Microbial fermentation method
Through multiomic analysis and the construction of dynamic conservation equations, and combining the fermentation control model to make mechanism control decisions, the problems of mechanism quantification and dynamic adjustment in the microbial fermentation environment are solved, and high-precision and stable fermentation control are achieved.
Patent Information
- Application Number
- CN202411904366.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-12-23
- Publication Date
- 2025-02-11
AI Technical Summary
The prior art is difficult to quantify the fermentation mechanism and dynamic adjustment control in complex microbial fermentation environments, resulting in the difficulty of achieving accurate adjustment of the fermentation process and real-time feedback.
The fermentation mechanism was determined through multiomic analysis (genome, transcriptome, proteomic group, metabolic group), and dynamic conservation equations were constructed based on these mechanisms, stability thresholds were set for quantitative analysis, and fermentation control model was generated, and mechanism control decisions and directions were performed. Adjust decisions, determine fermentation control strategies and transmit them to the control center of the directional microecological bacterial machine for control.
It significantly improves the control accuracy and stability of the microbial fermentation process, and achieves comprehensive, precise and intelligent control of the fermentation process.
Smart Images
Figure CN119410845A_ABST
Abstract
Description
Technical Field
[0001] The invention relates to the technical field of microbial fermentation, and in particular to a microbial fermentation method. Background Art
[0002] At present, microbial fermentation is an important technology widely used in biopharmaceuticals, food processing, environmental governance and other fields. Its control accuracy and efficiency directly affect the economic benefits and product quality of the production process.
[0003] Traditional microbial fermentation control methods mainly rely on experience and conventional physical and chemical parameter monitoring, which cannot fully capture the microscopic mechanisms of microorganisms at different growth stages and make it difficult to achieve accurate dynamic optimization. In the existing technology, although some fermentation control systems have begun to introduce omics analysis methods, most systems only focus on a single omics data and lack the deep integration and dynamic quantification of multi-level data.
[0004] In addition, microbial fermentation control is a variable and complex process. How to quantify the fermentation mechanism and dynamically adjust the control in a complex microbial fermentation environment to achieve precise regulation of the fermentation process and real-time feedback is still a technical problem that needs to be solved urgently. Summary of the invention
[0005] The present application provides a microbial fermentation method, which is used to solve the technical problems existing in the prior art on how to quantify the fermentation mechanism and dynamically adjust the control in a complex microbial fermentation environment, and realize precise regulation of the fermentation process and real-time feedback.
[0006] In view of the above problems, the present application provides a microbial fermentation method.
[0007] The present application provides a microbial fermentation method, the method comprising: determining the fermentation mechanism for a target microorganism by performing multi-omics analysis, wherein the multi-omics analysis at least includes genome-transcriptome-proteome-metabolome, and the fermentation mechanism includes a single mechanism and a clustering mechanism; based on the fermentation mechanism, quantitatively analyzing the mechanism elements to determine the dynamic conservation equation of the target microbial fermentation, wherein by setting a stability threshold, the stable mechanism elements are absolutely quantified, and the unstable mechanism elements are fuzzy quantified; based on the fermentation mechanism and the dynamic conservation equation, dynamic behavior fermentation control decision modeling is performed to generate a fermentation control model, wherein the fermentation control model includes a standardized block and a directional regulation block; determining a directional fermentation task, combining the fermentation control model, making a mechanism control decision and a directional regulation decision, and determining a fermentation control strategy; transmitting the fermentation control strategy to the control center of a directional microecological culture machine to perform fermentation control of the target microorganism.
[0008] One or more technical solutions provided in this application have at least the following technical effects or advantages:
[0009] A microbial fermentation method provided in an embodiment of the present application determines the fermentation mechanism of the target microorganism by performing multi-omics analysis, and based on the fermentation mechanism, quantitatively analyzes the mechanism elements to determine the dynamic conservation equation of the target microbial fermentation, and by setting a stability threshold, absolutely quantifies the stable mechanism elements and fuzzily quantifies the unstable mechanism elements; based on the fermentation mechanism and the dynamic conservation equation, performs fermentation control decision modeling of dynamic behavior, generates a fermentation control model, determines the directional fermentation task, combines the fermentation control model, performs mechanism control decision and directional adjustment decision, determines the fermentation control strategy and transmits it to the control center of the directional microecological culture machine, and performs fermentation control of the target microorganism, which is used to solve the technical problems existing in the prior art of how to quantify the fermentation mechanism and dynamically adjust the control in a complex microbial fermentation environment, realize accurate adjustment of the fermentation process and real-time feedback, significantly improves the control accuracy and stability of the microbial fermentation process, and realizes comprehensive, accurate and intelligent control of the fermentation process. BRIEF DESCRIPTION OF THE DRAWINGS
[0010] Figure 1 A schematic diagram of a microbial fermentation process is provided for this application;
[0011] Figure 2 A schematic diagram of a process for determining a dynamic conservation equation in a microbial fermentation method is provided for this application;
[0012] Figure 3 A schematic flow chart of directional control optimization decision-making in a microbial fermentation method is provided for this application. DETAILED DESCRIPTION
[0013] The present application provides a microbial fermentation method, determines the fermentation mechanism by conducting multi-omics analysis, and quantifies the mechanism elements to determine the dynamic conservation equation of fermentation. The fermentation control decision modeling of dynamic behavior is carried out in combination with the fermentation mechanism to generate a fermentation control model, make mechanism control decisions and directional adjustment decisions for directional fermentation tasks, determine the fermentation control strategy and transmit it to the control center of a directional microecological culture machine for fermentation control, so as to solve the technical problems existing in the prior art of how to quantify the fermentation mechanism and dynamically adjust the control in a complex microbial fermentation environment, and realize precise adjustment of the fermentation process and real-time feedback.
[0014] Example: Figure 1 As shown, the present application provides a microbial fermentation method, the method comprising:
[0015] S1: For the target microorganism, the fermentation mechanism is determined by performing multi-omics analysis, wherein the multi-omics analysis at least includes genome-transcriptome-proteome-metabolome, and the fermentation mechanism includes single mechanism and clustered mechanism.
[0016] In the application examples, the fermentation process of the target microorganism is analyzed by multi-omics to determine the fermentation mechanism. Specifically, the multi-omics analysis includes at least data from multiple omics levels such as genome, transcriptome, proteome and metabolome, which reveals the complex biological processes involved in the fermentation process of the microorganism and provides comprehensive information.
[0017] Among them, genomic analysis involves sequencing and analysis of the target microbial genomic DNA, aiming to reveal the genetic information and potential metabolic capacity of the microorganism. Transcriptome analysis evaluates the dynamic changes of gene expression by measuring RNA, helping to understand how the metabolic activities of microbial cells are regulated by gene expression at different fermentation stages. Proteome analysis further reveals the functions and metabolic pathways of proteins by identifying and quantifying proteins in target microbial cells, thereby providing important functional information for the fermentation mechanism of the target microorganism. Metabolome analysis measures the metabolites of microorganisms during the fermentation process and evaluates changes in their metabolic pathways and key metabolites, thereby helping to further understand the metabolic behavior of microorganisms.
[0018] Through comprehensive analysis of the above four omics levels, the fermentation mechanism of the target microorganism can be fully revealed, that is, how the microorganisms regulate their own physiological and metabolic activities during the fermentation process to achieve a specific fermentation effect.
[0019] In the examples of the present application, the fermentation mechanism is classified into two categories: single mechanism and clustered mechanism. A single mechanism refers to a mechanism that is revealed by a single group of omics data and is directly related to a specific metabolic pathway or physiological process. For example, the expression level of a key gene in fermentation is determined by transcriptome analysis, and then its effect on the fermentation process is inferred. The clustered mechanism refers to a complex mechanism that reveals the interconnected and synergistic effects of multiple physiological or metabolic pathways of microorganisms in the fermentation process through the fusion analysis of multi-omics data. For example, through the joint analysis of the genome and the metabolome, it is found that the metabolic regulation mechanism of microorganisms in a specific environment is the result of the joint action of multiple metabolic pathways.
[0020] Through the deep integration of multi-omics data, the physiological and metabolic states of microorganisms can be accurately captured, revealing their intrinsic mechanisms in the fermentation process, and providing a theoretical basis for subsequent fermentation control and optimization.
[0021] Furthermore, the fermentation mechanism is determined by performing multi-omics analysis, and step S1 of the present application includes:
[0022] The fermentation records of the target microorganism are called to conduct independent single-omics analysis to explore the omics mechanism of the target microorganism; for a fermentation cycle, the omics mechanism is subjected to multi-omics homology mapping and fused to determine the single mechanism; based on the intra-cluster mutual influence, the single mechanism is adjusted to determine the clustering mechanism; the single mechanism and the clustering mechanism are used as the fermentation mechanism.
[0023] Specifically, fermentation records, that is, the omics data accumulated in historical fermentation data, are used to explore the omics mechanisms of target microorganisms under different fermentation conditions. Single-omics independent analysis refers to the analysis of data at one omics level, such as the genome, transcriptome, proteome, or metabolome, to identify key biological information related to the fermentation process. For example, through genomic data analysis, metabolic genes and genetic regulatory networks in microorganisms can be identified; through transcriptome data analysis, it is possible to understand which genes have changed in expression at different fermentation stages, and then infer their role in the fermentation process.
[0024] Next, for a complete fermentation cycle, the above-mined single-omics mechanism is subjected to multi-omics homology mapping and fusion. Specifically, multi-omics homology mapping refers to matching and comparing the relationships between different omics data. For example, genes identified at the genomic level may be verified through changes in expression levels of transcriptome data, and further revealed through proteome and metabolome data analysis to reveal their specific role in metabolic pathways. Through this multi-omics homology mapping, the relationships between the various omics can be integrated to determine a comprehensive single mechanism. The single mechanism is based on the integration of the analysis results of a single omics data with other omics data, revealing a single, clear physiological or metabolic mechanism of the target microorganism during the fermentation process.
[0025] The intra-cluster interactions refer to the mutual influence and synergy between multi-omics data. For example, a specific gene may be highly expressed in the transcriptome, and the protein encoded by the gene may be closely related to the changes in multiple metabolites, forming an interdependent network. By analyzing and adjusting these interrelated mechanisms, a complex clustering mechanism can be identified, which reveals how multiple metabolic pathways or physiological processes interact and synergistically regulate during fermentation.
[0026] The single mechanism and clustering mechanism are used as the overall fermentation mechanism of the target microorganism. By integrating the accuracy of the single mechanism and the complexity of the clustering mechanism, a comprehensive mechanism information that conforms to the actual fermentation process is obtained, which not only reveals the independent functions of the microorganism such as gene expression and metabolic pathways, but also shows the synergy and dynamic changes between different biological levels, providing a scientific basis for subsequent fermentation control and optimization.
[0027] S2: Based on the fermentation mechanism, the mechanism elements are quantitatively analyzed to determine the dynamic conservation equation of the target microbial fermentation, wherein the stable mechanism elements are absolutely quantified and the unstable mechanism elements are fuzzily quantified by setting a stability threshold.
[0028] In the examples of the present application, the dynamic conservation equation is constructed by quantitatively describing the changing rules of various mechanism elements in the microbial fermentation process, thereby realizing accurate prediction and control of the fermentation process. The mechanism elements refer to various factors that affect key processes such as microbial growth, metabolism and product synthesis during the fermentation process, such as protein levels, metabolite concentrations, etc.
[0029] By setting a stability threshold, the mechanism elements are divided into stable mechanism elements and unstable mechanism elements. The stable mechanism elements refer to the key factors that always maintain a stable state during the fermentation process, such as the cell growth rate of microorganisms, the consumption rate of substrates, the concentration of certain metabolites, etc. Then, their values in the fermentation process are accurately calculated by absolute quantitative methods. For example, in the quantification of stable mechanism elements, the cell growth rate can be combined with the model formula through experimental data to obtain an accurate value range, thereby providing a quantitative control indicator for the fermentation process.
[0030] For unstable mechanism factors, fuzzy quantification is used for processing. The unstable mechanism factors generally refer to those factors that show large fluctuations or uncertainties during the fermentation process, such as the activity of certain enzymes, the concentration of metabolic intermediates, etc. The changes in these factors may be affected by many factors, and their changing patterns are relatively complex. Therefore, fuzzy quantification methods are used to process them. Fuzzy quantification refers to representing the range of changes of these unstable mechanism factors with fuzzy sets to obtain a quantitative value of uncertainty. For example, for the fluctuation of metabolite concentration, its possible concentration range can be represented by fuzzy sets.
[0031] Based on the above-mentioned absolutely quantified stable mechanism elements and fuzzily quantified unstable mechanism elements, the dynamic conservation equation of the target microbial fermentation process was constructed. That is, by quantifying the changing relationship of all key mechanism elements, a mathematical model describing the entire process of microbial fermentation was formed. This model can monitor the state changes of each mechanism element in real time during the fermentation process, thereby providing precise theoretical support for the dynamic adjustment of the fermentation process.
[0032] Further, such as Figure 2 As shown, the dynamic conservation equation of the target microbial fermentation is determined, and step S2 of the present application includes:
[0033] A fermentation reference baseline is set, and a point linear relationship is determined for the stable mechanism elements, wherein the fermentation reference baseline is a reference period determined by bacterial growth-substrate consumption-fermentation acid production; for the unstable mechanism elements, an interval linear relationship is determined with amplitude as a constraint; the point linear relationship and the interval linear relationship are integrated to determine the dynamic conservation equation covering all mechanism elements.
[0034] Specifically, the fermentation reference baseline refers to a typical cycle of a microbial fermentation process under standard fermentation conditions. In the embodiments of the present application, it is defined by major physiological processes such as bacterial growth, substrate consumption, and fermentation acid production.
[0035] On the basis of the fermentation reference baseline, the point linear relationship is further determined for the stable mechanism elements. The point linear relationship refers to the linear relationship between these stable mechanism elements and time or other variables within a specific fermentation cycle. For example, there is usually a certain linear correlation between the bacterial growth rate and substrate consumption.
[0036] On the other hand, for unstable mechanism elements, their changing rules are more complicated, often affected by multiple factors, and show greater volatility. For this reason, the present invention determines the interval linear relationship of unstable mechanism elements by setting the amplitude as a constraint. Amplitude constraint refers to taking into account its fluctuation range in the quantification process of unstable mechanism elements, and by setting a certain amplitude limit, ensuring that the changes of these unstable elements will not exceed the preset range. For example, the concentration of metabolic intermediates may fluctuate periodically. By setting the amplitude, its variation range can be limited to a certain interval, and on this basis, a linear relationship between it and time or other variables is established. The interval linear relationship reflects the changing rules of unstable mechanism elements within the fluctuation range, providing a basis for dynamic adjustment in the fermentation process.
[0037] Among them, the point linear relationship describes the law of stable mechanism elements, while the interval linear relationship accurately defines the fluctuation range of unstable mechanism elements. By integrating these two linear relationships, it is possible to achieve all-round dynamic monitoring and control of the target microbial fermentation process. The dynamic conservation equation can not only describe the relationship between various mechanism elements in the fermentation process, but also adjust the control strategy according to real-time data during the fermentation process to ensure the stability and efficiency of the fermentation process.
[0038] S3: Based on the fermentation mechanism and the dynamic conservation equation, dynamic behavior fermentation control decision modeling is performed to generate a fermentation control model, wherein the fermentation control model includes a standardized block and a directional adjustment block.
[0039] In the embodiments of the present application, a set of control strategies that can respond to changes in the fermentation state in real time is obtained by constructing a fermentation control model and combining the quantitative analysis of each mechanism element in the dynamic conservation equation.
[0040] First, the fermentation control model is divided into two main parts: the standardized block and the directional adjustment block, which are used to deal with different control requirements in the fermentation process in a targeted manner, so as to achieve efficient control of the complex fermentation process. The standardized block refers to the part of the model responsible for handling routine and standardized control tasks, mainly targeting stable and regular behaviors in the fermentation process, such as bacterial growth and substrate consumption. In the standardized block, the control strategy adopted is usually based on known laws and experience, such as the linear relationship between bacterial growth and substrate consumption, and the fermentation process is kept normal by adjusting parameters such as dissolved oxygen and temperature.
[0041] The directional regulation block is responsible for handling dynamic adjustment and optimization tasks during the fermentation process, especially for those more complex and variable factors (such as the generation of metabolites, response to environmental changes, etc.). The directional regulation block mainly ensures that the fermentation process can maintain efficient operation under the influence of uncertain factors through real-time monitoring and regulation of unstable mechanism elements. The control strategy in this block is often based on real-time feedback and fuzzy logic reasoning of multi-omics data, and optimizes the yield and quality of the product by dynamically adjusting the fermentation conditions. For example, in a metabolic pathway, when the concentration of a metabolite exceeds the expected range, the directional regulation block will adjust the substrate feed or dissolved oxygen content to promote the synthesis of the target product.
[0042] Exemplarily, the sampling sample training is constructed in such a way that the output of the standardization block is used as the input of the directional adjustment block, and the fermentation task is combined with the input to perform supervised training until convergence construction. Specifically, sample fermentation data is obtained, including sample fermentation task-sample fermentation strategy, and the convergence degree is evaluated based on the consistency between the output fermentation strategy and the sample fermentation strategy, and the fermentation control model is trained and generated.
[0043] Through the division of labor between the standardized block and the directional adjustment block, the fermentation control model can flexibly adjust the control strategy according to different fermentation stages and goals. In practical applications, the standardized block provides a stable basic support for fermentation, while the directional adjustment block can optimize the control strategy in real time when facing environmental changes or uncertain factors, ensuring the efficiency and stability of the fermentation process.
[0044] S4: Determine the directional fermentation task, combine the fermentation control model, make mechanism control decisions and directional regulation decisions, and determine the fermentation control strategy.
[0045] The directed fermentation task refers to the fermentation process goal that needs to be adjusted and optimized under specific fermentation conditions according to the needs of the target product or production process. For example, it focuses on optimizing the growth rate of the bacteria, or may focus on increasing the yield of a certain metabolite or reducing the generation of by-products. Therefore, the determination of the directed fermentation task is to specify which mechanism elements need to be optimized or regulated based on the fermentation goals, microbial characteristics and environmental changes. It can provide a clear task framework and control direction for subsequent control decisions.
[0046] Specifically, the mechanism control decision is based on the stable mechanism elements in the fermentation model, determines which factors are fixed and regular, and formulates adjustment measures according to the changing trends of these elements. For example, when the bacterial growth rate and substrate consumption rate during the fermentation process meet the predetermined linear relationship, the mechanism control decision can ensure the smooth progress of the fermentation process by adjusting the temperature, pH value or dissolved oxygen level.
[0047] At the same time, directional regulation decisions focus on unstable and dynamically changing mechanism elements during the fermentation process. Directed regulation decisions need to be flexibly adjusted based on real-time feedback data during the fermentation process, especially signals obtained from unstable mechanism elements. Exemplary, precise regulation of the fermentation environment, substrate supply, enzyme activity or other metabolic pathways. For example, at a certain stage in the fermentation process, the metabolite concentration may fluctuate abnormally. The directional regulation decision guides the system to return to the expected fermentation state through feedback regulation mechanisms, such as increasing or decreasing the substrate feed rate, adjusting the dissolved oxygen content, or changing the reaction temperature.
[0048] By combining mechanism control decisions with directional regulation decisions, the fermentation control strategy is finally determined, that is, the specific control measures and operation steps taken to achieve the directional fermentation task during the entire fermentation process. While ensuring the efficient fermentation process, it can flexibly respond to changing production needs to ensure the maximization of product quality and output.
[0049] Furthermore, the mechanism control decision and directional regulation decision are performed, and step S4 of the present application includes:
[0050] The directional fermentation task is decomposed to determine the standard subtasks and directional subtasks; based on the data interface of the standardization block, the standard subtasks are input and periodic fermentation decisions are made to determine the standardized fermentation plan; the standardized fermentation plan is transferred to the directional adjustment block, and based on the data interface of the directional adjustment block, the directional subtasks are input, directional control optimization decisions are made, and the fermentation control strategy is determined.
[0051] Specifically, the directed fermentation task is decomposed into standard subtasks and directed subtasks. The standard subtasks refer to those routine and regular operation tasks in the fermentation process, such as bacterial growth monitoring, substrate consumption rate measurement, etc., which are generally processed by preset standard control methods. The directed subtasks are tasks for special or unstable factors in the fermentation process, usually involving dynamic adjustments to metabolic pathways, metabolite concentrations or environmental parameters, with the aim of optimizing the fermentation effect through fine control.
[0052] Next, based on the data interface of the standardized block, the standard subtask is input into the block to make periodic fermentation decisions, that is, to make conventional fermentation decisions according to preset periodic parameters, such as fixed time points in the fermentation cycle. For example, when the bacterial growth rate reaches the preset value, the standardized block will automatically adjust the substrate feeding rate to ensure the continuous growth of the bacterial body and prevent excessive consumption of the substrate. It is mainly based on known biological laws and previous experimental data to ensure the smooth progress of the fermentation process.
[0053] After the decision of the standardization block is completed, the standardized fermentation plan will flow to the directional regulation block, and the directional subtask will be input to optimize the unstable mechanism elements in the fermentation process and output the fermentation control strategy. For example, when the concentration of metabolites exceeds the expected range, the directional regulation block will adjust the parameters such as substrate concentration, dissolved oxygen or temperature to inhibit the formation of by-products or increase the synthesis rate of the target product.
[0054] In summary, it not only ensures the stability of basic operations, but also allows for flexible adjustment of dynamic changes in fermentation, working together to form an efficient, stable and controllable fermentation control system.
[0055] Further, after making the directional control optimization decision, step S4 of the present application includes:
[0056] Directed control optimization decision-making is carried out to determine the directed fermentation plan; all fermentation control elements and the culture control mechanism of the directed microecological culture machine are obtained, and a parameter control conversion sequence is constructed based on a linear mapping relationship; based on the parameter control conversion sequence, the directed fermentation plan is subjected to parameter control conversion to determine the fermentation control strategy.
[0057] After completing the control decision, the control plan needs to be converted into the fermentation equipment, that is, the equipment control parameters that can be recognized by the directional microecological culture machine.
[0058] The fermentation control factors include fermentation factor parameters such as bacterial growth, substrate consumption, metabolite production, dissolved oxygen level, temperature control, etc. The culture control mechanism of the directional microecological culture machine refers to the control mechanism for real-time monitoring and regulation of bacterial growth, metabolic pathways, environmental variables, etc.
[0059] Based on the fermentation control elements and the culture control mechanism of the directional microecological culture machine, the linear mapping relationship is further used to construct the parameter control conversion sequence, that is, the mapping relationship between the fermentation control elements and the control parameters of the culture machine, and the quantitative relationship with the relative linear conversion. Based on this, the various fermentation control elements can be converted and matched with the control mechanism of the directional microecological culture machine, so as to realize the effective implementation of various control strategies. For example, through linear mapping, the relationship between bacterial growth and substrate consumption can be mapped to a specific dissolved oxygen adjustment strategy, so that the dissolved oxygen level can be adjusted in real time according to the needs of the bacteria.
[0060] Furthermore, the parameter control conversion sequence is used to convert the directional fermentation scheme into an equipment parameter control strategy of the directional microecological culture machine, namely, the fermentation control strategy. The fermentation control strategy can achieve efficient, stable and flexible control of the fermentation process.
[0061] Further, such as Figure 3 As shown, the directional control optimization decision is made to determine the directional fermentation plan. Step S4 of this application includes:
[0062] Based on the directional subtask, the directional target and the key mechanism elements are determined, wherein the key mechanism elements belong to the mechanism elements and satisfy the preset correlation; taking the directional target as the response dependent variable and the key mechanism elements as the response independent variable, a directional optimization iteration is performed to determine the directional fermentation plan; wherein, a single-step optimization constraint of the element value is performed with a preset optimization step length, a preset number of iterations is used as an optimization stage, and the significant items of the first optimization stage are used as taboo items of the second optimization stage.
[0063] Specifically, the directional goal refers to the specific goal that needs to be optimized or regulated during the fermentation process. For example, the directional goal may be to increase the yield of a certain metabolite, inhibit the formation of by-products, or optimize the growth rate of the bacteria. The key mechanism factors are those biological or physicochemical parameters that are closely related to the directional goal and can significantly affect the fermentation results.
[0064] The preset correlation is a standard for measuring the degree of association between the mechanism elements and the directional goal, and the preset correlation can be determined through data analysis and experimental results to ensure that there is a significant influence relationship between the key mechanism elements and the directional goal. For example, if the directional goal is to increase the yield of the target metabolite, the key enzyme activity, substrate concentration, dissolved oxygen level, etc. in the metabolic pathway may be involved as key mechanism elements.
[0065] The dependent response variable refers to the target variable that needs to be optimized in the directed fermentation process, while the independent response variable is the control factor or parameter that affects the target. The control parameters that can optimally regulate the fermentation process are found through directed optimization iteration.
[0066] In the iterative process of directional optimization, the preset optimization step size is used to constrain the single-step optimization of the factor value. The preset optimization step size refers to the amplitude of each adjustment of the mechanism element. By setting a reasonable step size value in the optimization process, it is possible to avoid excessive adjustments that lead to system instability, or too small adjustments that lead to too slow optimization process. By using it to constrain single-step optimization, it is ensured that each iterative adjustment is within the controllable range, making the optimization process more accurate and efficient.
[0067] At the same time, the preset number of iterations is used as one optimization stage, and the significant items in the first optimization stage are used as taboo items in the second optimization stage. The significant items in the first optimization stage refer to the mechanism elements that have shown a greater impact in the initial iteration process. These elements have obtained good regulation results in the optimization of the first stage. In order to avoid falling into the local optimum, these significant items are set as taboo items in the second optimization stage, that is, no further adjustments are made to them. This step effectively avoids redundant optimization, shortens the iteration time, and can focus on optimizing those mechanism elements that have not yet reached the optimal state.
[0068] Ultimately, through the process of directional optimization iteration, key mechanism elements are gradually adjusted during the optimization process to ensure the best control strategy for achieving the directional goals, ensuring that the key factors in the fermentation process are precisely regulated, thereby achieving the optimization of fermentation efficiency and product quality.
[0069] S5: transmitting the fermentation control strategy to the control center of the directional microecological bacterial culture machine to perform fermentation control of the target microorganisms.
[0070] Among them, the directional microecological culture machine is a device specially used for microbial culture and fermentation process control. It can monitor the changes in the fermentation environment in real time through sensors and control systems, and adjust the culture conditions according to the transmitted control strategy.
[0071] The fermentation control strategy, that is, the control strategy for the entire fermentation cycle that is consistent with the parameters and controls under the control mechanism of the culture machine, is transmitted to the control center. The control center of the directional microecological culture machine will control and adjust the fermentation process of the target microorganism in real time based on the received strategy. The control center will adjust the key control parameters according to the real-time data during the fermentation process. For example, if the control strategy stipulates that the dissolved oxygen level needs to be increased at a certain stage, the control center will adjust the gas supply system to increase the oxygen concentration, or adjust the stirring speed according to the parameter settings in the strategy to increase the solubility of oxygen. If the temperature or pH value fluctuates during the fermentation process, the control center will automatically adjust the heating or cooling system according to the control strategy, or add acid and alkali solutions to maintain a stable fermentation environment.
[0072] In addition, the control system of the directional microecological culture machine also has a feedback mechanism. During the fermentation process, the control center not only executes the commands from the control strategy, but also compares the real-time monitoring data with the set targets to determine whether the fermentation process deviates from the predetermined track. If deviations are found, the control center can automatically adjust the fermentation conditions through the feedback mechanism to ensure that the target microorganisms continue to grow and metabolize in an ideal environment, thereby achieving the maximum yield and quality of the target product.
[0073] Furthermore, after the fermentation control of the target microorganism is performed, the steps of the present application also include:
[0074] Traversing the stability mechanism elements, determining fermentation monitoring elements, wherein the fermentation monitoring elements are any at least two of the stability mechanism elements; for the fermentation monitoring elements, monitoring the elements along with the fermentation control process, and determining element monitoring data; based on the element monitoring data, performing abnormal fermentation state assessment and feedback control management.
[0075] In the embodiment of the present application, considering that the amount of all-factor monitoring data is too large, at least two elements of the stability mechanism elements are taken as the fermentation monitoring elements to monitor the fermentation process status of the target microorganism.
[0076] Furthermore, data related to the fermentation monitoring elements are collected in real time or regularly throughout the fermentation process. These data are usually acquired in real time through sensors, such as pH sensors, oxygen concentration sensors, temperature sensors, etc. Preferably, for each selected fermentation monitoring element, the frequency and method of data collection may vary depending on the specific needs of the fermentation process. In general, for elements that change faster, such as dissolved oxygen concentration, a higher frequency of monitoring may be required, while for elements that change more slowly, such as bacterial growth rate, a lower frequency of monitoring may be used.
[0077] After acquiring the element monitoring data, the abnormal fermentation state is evaluated and feedback control management is performed based on it. That is, it is determined whether the monitoring element meets the numerical value of the current fermentation process to determine whether the fermentation process is abnormal. For example, when the pH value or dissolved oxygen concentration deviates from the preset range, it may mean that there is a problem in the fermentation process. For example, insufficient dissolved oxygen may lead to anaerobic metabolism, and drastic fluctuations in pH value may affect the growth or metabolism of microorganisms. When evaluating abnormal states, preset thresholds or standards are usually used to determine whether they are within the normal range. If the monitored parameters exceed these thresholds, the fermentation state can be determined to be abnormal.
[0078] Once an abnormal state is detected, based on the results of the abnormal fermentation state assessment, the control variables in the fermentation process are automatically adjusted to dynamically regulate the fermentation process to ensure the stability of the fermentation process and ensure high yield and high quality of the final target product.
[0079] Furthermore, the abnormal fermentation status assessment and feedback control management of this application also includes the following steps:
[0080] Based on the element monitoring data of the first fermentation monitoring element, the first fermentation state is evaluated; based on the element monitoring data of the second fermentation detection element, the second fermentation state is evaluated; whether the first fermentation state satisfies the fermentation process state, whether the second fermentation state satisfies the fermentation process state, and a first evaluation result is determined; according to the dynamic conservation equation, the consistency of the first fermentation state and the second fermentation state is determined to generate a second evaluation result; based on the first evaluation result and the second evaluation result, feedback control management of the target microorganism is performed.
[0081] In an embodiment of the present application, the first fermentation monitoring factor refers to the first key monitoring parameter selected during the fermentation process, such as dissolved oxygen concentration or pH value. The purpose of evaluating the first fermentation state based on the collected monitoring data is to determine whether the current fermentation process is in a stable state and whether it meets the preset fermentation conditions. For example, if the dissolved oxygen concentration is too low, it may affect the aerobic metabolism of microorganisms, thereby causing the efficiency of the fermentation process to decrease. Therefore, by comparing with a predetermined ideal range, it is evaluated whether it is in a normal fermentation state. Similarly, based on the monitoring data of the second fermentation monitoring factor, the second fermentation state is evaluated.
[0082] Then, the first fermentation state and the second fermentation state are compared with the ideal state of the fermentation process to confirm whether there is any deviation. For example, if the first fermentation state shows that the dissolved oxygen concentration is below the threshold, and the second fermentation state shows that the substrate consumption rate is low, then this may indicate that there is a problem in some part of the fermentation process. Therefore, based on these evaluation results, a first evaluation result is determined, that is, whether the current fermentation state meets the predetermined fermentation target.
[0083] Next, according to the dynamic conservation equation, based on the balance between the mechanism elements, the consistency of the first fermentation state and the second fermentation state is judged, that is, the monitored element verification process. If the two are inconsistent, it indicates that there may be a detection error, and a second evaluation result is generated.
[0084] Finally, based on the first evaluation result and the second evaluation result, feedback control management of the target microorganism is performed. That is, if the first evaluation result and the second evaluation result indicate that there is an abnormality in the fermentation process, the feedback control system will automatically take measures to make adjustments to ensure that the target microorganism is fermented under optimal conditions, thereby improving production efficiency and the quality of the target product.
[0085] The present application provides a microbial fermentation method, which has the following technical effects:
[0086] 1. Through multi-omics analysis such as genome, transcriptome, proteome, and metabolome, the fermentation mechanism of the target microorganism is comprehensively evaluated to achieve a comprehensive understanding of the fermentation process of the target microorganism, reveal its biological mechanism, and provide a scientific basis for subsequent fermentation optimization. By absolutely quantifying the stable mechanism elements and fuzzy quantifying the unstable mechanism elements, a dynamic conservation equation is constructed to accurately describe the various mechanism elements of the fermentation process. It provides a dynamic and accurate fermentation process modeling method, which helps to predict changes in the fermentation process in real time and achieve efficient control.
[0087] 2. Generate a fermentation control model based on the dynamic conservation equation, decompose the task into standard subtasks and directional subtasks, and make targeted decisions by combining the standardized blocks and directional adjustment blocks of the model to ensure that the fermentation process meets the production goals, effectively improve the controllability and stability of the fermentation process, reduce the need for human intervention, and improve the level of automation.
[0088] 3. Take at least two stable mechanism elements for real-time monitoring to evaluate the fermentation status and avoid massive data processing under full-factor monitoring. At the same time, combine the dynamic conservation equation to determine the consistency of the element status. On the basis of ensuring the monitoring accuracy, reduce the monitoring redundancy, be able to detect abnormalities in time and adjust the fermentation process to ensure stability and efficiency in the production process.
[0089] The present invention significantly improves the control accuracy and stability of the microbial fermentation process, and realizes comprehensive, precise and intelligent control of the fermentation process.
[0090] Through the above detailed description of a microbial fermentation method in this specification, those skilled in the art can clearly know a microbial fermentation method in this embodiment. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.
[0091] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.
Claims
1. A microbial fermentation method, characterized in that: The method comprises: For the target microorganism, the fermentation mechanism is determined by performing multi-omics analysis, wherein the multi-omics analysis at least includes genome-transcriptome-proteome-metabolome, and the fermentation mechanism includes single mechanism and cluster mechanism; Based on the fermentation mechanism, the mechanism elements are quantitatively analyzed to determine the dynamic conservation equation of the target microbial fermentation, wherein the stable mechanism elements are absolutely quantified and the unstable mechanism elements are fuzzily quantified by setting a stability threshold; Based on the fermentation mechanism and the dynamic conservation equation, dynamic behavior fermentation control decision modeling is performed to generate a fermentation control model, wherein the fermentation control model includes a standardized block and a directional adjustment block; Determine the directional fermentation task, combine the fermentation control model, make mechanism control decisions and directional regulation decisions, and determine the fermentation control strategy; The fermentation control strategy is transmitted to the control center of the directional microecological bacterial culture machine to perform fermentation control of the target microorganisms.
2. A microbial fermentation method according to claim 1, characterized in that: The fermentation mechanism is determined by performing multi-omics analysis, including: Call the fermentation records of the target microorganisms, conduct independent single-omics analysis, and explore the omics mechanism of the target microorganisms; For a fermentation cycle, the omics mechanism is subjected to homology mapping between multiple omics, and a single mechanism is determined by fusion; Based on the intra-cluster mutual influence, the single mechanism is adjusted to determine the clustering mechanism; The single mechanism and the clustering mechanism are taken as the fermentation mechanism.
3. A microbial fermentation method according to claim 1, characterized in that: Determine the dynamic conservation equation of the target microbial fermentation, including: A fermentation reference baseline is set, and a point linear relationship is determined for the stability mechanism elements, wherein the fermentation reference baseline is a reference cycle determined by bacterial growth-substrate consumption-fermentation acid production; For the unstable mechanism elements, the interval linear relationship is determined with the amplitude as a constraint; The point linear relationship and the interval linear relationship are integrated to determine the dynamic conservation equation covering all mechanism elements.
4. A microbial fermentation method according to claim 1, characterized in that: The mechanism control decision and directional regulation decision include: Decomposing the directed fermentation task to determine standard subtasks and directed subtasks; Based on the data interface of the standardized block, the standard subtask is input and periodic fermentation decision is made to determine the standardized fermentation plan; The standardized fermentation scheme is transferred to the directional regulation block, and based on the data interface of the directional regulation block, the directional subtask is input to make a directional control optimization decision and determine the fermentation control strategy.
5. A microbial fermentation method according to claim 4, characterized in that: After making a directional control optimization decision, it includes: Make directional control optimization decisions and determine the directional fermentation plan; Obtain all the fermentation control elements and the culture control mechanism of the directional microecological culture machine, and construct a parameter control conversion sequence based on a linear mapping relationship; Based on the parameter-control conversion sequence, the parameter-control conversion is performed on the directional fermentation scheme to determine the fermentation control strategy.
6. A microbial fermentation method according to claim 5, characterized in that: The directional control optimization decision-making and determination of the directional fermentation scheme include: Based on the directional subtask, determining the directional target and the key mechanism element, wherein the key mechanism element belongs to the mechanism element and satisfies a preset correlation; Taking the directional target as the response dependent variable and the key mechanism element as the response independent variable, performing directional optimization iteration to determine the directional fermentation plan; The single-step optimization constraint of the factor value is performed with a preset optimization step length, a preset number of iterations is used as one optimization stage, and the significant items in the first optimization stage are used as taboo items in the second optimization stage.
7. A microbial fermentation method according to claim 1, characterized in that: After the fermentation control of the target microorganism is performed, the method further includes: Traversing the stability mechanism elements to determine fermentation monitoring elements, wherein the fermentation monitoring elements are any at least two of the stability mechanism elements; For the fermentation monitoring elements, element monitoring is performed along with the fermentation control process to determine element monitoring data; Based on the element monitoring data, abnormal fermentation status assessment and feedback control management are carried out.
8. A microbial fermentation method according to claim 7, characterized in that: The abnormal fermentation state assessment and feedback control management include: evaluating a first fermentation state based on the element monitoring data of the first fermentation monitoring element; evaluating the second fermentation state based on the element monitoring data of the second fermentation detection element; determining whether the first fermentation state satisfies a fermentation progress state, determining whether the second fermentation state satisfies a fermentation progress state, and determining a first evaluation result; According to the dynamic conservation equation, performing consistency determination on the first fermentation state and the second fermentation state to generate a second evaluation result; Based on the first evaluation result and the second evaluation result, feedback control management of the target microorganism is performed.