Fermentation microorganism dynamic screening and composition method based on multi-omics combination
By optimizing the inoculation strategy through multi-omics analysis and predictive models, the problem of accurately screening superior fermentation strains in existing technologies has been solved, achieving efficient screening of fermentation performance and stability of the fermentation process.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-27
- Publication Date
- 2026-04-03
AI Technical Summary
Existing technologies cannot accurately and efficiently screen strains with excellent fermentation performance and obtain corresponding ratios, resulting in low fermentation efficiency and unstable product quality.
By performing stratified sampling on fermentation equipment and combining genomics, transcriptomics, proteomics and metabolomics analysis, key metabolites and metabolic pathways were obtained. Multi-omics data were used to mine key target features, core functional strains and auxiliary functional strains were screened, and the inoculation strategy was optimized through fermentation performance prediction models to obtain the optimal inoculation strategy.
It enables precise and efficient screening of strains and their ratios with excellent fermentation performance, improves fermentation efficiency and product quality, enhances batch-to-batch stability and repeatability, and reduces the risk of contamination and the probability of fermentation failure.
Smart Images
Figure CN121789786A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the fields of microbiome engineering and industrial fermentation technology, and relates to, but is not limited to, a method for dynamic screening and composition of fermentation microorganisms based on multi-omics collaboration. Background Technology
[0002] Fermentation engineering has wide applications in many fields such as food, medicine, and bioenergy. During fermentation, the types and composition of microorganisms change dynamically, affecting the yield and quality of fermentation products. To improve fermentation efficiency and product quality, it is necessary to accurately screen and optimize the composition of fermentation microorganisms.
[0003] In related technologies, microbial screening using culture medium separation, microscopic observation, and biochemical identification methods can only target culturable microorganisms and cannot provide a comprehensive understanding of all microorganisms in the fermentation system. Furthermore, these methods are cumbersome, inefficient, and cannot provide specific ratios.
[0004] Therefore, how to accurately and efficiently screen strains with excellent fermentation performance and obtain the corresponding ratios has become an urgent problem to be solved. Summary of the Invention
[0005] In view of this, embodiments of the present invention provide a method for dynamic screening and composition of fermentation microorganisms based on multi-omics collaboration, which at least solves the problem that related technologies cannot accurately and efficiently screen strains with excellent fermentation performance and obtain corresponding ratios.
[0006] According to a first aspect of the present invention, a method for dynamic screening and composition of fermentation microorganisms based on multi-omics collaboration is provided, comprising: Stratified sampling was performed on the fermentation equipment to obtain fermentation samples from three fermentation stages; and microorganisms and metabolites were extracted based on the fermentation samples. Genomics analysis, transcriptional expression profiling analysis, and proteomics analysis were performed on the microorganisms to obtain multiple analytical results; and metabolomics analysis was performed on the metabolites to obtain key metabolites and metabolic pathways. Based on multiple analysis results, the key metabolites, metabolic pathways, and the collection time and geographical location information of fermentation samples, the key characteristics of the target are obtained. Based on the key characteristics of the target, target candidate strains are obtained from a pre-set microbial resource library, and core functional strains and auxiliary functional strains are classified from the target candidate strains. Based on the core functional strains and auxiliary functional strains, multiple inoculation strategies were obtained for each of the three stages of fermentation; and a first fermentation environment was set for each fermentation stage. Multiple inoculation strategies for each fermentation stage and the first fermentation environment are input into the fermentation performance prediction model to obtain the predicted fermentation performance. Based on the predicted fermentation performance, the optimal inoculation strategy for each fermentation stage is obtained from multiple inoculation strategies. The optimal inoculation strategy includes the composition of the target strain and the corresponding ratio.
[0007] According to a second aspect of the present invention, an electronic device is provided, comprising: a processor, a memory, a communication interface, and a communication bus, wherein the processor, the memory, and the communication interface communicate with each other via the communication bus; the memory is used to store at least one executable instruction, wherein the executable instruction causes the processor to perform an operation corresponding to the method described in the first aspect.
[0008] According to a third aspect of the present invention, a computer storage medium is provided having a computer program stored thereon, which, when executed by a processor, implements the method described in the first aspect.
[0009] According to the scheme provided in the embodiments of the present invention, stratified sampling is performed on the fermentation equipment to obtain fermentation samples at three fermentation stages; microorganisms and metabolites are extracted based on the fermentation samples; genomic analysis, transcriptional expression profiling, and proteomics analysis are performed on the microorganisms to obtain multiple analysis results; metabolomics analysis is performed on the metabolites to obtain key metabolites and metabolic pathways; target key features are obtained based on multiple analysis results, the key metabolites, metabolic pathways, and the collection time and geographical location information of the fermentation samples; target candidate strains are obtained from a preset microbial resource library based on the target key features, and core functional strains and auxiliary functional strains are classified from the target candidate strains; multiple inoculation strategies corresponding to each of the three stages in the fermentation process are obtained according to the core functional strains and auxiliary functional strains; a first fermentation environment is set for each fermentation stage; multiple inoculation strategies and the first fermentation environment for each fermentation stage are input into a fermentation performance prediction model to obtain predicted fermentation performance, and the optimal inoculation strategy for each fermentation stage is obtained from the multiple inoculation strategies based on the predicted fermentation performance; the optimal inoculation strategy includes the composition and corresponding ratio of the target strains. In this process, samples were taken in stages within the fermentation equipment to extract microorganisms and metabolites at each stage. Key functional characteristics were identified through genomic, transcriptomic, proteomic, and metabolomic analyses, combined with time and environmental information. Based on these characteristics, candidate strains were screened from a strain resource bank and categorized into core and auxiliary functional strains. Multiple inoculation schemes were designed in conjunction with the fermentation environment at each stage. The optimal inoculation strategy for each stage was selected using a predictive model. The final optimal strategy included which strains to use and their ratios, achieving the effect of accurately and efficiently screening strains with excellent fermentation performance and obtaining the corresponding ratios. Attached Figure Description
[0010] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort, wherein: Figure 1 A schematic flowchart illustrating a method for dynamic screening and composition of fermentation microorganisms based on multi-omics collaboration, provided in an embodiment of the present invention; Figure 2 This is a schematic diagram of the structure of an electronic device according to an embodiment of the present invention. Detailed Implementation
[0011] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. The following embodiments are used to illustrate the present invention, but are not intended to limit the scope of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0012] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.
[0013] It should be noted that the terms "first, second, and third" used in the embodiments of the present invention are only used to distinguish similar objects and do not represent a specific ordering of objects. It is understood that "first, second, and third" can be interchanged in a specific order or sequence where permitted, so that the embodiments of the present invention described herein can be implemented in an order other than that illustrated or described herein.
[0014] It will be understood by those skilled in the art that, unless otherwise defined, all terms used herein (including technical and scientific terms) have the same meaning as commonly understood by one of ordinary skill in the art to which these embodiments of the invention pertain. It should also be understood that terms such as those defined in general dictionaries should be understood to have the same meaning as in the context of the prior art and should not be interpreted in an idealized or overly formal sense unless specifically defined as herein.
[0015] Figure 1This is a flowchart illustrating a method for dynamic screening and composition of fermentation microorganisms based on multi-omics integration, provided by an embodiment of the present invention. This method can be executed by an electronic device, such as a computer or server.
[0016] like Figure 1 As shown, the dynamic screening and composition method for fermentation microorganisms based on multi-omics integration includes: S101. Perform stratified sampling on the fermentation equipment to obtain fermentation samples at three fermentation stages; and extract microorganisms and metabolites based on the fermentation samples.
[0017] In an embodiment of the invention, three layers—upper, middle, and lower—of the fermentation equipment are designated as fixed sampling points, with multiple repeat sampling positions set along the tank wall and central area of each layer. Due to the differences in material gradients within the fermentation equipment, the upper layer is easily affected by oxygen and volatiles, the middle layer is the metabolically active core area, and the lower layer easily accumulates sediment and anaerobic microorganisms. Layered sampling can cover the spatial distribution characteristics of the community. Furthermore, the difference in stirring efficiency between the equipment wall and the central area of the fermentation equipment may lead to different microbial attachment states. Therefore, repeat sampling is used to obtain fermentation samples at different fermentation stages, and finally, microorganisms and metabolites are extracted based on the fermentation samples.
[0018] When sampling was conducted at different fermentation stages, samples were taken every 2 hours during the critical fermentation stage (vigorous fermentation period), and every 6 hours during the initial fermentation period and the stable fermentation period. During the vigorous fermentation period, the microbial metabolic rate was fast.
[0019] During sampling, high-temperature resistant quartz sampling tubes are used. These tubes are autoclaved at 121℃ for 30 minutes, then cooled to room temperature in a sterile operating table, and rinsed multiple times with fermentation broth before use. Simultaneously, operators wear sterile protective clothing and sample through the sterile sampling port on the top of the tank to avoid contact with the external environment. After collection, samples are immediately injected into sterile centrifuge tubes, aliquoted into multiple portions for microbial isolation, nucleic acid extraction, metabolite analysis, etc., and sealed and placed in a preset temperature transport box, such as -80℃. A blank control is simultaneously set up for each batch of samples, and sterile physiological saline is processed using the same procedure. PCR (polymerase chain reaction) detection is used to determine the presence of exogenous microbial genes, ensuring a contamination rate of <0.1%.
[0020] For example, the fermentation cycle is divided into three stages: the first fermentation stage of 0-12h, the second fermentation stage of 12-48h, and the third fermentation stage of 48-72h. Samples are taken for each of the three fermentation stages. Samples are taken every 2 hours in the second fermentation stage and every 6 hours in the other fermentation stages. The sampling points are selected from the upper, middle and lower layers of the fermentation tank.
[0021] S102. Genomic analysis, transcriptional expression profiling and proteomics analysis were performed on the microorganisms to obtain multiple analytical results; and metabolomics analysis was performed on the metabolites to obtain key metabolites and metabolic pathways.
[0022] In embodiments of this invention, genomics aims to understand which microorganisms exist in the community and their potential functional genes; transcriptomics aims to understand which genes are being expressed; proteomics aims to understand the functional proteins actually translated; and metabolomics aims to analyze intracellular / extracellular small molecule metabolites. Genomic analysis of microorganisms yields analytical results; transcriptomic expression profiling of microorganisms yields analytical results; and proteomics analysis yields analytical results. Simultaneously, gas chromatography-mass spectrometry (GC-MS) and liquid chromatography-mass spectrometry (LC-MS) are employed to achieve high-coverage and non-targeted comprehensive detection of small molecule metabolites generated during fermentation. Qualitative and quantitative analysis of metabolites is achieved through chromatographic separation and mass spectrometry identification, combined with standard comparison and database matching. Bioinformatics tools such as MetaboAnalyst (a metabolomics data analysis and visualization platform) are used to statistically analyze and visualize the types, relative contents, and dynamic trends of metabolites, thereby identifying significantly different metabolites, analyzing their involvement in key metabolic pathways, and finally obtaining key metabolites and metabolic pathways.
[0023] S103. Based on multiple analysis results, key metabolites, metabolic pathways, and information on the collection time and geographical location of fermentation samples, obtain the key characteristics of the target.
[0024] In embodiments of the present invention, the collection time can be the fermentation stage, meaning the collection time is categorized into the corresponding fermentation stage. Geographical location information can be the sampling point within the fermentation equipment, and the target key features can be changes in microbial abundance, microbial interactions, and dynamics of metabolite concentrations. After integrating multiple analytical results, key metabolites, metabolic pathways, and the collection time and geographic location information of the fermentation samples, the target key features are extracted from the integrated data.
[0025] S104. Based on the key characteristics of the target, obtain the target candidate strains from the preset microbial resource library, and classify the core functional strains and auxiliary functional strains from the target candidate strains.
[0026] In embodiments of the present invention, the microbial resource library is an existing preserved strain library. The target key characteristics are matched with the characteristics of the strains in the microbial resource library to obtain target candidate strains. Core functional strains are strains that directly participate in the synthesis of the main product or key metabolic steps, while auxiliary functional strains are strains that provide indirect support functions such as providing growth factors, regulating pH, inhibiting contaminating bacteria, and promoting symbiosis. Based on their functions, the target candidate strains are classified into core functional strains and auxiliary functional strains.
[0027] S105. Based on the core functional strains and auxiliary functional strains, obtain multiple inoculation strategies corresponding to the three stages of the fermentation process; and set up the first fermentation environment for each fermentation stage.
[0028] In embodiments of the present invention, the fermentation process is divided into three fermentation stages, with multiple inoculation strategies set for each stage. These strategies may include the inoculation ratio of core functional strains and auxiliary functional strains, the specific timing of inoculation, and the selection of which strain to inoculate. Simultaneously, a corresponding primary fermentation environment is set for each fermentation stage, including parameters such as temperature, pH, dissolved oxygen, stirring speed, and feeding rate.
[0029] S106. Input multiple inoculation strategies and the first fermentation environment for each fermentation stage into the fermentation performance prediction model to obtain the predicted fermentation performance, and obtain the optimal inoculation strategy for each fermentation stage from multiple inoculation strategies based on the predicted fermentation performance; the optimal inoculation strategy includes the composition of the target strain and the corresponding ratio.
[0030] In embodiments of the present invention, fermentation performance can be yield and conversion rate, etc. Multiple inoculation strategies for each fermentation stage and the first fermentation environment are input into the fermentation performance prediction model to predict fermentation performance. Based on the predicted fermentation performance, the optimal inoculation strategy is obtained from the multiple inoculation strategies corresponding to each fermentation stage, including the composition of the target strain and the corresponding ratio.
[0031] It is understood that, in the embodiments of the present invention, stratified sampling is performed on the fermentation equipment to obtain fermentation samples at three fermentation stages; microorganisms and metabolites are extracted based on the fermentation samples; genomics analysis, transcriptional expression profiling analysis, and proteomics analysis are performed on the microorganisms to obtain multiple analytical results; metabolomics analysis is performed on the metabolites to obtain key metabolites and metabolic pathways; target key features are obtained based on multiple analytical results, key metabolites, metabolic pathways, and the collection time and geographical location information of the fermentation samples; target candidate strains are obtained from a preset microbial resource library based on the target key features, and core functional strains and auxiliary functional strains are classified from the target candidate strains; multiple inoculation strategies corresponding to each of the three stages in the fermentation process are obtained according to the core functional strains and auxiliary functional strains; a first fermentation environment is set for each fermentation stage; multiple inoculation strategies and the first fermentation environment for each fermentation stage are input into a fermentation performance prediction model to obtain predicted fermentation performance, and the optimal inoculation strategy for each fermentation stage is obtained from the multiple inoculation strategies based on the predicted fermentability; the optimal inoculation strategy includes the composition and corresponding ratio of the target strains. In this process, sampling is performed in stages in the fermentation equipment to extract microorganisms and metabolites at each fermentation stage. By analyzing genomics, transcriptomics, proteomics, and metabolomics, combined with temporal and environmental information, key functional characteristics were identified. Based on these characteristics, candidate strains were screened from a microbial resource bank, categorized into core and auxiliary functional strains. Multiple inoculation schemes were designed considering the fermentation environment at each stage. A predictive model was used to select the optimal inoculation strategy for each fermentation stage. The final optimal strategy included the specific strains and their ratios, achieving precise and efficient screening of strains with excellent fermentation performance and obtaining the corresponding ratios.
[0032] In some embodiments of the present invention, the genomic analysis, transcriptional expression profiling, proteomics analysis and metabolomics analysis of the microorganisms in S102, and the resulting multiple analysis results can be achieved through S1021 to S1023, as described in the following steps.
[0033] S1021. Perform genome sequencing on microorganisms to obtain genomic information, and perform sequence alignment and analysis on the genomic information to obtain key genes.
[0034] In some embodiments of the present invention, high-throughput sequencing technology can be used to sequence the entire DNA of microorganisms to obtain genomic information. Bioinformatics software, such as BLAST or MEGA, can be used to perform sequence alignment and analysis of the genomic information to identify microbial species and to mine key genes related to fermentation based on the microbial species.
[0035] S1022. Obtain the total ribonucleic acid of the microorganisms and obtain differentially expressed genes based on the transcriptional expression profile corresponding to the total ribonucleic acid.
[0036] In some embodiments of the present invention, total ribonucleic acid is extracted from microorganisms, and a transcriptional expression profile describing gene expression levels is generated using technologies such as high-throughput transcriptome sequencing. Differentially expressed genes are then obtained based on the transcriptional expression profile.
[0037] S1023. Identify and quantify the proteins synthesized by microorganisms to obtain the protein expression profile of microorganisms; and obtain key proteins based on the protein expression profile.
[0038] In some embodiments of the present invention, the proteins synthesized by microorganisms are identified and quantitatively analyzed by mass spectrometry, a protein expression profile is constructed, and key proteins are screened by combining methods such as functional enrichment and pathway analysis.
[0039] In some embodiments of the present invention, S103 can be implemented by S1031 to S1032, as described in the following steps.
[0040] S1031. The features corresponding to key genes, differentially expressed genes, key proteins, key metabolites and metabolic pathways are decomposed into their respective dimensions to obtain their corresponding multidimensional feature subsets; and mapped to time labels and spatial labels according to the collection time and geographical location information of fermentation samples.
[0041] In some embodiments of the present invention, the features corresponding to key genes, differentially expressed genes, key proteins, key metabolites and metabolic pathways are decomposed into independent dimensions to obtain their respective multidimensional feature subsets. Based on the collection timestamps and GPS coordinates / regional codes of the fermentation samples, standardized time labels and spatial labels are obtained.
[0042] S1032. Assemble the multidimensional feature subset, time label and spatial label to obtain the initial matrix; and select the target key features based on the initial matrix.
[0043] In some embodiments of the present invention, multidimensional feature subsets, time labels, and spatial labels are combined into a three-dimensional matrix with spatiotemporal combined sample IDs as rows and multidimensional feature subsets as columns, and this three-dimensional matrix is used as an initial matrix to further obtain target key features.
[0044] For example, the time label can be different stages of fermentation and the sampling time, and the spatial label can be the wall S1 of the first fermentation device to the lower center S6. They are assembled into a three-dimensional matrix with the spatiotemporal combination sample ID as the row and the multidimensional feature subset as the column. For example, in the sample ID encoding S1-T2-2h, S1 represents the upper tank wall (spatial dimension), T2 represents the vigorous fermentation period (time dimension, T2 corresponds to the vigorous period in the encoding rule), and 2h is the specific sampling time.
[0045] After obtaining the initial matrix, the features corresponding to the genome, transcriptome, and proteome are Z-score standardized, and the features corresponding to the metabolome are first transformed by log2(x+1) and then Z-score standardized.
[0046] In some embodiments of the present invention, S1032 can be implemented by S201 to S202, as described in the following steps.
[0047] S201. Calculate the Pearson correlation coefficient between each pair of features in the initial matrix, and perform feature screening based on the Pearson correlation coefficient to obtain the initial key features.
[0048] In some embodiments of the present invention, the Pearson correlation coefficient between each pair of features in the initial matrix is calculated, and redundant feature pairs are found based on the Pearson correlation coefficient and the corresponding preset threshold. The variance inflation factor of each feature in the redundant feature pair is calculated, and the two features are filtered together with the corresponding preset threshold to obtain the initial key features.
[0049] S202. Input the initial key features into multiple ensemble learning models to obtain the scores of the corresponding initial key features. Aggregate the scores and filter the initial key features based on the aggregated scores to obtain the target key features.
[0050] In some embodiments of the present invention, multiple ensemble learning models may include three models: Random Forest, eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM). Initial key features are input into the Random Forest, XGBoost, and LightGBM models respectively to rank feature importance. Each model outputs a score for the initial key features. All scores are aggregated, such as by calculating an average, and then the initial key features are ranked according to the aggregated scores. Features with higher rankings are selected as target key features.
[0051] In some embodiments of the present invention, S104 can be implemented by S1041 to S1043, as described in the following steps.
[0052] S1041. Screening is performed on a pre-set microbial resource library based on the target key characteristics to obtain the first candidate strain; and different second fermentation environments are set in the three fermentation stages respectively.
[0053] In some embodiments of the present invention, strain data from a preset microbial resource library are input into a feature extraction model to obtain features corresponding to each strain. The features corresponding to each strain are matched with key features, and the successfully matched strains are used as first candidate strains. Then, different second fermentation environments are set in the three fermentation stages respectively.
[0054] Specifically, the first fermentation stage is the initial stage, which sets up a second fermentation environment with high sugar stress. The second fermentation stage is the vigorous stage, which sets up a second fermentation environment with programmed temperature fluctuations between multiple temperature values, such as 30℃, 37℃, and 30℃. The third fermentation stage is the stable stage, which sets up a second fermentation environment with dissolved oxygen concentration controlled below, for example, 5%, and pH maintained at, for example, 4.0 due to the accumulation of organic acids.
[0055] S1042. The first candidate strain is inoculated into the second fermentation environment of the first fermentation stage, and after cultivation, it is screened to obtain the second candidate strain; the second candidate strain is inoculated into the second fermentation environment of the second fermentation stage, and after cultivation, it is screened to obtain the third candidate strain.
[0056] In some embodiments of the present invention, after the first candidate strain is inoculated into the second fermentation environment of the first fermentation stage and cultured, strains with a survival rate ≥60% and a sugar consumption rate ≥0.5g / L·h are selected as the second candidate strains. The second candidate strains are inoculated into the second fermentation environment of the second fermentation stage and cultured, and strains with a yield fluctuation ≤15% are selected as the third candidate strains.
[0057] S1043. The third candidate strain is inoculated in the second fermentation environment of the third fermentation stage. After cultivation, the strain is screened to obtain the fourth candidate strain. Orthogonal experiments are then performed on the fourth candidate strain to obtain the target candidate strain.
[0058] In some embodiments of the present invention, the third candidate strain is inoculated and cultured in the second fermentation environment of the third fermentation stage, and strains with a product synthesis duration of >24h are screened as the fourth candidate strain.
[0059] Furthermore, an orthogonal experiment was conducted on the fourth candidate strain. The orthogonal experimental parameters included temperature, pH, substrate concentration, growth rate, product yield, and byproduct inhibition rate. The target candidate strain was obtained from the fourth candidate strain through multi-factor cross-validation. The temperature could be, for example, 30℃, 35℃, or 40℃; the pH could be, for example, 3.5, 4.5, or 5.5; and the substrate concentration could be, for example, 5%, 10%, or 15%.
[0060] In some embodiments of the present invention, the extraction of microorganisms and metabolites based on fermentation samples in S101 can be achieved through S101A to S101B, as described in the following steps.
[0061] S101A. The fermentation sample is filtered to obtain the processed fermentation sample; and the processed fermentation sample is centrifuged to obtain microbial precipitate and supernatant.
[0062] S101B: Microorganisms are extracted from microbial precipitates, and metabolites are extracted from the supernatant using organic solvent extraction.
[0063] In some embodiments of the present invention, the fermentation sample is filtered to remove impurities, and the fermentation sample is further processed by centrifugation and organic solvent extraction to extract microorganisms and metabolites from the fermentation sample.
[0064] In embodiments of the present invention, after determining the optimal inoculation strategy, it can be applied to the actual fermentation process. For example, in the first fermentation stage, the core strain and auxiliary strain are inoculated in an optimized ratio, and the inoculation concentration is controlled at, for example, 10. 6 -10 8 Within the CFU / mL range; key performance parameters, such as product generation rate and substrate consumption rate, are monitored in real time during fermentation. If a performance decline is detected, highly active core strains are promptly added, along with growth-promoting metabolites produced by helper strains, to maintain synergistic effects and fermentation efficiency. This direct application of optimal strategies to the actual fermentation process improves product yield and fermentation efficiency, shortens the production cycle, enhances batch-to-batch stability and reproducibility, facilitating standardized production, reduces the risk of contamination and fermentation failure, and optimizes resource utilization through precise control of inoculation timing, ratio, and feeding.
[0065] Reference Figure 2 The diagram shows a structural schematic of an electronic device according to an embodiment of the present invention. The specific embodiments of the present invention do not limit the specific implementation of the electronic device.
[0066] like Figure 2 As shown, the electronic device may include: a processor 502, a communications interface 504, a memory 506, and a communications bus 508.
[0067] in: The processor 502, communication interface 504, and memory 506 communicate with each other via communication bus 508.
[0068] Communication interface 504 is used to communicate with other electronic devices or servers.
[0069] The processor 502 is used to execute program 510, specifically the relevant steps in the above method embodiments.
[0070] Specifically, program 510 may include program code that includes computer operation instructions.
[0071] Processor 502 may be a central processing unit (CPU), an application-specific integrated circuit (ASIC), or one or more integrated circuits configured to implement embodiments of the present invention. The smart device may include one or more processors of the same type, such as one or more CPUs; or it may include processors of different types, such as one or more CPUs and one or more ASICs.
[0072] Memory 506 is used to store program 510. Memory 506 may include high-speed RAM memory, and may also include non-volatile memory, such as at least one disk storage device.
[0073] Specifically, program 510 can be used to cause processor 502 to perform the operations corresponding to the methods described in the above method embodiments.
[0074] The specific implementation of each step in program 510 can be found in the corresponding descriptions of the steps and units in the above method embodiments, and will not be repeated here. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the devices and modules described above can be referred to the corresponding process descriptions in the foregoing method embodiments, and will not be repeated here.
[0075] It should be noted that, depending on the implementation needs, the various components / steps described in the embodiments of the present invention can be broken down into more components / steps, or two or more components / steps or parts of the operation of components / steps can be combined into new components / steps to achieve the purpose of the embodiments of the present invention.
[0076] The methods described above according to embodiments of the present invention can be implemented in hardware, firmware, or as software or computer code that can be stored in a recording medium (such as a CD-ROM, RAM, floppy disk, hard disk, or magneto-optical disk), or as computer code originally stored on a remote recording medium or a non-transitory machine-readable medium and subsequently stored on a local recording medium, downloaded via a network. Thus, the methods described herein can be processed by software stored on a recording medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware (such as an ASIC or FPGA). It is understood that the computer, processor, microprocessor controller, or programmable hardware includes storage components (e.g., RAM, ROM, flash memory, etc.) capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods described herein. Furthermore, when a general-purpose computer accesses code used to implement the methods shown herein, the execution of the code transforms the general-purpose computer into a dedicated computer for executing the methods shown herein.
[0077] Those skilled in the art will recognize that the units and method steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of the embodiments of the present invention.
[0078] The above embodiments are only used to illustrate the embodiments of the present invention, and are not intended to limit the embodiments of the present invention. Those skilled in the art can make various changes and modifications without departing from the spirit and scope of the embodiments of the present invention. Therefore, all equivalent technical solutions also fall within the scope of the embodiments of the present invention, and the patent protection scope of the embodiments of the present invention should be defined by the claims.
Claims
1. A method for dynamic screening and composition analysis of fermentation microorganisms based on multi-omics collaboration, characterized in that, include: Stratified sampling was performed on the fermentation equipment to obtain fermentation samples from three fermentation stages. Microorganisms and metabolites were extracted from fermentation samples; Genomics analysis, transcriptional expression profiling analysis, and proteomics analysis were performed on the microorganisms to obtain multiple analytical results; and metabolomics analysis was performed on the metabolites to obtain key metabolites and metabolic pathways. Based on multiple analysis results, the key metabolites, metabolic pathways, and the collection time and geographical location information of fermentation samples, the key characteristics of the target are obtained. Based on the key characteristics of the target, target candidate strains are obtained from a pre-set microbial resource library, and core functional strains and auxiliary functional strains are classified from the target candidate strains. Multiple inoculation strategies corresponding to the three stages of fermentation were obtained based on the core functional strains and auxiliary functional strains. And set up a first fermentation environment for each fermentation stage; Multiple inoculation strategies for each fermentation stage and the first fermentation environment are input into the fermentation performance prediction model to obtain the predicted fermentation performance. Based on the predicted fermentation performance, the optimal inoculation strategy for each fermentation stage is obtained from multiple inoculation strategies. The optimal inoculation strategy includes the composition of the target strain and the corresponding ratio.
2. The method according to claim 1, characterized in that, The microorganisms were subjected to genomic analysis, transcriptional expression profiling, and proteomics analysis, respectively, yielding multiple analytical results, including: Microbial genome sequencing is performed to obtain genomic information, and sequence alignment and analysis of the genomic information are conducted to obtain key genes; Total ribonucleic acid (RNA) of microorganisms was obtained, and differentially expressed genes were identified based on the transcriptional expression profile corresponding to the total RNA. The proteins synthesized by microorganisms are identified and quantitatively analyzed to obtain the protein expression profiles of microorganisms; and key proteins are obtained based on the protein expression profiles.
3. The method according to claim 2, characterized in that, The acquisition of key target features based on multiple analysis results, key metabolites, metabolic pathways, and the collection time and geographical location information of fermentation samples includes: The features corresponding to key genes, differentially expressed genes, key proteins, key metabolites, and metabolic pathways are decomposed into their respective dimensions to obtain their corresponding multidimensional feature subsets; and then mapped to time labels and spatial labels according to the collection time and geographical location information of the fermentation samples. The multidimensional feature subset, time label, and spatial label are assembled to obtain an initial matrix; and the target key features are selected based on the initial matrix.
4. The method according to claim 3, characterized in that, The selection of key target features based on the initial matrix includes: Calculate the Pearson correlation coefficient between each pair of features in the initial matrix, and then perform feature selection based on the Pearson correlation coefficient to obtain the initial key features; The initial key features are input into multiple ensemble learning models to obtain scores for their respective initial key features. The scores are then aggregated, and the target key features are obtained by filtering the initial key features based on the aggregated scores.
5. The method according to claim 1, characterized in that, The process of obtaining target candidate strains from a pre-defined microbial resource library based on key target characteristics includes: The first candidate strain was obtained by screening the target key characteristics on a pre-set microbial resource library; and different second fermentation environments were set in the three fermentation stages respectively. The first candidate strain was inoculated into the second fermentation environment of the first fermentation stage, and after cultivation, it was screened to obtain the second candidate strain; the second candidate strain was inoculated into the second fermentation environment of the second fermentation stage, and after cultivation, it was screened to obtain the third candidate strain. The third candidate strain was inoculated in the second fermentation environment of the third fermentation stage. After cultivation, it was screened to obtain the fourth candidate strain. Orthogonal experiments were then performed on the fourth candidate strain to obtain the target candidate strain.
6. The method according to claim 1, characterized in that, The extraction of microorganisms and metabolites based on fermentation samples includes: The fermentation sample was filtered to obtain the treated fermentation sample; and the treated fermentation sample was centrifuged to obtain microbial precipitate and supernatant. Microorganisms were extracted from a microbial precipitate, and the metabolites were extracted from the supernatant using organic solvent extraction.