels to different points on a mounting plate or to different components of the system.

20. The system of any of claims 1 to 19, further comprising an agitation mechanism for agitating the vessels.

21. The system of claim 20, wherein the agitation mechanism is a rotary magnetic drive positioned to allow agitation of individual vessels.

22. The system of any one of claims 1 to 21, further comprising an aspirator module having an aspirating tip and at least one tube for external aspiration connection.

23. The system of any one of claims 1 to 22, wherein the aspirator module is motorized for moving the aspirating tip into and out of the vessel.

24. The system of any of claims 1 to 23, wherein the fluid handler comprises a dispensing tip.

25. The system of any of claims 1 to 24, wherein the fluid handler is motorized for moving the dispensing tip into and out of the vessel.

26. The system of any one of claims 1 to 25, wherein the processor having applied propagation event logic based on measurements received from the sensor, performs a second propagation event logic to determine if a vessel is to be propagated.

27. The system of any one of claims 1 to 26, wherein the instructions further comprise instructions to compute at least one of the following measurements using the data produced by the sensor module: growth rate, doubling time, oxy gen-uptake and carbon dioxide evolution rates (OUR / CER), and yield coefficients.

28. The system of any one of claims 1 to 27, wherein the instructions further comprise instructions to use the indicator measurement acquired by the sensor module to determine an indicator trajectory.

29. The system of claim 28, wherein the indicator trajectory is assessed for at least one vessel.

30. The system of claim 29, wherein propagation plan further comprises an algorithm configured to convert indicator trajectories into system control instructions.

31. The system of any one of claims 1 to 30, wherein the instructions further comprise instructions to execute the propagation plan following an initial set of sensor measurements.

32. The system of any one of claims 1 to 31, wherein the instructions further comprise instructions to dynamically revise one or more operations of the fluid handler, the manipulation module, the sensor module, the aspirator module, and / or the resource dispenser in real time based on the indicator measurement obtained by the sensor module, thereby implementing autonomous feedback control of mixture cultivation.

33. The system of any one of claims 1 to 32, wherein the instructions further comprise instructions for:i. storing logs of indicator measurements and times the indicator measurements were taken;ii. utilizing the logs to perform iterative propagation event logic and / or propagation plan computation;iii. convert the performed propagation event logic and / or propagation plan computations into propagation plans for execution by said processor.

34. The system of any one of claims 1 to 33, wherein the housing further comprises a mounting plate onto which one or more of the vessel set, the resource dispenser, the fluid handler, and the sensor module are directly or indirectly mounted.

35. The system of any one of claims 1 to 34, wherein the housing further comprises a base, a top, and at least one side wall to act as an enclosure for the system.

36. The system of any one of claims 1 to 35, wherein the housing further comprises an access panel with a viewing window.

37. The system of any one of claims 1 to 36, wherein the system further comprises an air recirculation system interacting with the housing.

38. The system of claim 37, wherein the recirculating system further comprises a temperature controller and a filtered vent.

39. The system of any one of claims 1 to 38, wherein the system is capable of automating artificial selection of microorganisms according to predetermined traits.

40. The system of any one of claims 1 to 39, further comprising a graphical user interface (GUI) that is capable of enabling a user to specify propagation event logic parameters, propagation plan parameters, and procedure termination conditions.

41. A method of automatically preparing and / or cultivating a microbiological mixture, the method comprising:i. generating a propagation plan for the microbiological mixture in an automated system; andii. executing the propagation plan by:dispensing from a fluid handler the microbiological mixture into vessels in a vessel set to inoculate the vessels to initiate automated artificial selection;applying a sensor module to take indicator measurements of the vessels representing a condition within the vessels;recursively logging the indicator measurements and times for the vessels until a propagation event is triggered or the method is terminated; andadjusting one or more subsequent manipulations of components of the automated system in accordance with the indicator measurements received from the sensor module.

42. The method of claim 41, wherein the method measures microbial phenotypes.

43. The method of claim 42, wherein the method propagates microbes with the corresponding genotype, methylation patterns, or other characteristics underlying the microbe’ heritable variation..

44. The method of claim 43, wherein the method sustains selective pressure over timescales long enough to allow the evolution of a desired trait.

45. The method of any one of claims 41 to 44, wherein the method automates the artificial selection of microbes.

46. The method of any one of claims 41 to 45, further comprising storing the indicator measurements taken by the sensor module to make them available for propagation-event logic and propagation-plan computation.

47. The method of any one of claims 41 to 46, further comprising operating a propagation-event logic algorithm to convert the indicator measurements and propagation histories into a propagation plan.

48. The method of any one of claims 41 to 47, further comprising storing and operating a propagation-plan algorithm to activate upon a propagation event and convert indicator measurements and propagation histories into a plan to terminate preparation and / or cultivation in a subset of vessels in the vessel set.

49. The method of claim 48, further comprising re-using contents of vessels used for breeding microbes in designated aliquots to initialize new reactions in recently emptied vessels.

50. The method of any one of claims 40 to 49, wherein the method further comprises:i. initializing a first iteration of the artificial selection;ii. logging a first set of indicator measurements and times;iii. logging a second set of indicator measurements and times;iv. computing propagation-event logic based on the logged first and second set of indicator measurements and times;v. upon triggering of a propagation event, computing a first propagation plan based on the logged first and second set of indicator measurements and times; vi. implementing the propagation plan;vii. logging further indicator measurements and times;viii. using logged information to inform downstream propagation-event logic and propagation plans; andix. repeating the above steps until a goal has been reached.

51. The method of any one of claims 40 to 50, further comprising cleaning emptied vessels prior to inoculation of mixtures according to the propagation plan.

52. The method of any one of claims 40 to 51, further comprising:i. generating propagation-event logic and a propagation plan after n prior propagation events based on all or any portion of prior logging of indicator measurements; andii. proceeding to populate / initialize, sense, and propagate descendant microbiological mixtures in a recursive fashion, appending iteration logs and propagation logs until a procedural termination condition is reached.

53. The method of any one of claims 40 to 52, wherein the method is capable of automating artificial selection of microorganisms.

54. The method of any one of claims 40 to 53, further comprising aspirating from one or more of the vessels in the vessel set the microbiological mixture, reagents or a sanitizing fluid.

55. The method of any one of claims 40 to 54, further comprising rotating a rotary platform to move the vessel set to position the vessels at different locations to be accessed for fluid dispensing or fluid aspiration, or for sensing.

56. The method of any one of claims 40 to 55, further comprising aspirating with the fluid handler all or a portion of the microbiological mixture from one of the vessels in the vessel set and inoculating another one of the vessels in the vessel set containing culture media.

57. The method of any one of claims 40 to 56, further comprising, prior to dispensing the microbiological mixture into vessels in the vessel set:i. dispensing a sanitizing fluid into one or more of the vessels in the vessel set; andii. aspirating the dispensed sanitizing fluid.

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