Mining method and system for synthetic biological functional element and storage medium

Abstract

The invention relates to the technical field of bioinformatics, in particular to a mining method and system for synthetic biology functional elements and a storage medium. The method comprises the following steps: acquiring functional demand data of a target metabolic pathway; performing evolutionary distance analysis and function prediction on homologous genes of a plurality of species according to the function demand data, and performing protein structural domain conservative classification on preset genome sequence data to obtain candidate element cluster data with function relevance; obtaining expression system data of the host cells; and according to the candidate element cluster data and the expression system data, scoring each functional element based on codon optimization degree, protein folding stability and metabolic load prediction to obtain hierarchical element suitability data. According to the method, factors such as sequence characteristics, expression efficiency and regulation and control network interaction are considered at the same time, multi-level accurate screening is achieved, and therefore the low efficiency and limitation of a traditional screening mode are overcome.

Description

Technical Field

[0001] The present invention relates to the technical field of bioinformatics, and in particular to a method, system and storage medium for mining synthetic biology functional elements. Background Art

[0002] Synthetic biology is the design and construction of new biological functional elements, systems and organisms through engineering to achieve predetermined functional goals. Functional elements are the basic units for building biological systems in synthetic biology. They usually include genes, regulatory elements, enzymes, metabolic pathways, etc. The mining of functional elements in synthetic biology refers to the identification, design, screening and optimization of new biological functional elements through various means to build efficient biological systems and engineered microorganisms; this process not only involves mining existing biological elements from nature, but also includes the generation of new functional elements through artificial design, synthesis and transformation. The goal of this method of mining is mainly to identify, extract and optimize gene regulatory elements (such as promoters, enhancers, terminators, etc.) related to specific biological functions, as well as other functional sequences that are critical to gene expression, signal transduction, metabolic pathways and biological process regulation. These functional elements are crucial to the research and application of synthetic biology because they allow engineered microorganisms or cell systems to perform complex biological tasks.

[0003] However, current methods for mining functional elements in synthetic biology often have the following problems: In synthetic biology applications, multifunctional elements are often required to work together (such as multiple enzymes working together to complete a metabolic pathway), but there is often a mismatch between the expression levels and functions of different elements; regulatory elements, promoters, etc. may interfere with the expression or function of other genes, resulting in reduced efficiency of the entire system. In the construction of synthetic pathways, when multiple functional elements are spliced ​​into a complete metabolic pathway, there are often inconsistent expression levels of different elements. Even in the same host cell, the interaction between different promoter strengths, enzyme activities, and metabolite concentrations may lead to inefficient pathways or even waste of resources. Summary of the invention

[0004] Based on this, it is necessary for the present invention to provide a method, system and storage medium for mining synthetic biology functional elements to solve at least one of the above-mentioned technical problems.

[0005] To achieve the above object, a method for mining synthetic biological functional elements comprises the following steps: Step S1: Acquire the functional requirement data of the target metabolic pathway; perform evolutionary distance analysis and functional prediction on homologous genes of multiple species according to the functional requirement data, and classify the preset genome sequence data based on the conservation of protein domains to obtain candidate element population data with functional relevance; Step S2: obtaining the expression system data of the host cell; scoring each functional element based on the codon optimization degree, protein folding stability and metabolic load prediction according to the candidate element population data and the expression system data, and dividing the element into a high expression group, a medium expression group and a low expression group according to the scoring results, to obtain hierarchical element suitability data; Step S3: constructing an expression vector with a regulatable promoter and a standardized connection interface based on the element adaptability data to obtain modular recombinant vector data, wherein the standardized connection interface includes a ribosome binding site for transcription initiation and a bidirectional terminator for transcription termination; Step S4: encapsulating the cell population in a microfluidic droplet for single cell separation according to the modular recombinant vector data, and monitoring the growth status and element expression level of the single cell in real time by fluorescence intensity to obtain single cell expression profile data; Step S5: Screen the functional elements of cell growth rate, product accumulation rate and metabolic flux changes based on the single-cell expression profile data to obtain functional element characteristic map data.

[0006] The present invention significantly improves the accuracy and practicality of the screening of target metabolic pathway elements through a systematic step design from macro to micro, from group to individual. By analyzing the functional requirements of the target metabolic pathway, the key metabolic nodes that need to be optimized or strengthened are clearly identified, and combined with evolutionary distance analysis and functional prediction, genes with potential functional advantages in different species are identified to ensure the diversity and functional relevance of candidate elements. At the same time, the classification technology based on the conservation of protein domains improves the accuracy of element screening, laying a solid foundation for subsequent adaptation and optimization. By combining the host expression system data and the candidate element data, the functional elements are comprehensively scored from aspects such as codon optimization, protein folding stability, and metabolic load prediction, and are divided into high expression group, medium expression group, and low expression group. This process not only ensures the translation efficiency and functional stability of the element, but also significantly reduces the metabolic burden, thereby optimizing the adaptability of the element in the host, and providing an efficient hierarchical guidance basis for modular design. By using a modular recombinant vector constructed based on adaptability data, a standardized connection interface and a regulatable promoter are used, which not only improves the compatibility and flexibility between elements, but also simplifies the complexity of experimental construction. Regulatable promoters provide a broad design space for dynamic regulation and conditional expression, while standardized connection interfaces accelerate the vector optimization and verification process. Single-cell separation and real-time monitoring are achieved through microfluidic droplet technology, avoiding the interference of group effects on the data. At the same time, the growth status and element expression level of single cells are dynamically monitored through fluorescent signals, providing a guarantee for high-throughput and high-precision screening. This method not only obtains the dynamic expression characteristics of functional elements, but also fully reveals cell heterogeneity, providing profound insights for optimizing element combinations. Based on single-cell expression profile data, by combining indicators such as growth rate, product accumulation rate, and metabolic flux changes, the functional elements are comprehensively evaluated and screened in multiple dimensions to generate a functional element feature map. The map intuitively shows the differences in element performance, providing a reliable guide for subsequent optimization combination design. The efficient element combination finally screened out can greatly improve the overall performance of the target metabolic pathway. Overall, this method significantly improves the efficiency and transformation potential of metabolic engineering research through precise functional requirement analysis, hierarchical adaptability optimization, modular vector design, high-throughput single-cell monitoring, and multi-dimensional component performance evaluation, providing a scientific, systematic, and efficient solution for the design and optimization of complex metabolic pathways.

[0007] Preferably, step S1 comprises the following steps: Step S11: obtaining functional requirement data of the target metabolic pathway; Step S12: using a sequence alignment algorithm to search for homologous sequences on the preset genome sequence data according to the functional requirement data, thereby obtaining initial candidate sequence data; Step S13: Based on the initial candidate sequence data, multiple sequence alignment and phylogenetic analysis are performed on homologous gene sequences of different species, and an evolutionary tree is constructed using the maximum likelihood method and a bootstrap test is performed to obtain gene evolution distance distribution data; Step S14: performing protein domain annotation and conservation analysis on the sequence according to the gene evolution distance distribution data, and predicting the boundaries of the functional domains, thereby obtaining domain conservation distribution data; Step S15: performing sequence classification based on the type and combination pattern of functional domains through the domain conservation distribution data, thereby obtaining functional domain classification data; Step S16: Based on the functional domain classification data, the functional association of the category sequence is predicted using a sequence similarity network analysis method, and annotation is performed to obtain candidate element group data with functional association.

[0008] The present invention significantly improves the accuracy and reliability of the screening of functional elements of the target metabolic pathway by layer-by-layer refinement and optimization. The functional requirement data of the target metabolic pathway is clarified, providing clear directions and constraints for subsequent screening. The genome sequence is searched for homologous sequences through a sequence alignment algorithm, and the initial candidate sequence data that meets the functional requirements are efficiently screened out, greatly reducing the interference of irrelevant sequences. Through multiple sequence alignment and phylogenetic analysis, the maximum likelihood method is used to construct an evolutionary tree and the bootstrap method is used to test it, thereby improving the accuracy of evolutionary distance analysis, thereby generating accurate gene evolutionary distance distribution data. Based on the evolutionary distance data, protein domain annotation and conservation analysis are performed, and combined with the prediction of functional domain boundaries, domain conservation distribution data is generated, revealing the functional potential of the candidate sequence and its connection with the conservation of the domain. Sequence classification is performed through domain conservation distribution data, and functional domain classification data is generated based on the type and combination pattern of the functional domain, which lays the foundation for the accurate classification and diversity analysis of functional elements. Finally, the functional relevance is predicted by the sequence similarity network analysis method through the functional domain classification data, and the elements are annotated, thereby obtaining candidate element population data with clear functions and strong relevance. The overall process combines multi-level bioinformatics analysis methods to achieve effective transformation from data to knowledge, from initial candidate sequences to precise screening of functional elements, providing solid technical support for the design and optimization of metabolic engineering.

[0009] Preferably, step S13 comprises the following steps: Step S131: establishing a multiple sequence alignment matrix based on the initial candidate sequence data, and performing progressive pairwise alignment using the ClustalW algorithm to obtain sequence consistency score data; Step S132: performing Gap penalty optimization and weight matrix adjustment on the comparison result based on the sequence consistency score data, and calculating sequence similarity using the PAM250 substitution matrix, thereby obtaining sequence alignment data; Step S133: calculating the evolutionary distance between sequences based on the sequence alignment data, and correcting the nucleotide substitution rate using the Kimura two-parameter model to obtain genetic distance matrix data; Step S134: construct a phylogenetic tree based on the genetic distance matrix data using the maximum likelihood method to obtain gene evolution distance distribution data, wherein the branch length of the phylogenetic tree reflects the evolutionary distance between sequences, and the support rate of the branch node is calculated by the bootstrap method.

[0010] The present invention provides comprehensive and reliable technical support for gene evolution analysis by constructing a multiple sequence alignment matrix, optimizing the alignment algorithm, and accurately calculating the evolutionary distance. The ClustalW algorithm is used to perform progressive pairwise alignment on the initial candidate sequence data, effectively generating sequence consistency score data, and ensuring the accuracy and comprehensiveness of the alignment results. Gap penalty optimization and weight matrix adjustment are performed based on the consistency score data, and the PAM250 substitution matrix is ​​introduced to calculate the sequence similarity, which further improves the accuracy of the alignment results and lays a solid foundation for evolutionary analysis. The nucleotide substitution rate is corrected by the Kimura two-parameter model, and the genetic distance between sequences is accurately calculated, so as to generate scientific and rigorous genetic distance matrix data, which provides key parameters for subsequent phylogenetic analysis. The maximum likelihood method is used to construct a phylogenetic tree based on the genetic distance matrix, and the branch length intuitively reflects the evolutionary distance between sequences. At the same time, the support rate of the branch node is calculated by the bootstrap method, which enhances the credibility and explanatory power of the phylogenetic tree. The synergistic effect of this series of steps not only improves the accuracy of evolutionary distance analysis, but also provides high-quality data support and systematic analysis methods for the screening and function prediction of candidate elements, which greatly helps the research and application of metabolic engineering.

[0011] Preferably, step S14 comprises the following steps: Step S141: predicting the domain of the sequence according to the gene evolution distance distribution data, and annotating the domain through the Pfam database, thereby obtaining initial domain annotation data; Step S142: performing sequence conservative motif search on the initial domain annotation data, setting the minimum motif length to 6 amino acids, the maximum motif length to 50 amino acids, and setting the P-value threshold to 0.05, thereby obtaining conservative motif distribution data; Step S143: constructing a domain-specific hidden Markov model based on the conserved motif distribution data, optimizing the model parameters through multiple rounds of iterative training until a preset convergence threshold is reached, thereby obtaining domain prediction model data; Step S144: locating the domain boundary in the sequence based on the domain prediction model data, and performing local scoring by a sliding window method, thereby obtaining domain boundary prediction data; Step S145: performing conservation score evaluation on each domain according to the domain boundary prediction data, thereby obtaining domain conservation score data, wherein the conservation score evaluation specifically uses Jensen-Shannon divergence to evaluate the degree of conservation of sites in sequence alignment; Step S146: Use the domain conservation score data to perform functional verification on each predicted domain, thereby obtaining domain conservation distribution data.

[0012] The present invention realizes efficient prediction, precise positioning and functional verification of gene domains by combining multiple bioinformatics methods, and provides important support for the screening of key components of metabolic pathways. The Pfam database is used to annotate the gene evolutionary distance distribution data with domains, and the initial domain annotation data is generated, which lays the foundation for subsequent analysis. By setting the minimum motif length and P-value threshold, a systematic search is performed on the conservative motifs to generate conservative motif distribution data, thereby revealing the core features with functional relevance in the sequence. By constructing a hidden Markov model and performing multiple rounds of iterative training, the accuracy and stability of the model parameters are optimized, and reliable domain prediction model data are generated, providing technical support for boundary positioning. Based on the prediction model, the domain boundaries are accurately positioned and locally scored using the sliding window method, and high-resolution domain boundary prediction data is generated, which provides specific boundary information for further analysis of functional elements. The conservation score is calculated by Jensen-Shannon divergence, the sequence conservation is quantitatively evaluated, and the domain conservation scoring data is generated, revealing the key sites with the strongest functional relevance in the sequence. Based on the conservation score data, the domains are functionally verified to generate high-quality domain conservation distribution data, thereby comprehensively evaluating the functional properties and evolutionary significance of the domains. The coordinated optimization of this series of steps significantly improves the accuracy and biological significance of domain prediction, provides a systematic, precise and efficient solution for the design and optimization of functional elements in metabolic engineering, and promotes the research and application of complex biological networks.

[0013] The present invention also provides a mining system for synthetic biology functional elements, which is used to execute the mining method for synthetic biology functional elements. The mining system for synthetic biology functional elements comprises: The target requirement analysis module is used to obtain the functional requirement data of the target metabolic pathway; based on the functional requirement data, the homologous genes of multiple species are analyzed for evolutionary distance and functional prediction, and the preset genome sequence data are classified based on the conservation of protein domains to obtain candidate element population data with functional relevance; The component adaptability evaluation module is used to obtain the expression system data of the host cell; score each functional element based on the codon optimization degree, protein folding stability and metabolic load prediction according to the candidate element population data and expression system data, and divide the elements into high expression group, medium expression group and low expression group according to the scoring results to obtain hierarchical component adaptability data; A modular vector construction module, used for constructing an expression vector with a regulatable promoter and a standardized connection interface based on the element fitness data, to obtain modular recombinant vector data, wherein the standardized connection interface includes a ribosome binding site for transcription initiation and a bidirectional terminator for transcription termination; The single-cell expression monitoring module is used to encapsulate the cell population in microfluidic droplets for single-cell separation according to the modular recombinant vector data, and to monitor the growth status and element expression level of individual cells in real time through fluorescence intensity to obtain single-cell expression profile data; The functional element screening module is used to screen the functional elements of cell growth rate, product accumulation rate and metabolic flux changes based on single-cell expression profile data to obtain functional element characteristic map data.

[0014] The present invention also provides a storage medium, comprising: Memory for storing computer programs; A processor is used to implement the above-mentioned method for mining synthetic biology functional elements when executing the computer program.

[0015] The present invention realizes efficient optimization of target metabolic pathway design and accurate screening of functional elements through a systematic method, which greatly improves the research and application efficiency of metabolic engineering. The target demand analysis module obtains the functional demand data of the target metabolic pathway, and combines the evolutionary distance analysis and function prediction of homologous genes of multiple species, classifies the preset genome sequence data based on the conservation of protein domains, and generates candidate element population data with functional relevance. The effect of this module is to comprehensively explore potential functional elements and ensure their high correlation with the requirements of the target metabolic pathway. The element adaptability evaluation module combines the expression system data of the host cell, scores the candidate elements from multiple dimensions such as codon optimization, protein folding stability and metabolic load prediction, and efficiently stratifies the elements to generate hierarchical element adaptability data. This module ensures the compatibility of the elements with the host system, thereby improving the expression efficiency and system stability. The modular vector construction module constructs an expression vector with a standardized connection interface based on the element adaptability data, ensures the accurate docking of the regulatable promoter and ribosome binding site with the bidirectional terminator, and generates modular recombinant vector data. This modular design significantly improves the flexibility and standardization of vector construction, laying the foundation for further experiments. The single-cell expression monitoring module uses microfluidics technology to achieve single-cell encapsulation, and realizes high-precision analysis of cell growth status and element expression levels through real-time fluorescence monitoring, generating single-cell expression profile data, and providing high-resolution data support for subsequent functional evaluation. The functional element screening module is based on single-cell expression profile data, comprehensively analyzing cell growth rate, product accumulation rate and metabolic flux changes, and generating functional element characteristic map data. This module provides a comprehensive and accurate basis for the screening of functional elements, significantly improving the efficiency of metabolic pathway design and the feasibility of target product production. The overall process embodies the full-link integration from demand analysis to element screening, providing an efficient and standardized solution for metabolic engineering research, and promoting the further development of industrial biotechnology. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Other features, objects and advantages of the present invention will become more apparent from the detailed description of non-limiting embodiments thereof made with reference to the following drawings: Figure 1 A schematic flow chart of the steps of the method for mining synthetic biological functional elements of the present invention; Figure 2 for Figure 1 Detailed step flow diagram of step S1; Figure 3 for Figure 1 Detailed step flow chart of step S2 in FIG. DETAILED DESCRIPTION

[0017] The technical method of the present invention is described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by technicians in this field without creative work are within the scope of protection of the present invention.

[0018] In addition, the accompanying drawings are only schematic illustrations of the present invention and are not necessarily drawn to scale. The same reference numerals in the figures represent the same or similar parts, and their repeated description will be omitted. Some of the block diagrams shown in the accompanying drawings are functional entities and do not necessarily correspond to physically or logically independent entities. The functional entities can be implemented in software form, or implemented in one or more hardware modules or integrated circuits, or implemented in different networks and / or processor methods and / or microcontroller methods.

[0019] It should be understood that, although the terms "first", "second", etc. may be used herein to describe various units, these units should not be limited by these terms. These terms are used only to distinguish one unit from another unit. For example, without departing from the scope of the exemplary embodiments, the first unit may be referred to as the second unit, and similarly the second unit may be referred to as the first unit. The term "and / or" used herein includes any and all combinations of one or more of the listed associated items.

[0020] To achieve this, please refer to Figures 1 to 3 The present invention provides a method for mining synthetic biological functional elements, the method comprising the following steps: Step S1: Acquire the functional requirement data of the target metabolic pathway; perform evolutionary distance analysis and functional prediction on homologous genes of multiple species according to the functional requirement data, and classify the preset genome sequence data based on the conservation of protein domains to obtain candidate element population data with functional relevance; The functional requirement data of the target metabolic pathway can be obtained by combining literature research with bioinformatics analysis to clarify the key reactions of the metabolic pathway and the catalytic effects of the enzymes required; for homologous genes of multiple species, the BLAST tool is used to analyze the highly conserved sequence regions in the database, and the evolutionary distance is calculated by software such as MEGA; at the same time, the Pfam or InterPro database is used to predict the function of the gene product to determine whether the gene has the target function; then based on the conservation of the protein domain, the preset genome sequence is divided into several categories according to the degree of functional conservation by the HMMER tool, and finally the candidate element population data with functional association is screened out by combining cluster analysis and network analysis tools (such as Cytoscape). In a specific application scenario, taking the synthesis of a certain metabolite (such as succinic acid) as an example, the enzyme gene related to succinic acid synthesis can be selected and the above analysis can be performed.

[0021] Step S2: obtaining the expression system data of the host cell; scoring each functional element based on the codon optimization degree, protein folding stability and metabolic load prediction according to the candidate element population data and the expression system data, and dividing the element into a high expression group, a medium expression group and a low expression group according to the scoring results, to obtain hierarchical element suitability data; The host cell expression system data of the embodiment of the present invention can be obtained by consulting the literature related to the host genome and transcriptome, and combining experiments to determine the protein expression ability of the host cell; the gene sequence of the candidate element group is input into the CodonOptimization Online Tool (COOT) to evaluate the codon optimization degree, and the protein folding stability is predicted using Rosetta software, and the metabolic load is evaluated by the Flux Balance Analysis (FBA) model; after scoring each element according to the above indicators, the K-means clustering algorithm is used to group the elements into high expression groups, medium expression groups, and low expression groups, and finally generate hierarchical element fitness data. Taking the expression system of Escherichia coli as the host cell as an example, the codon fitness index (CAI) of the high expression group element is greater than 0.85, while that of the low expression group is less than 0.6.

[0022] Step S3: constructing an expression vector with a regulatable promoter and a standardized connection interface based on the element adaptability data to obtain modular recombinant vector data, wherein the standardized connection interface includes a ribosome binding site for transcription initiation and a bidirectional terminator for transcription termination; According to the hierarchical element adaptability data, the embodiment of the present invention selects the functional elements of the high expression group to design an expression vector with a regulatable promoter and a standardized connection interface. The standardized connection interface design follows the Golden Gate Assembly rule and includes a ribosome binding site and a bidirectional terminator adapted to a specific restriction site; the vector skeleton is designed using SnapGene software and constructed using the Gibson Assembly method; a responsive promoter (such as a Tet-inducible promoter) is used in the vector to regulate the expression intensity of the target gene. Finally, modular recombinant vector data is obtained. Taking the design of the pET-28a-based vector as an example, its ribosome binding site sequence is AGGAGGTT and the terminator sequence is TAAATAA.

[0023] Step S4: encapsulating the cell population in a microfluidic droplet for single cell separation according to the modular recombinant vector data, and monitoring the growth status and element expression level of the single cell in real time by fluorescence intensity to obtain single cell expression profile data; The embodiment of the present invention uses a microfluidic chip (such as a Droplet Digital PCR device) to encapsulate a cell group, generates a single-cell separation environment on the chip through a droplet formation module, and the droplet diameter is set to 30 microns to accommodate a single cell; a fluorescent probe is used to label the target gene product, and the growth state and element expression level of the single cell in the droplet are monitored in real time through a fluorescence microscope, and the measurement accuracy of the fluorescence intensity can be improved by a Photometrics Evolve 512 EMCCD camera; in a test environment, green fluorescent protein (GFP) is used as a reporter gene, and the single-cell expression profile data is quantified through the dynamic changes of the fluorescence signal.

[0024] Step S5: Screen the functional elements of cell growth rate, product accumulation rate and metabolic flux changes based on the single-cell expression profile data to obtain functional element characteristic map data.

[0025] The embodiment of the present invention imports single-cell expression profile data into the Seurat package in the R language for data processing, and performs dimensionality reduction analysis on single-cell growth rate, product accumulation rate and metabolic flux change by the UMAP method; through a functional element screening strategy based on principal component analysis (PCA), functional elements with significant differences are marked, and their characteristic maps are constructed; taking the synthetic paclitaxel metabolic pathway as an example, by analyzing single-cell data, key gene elements that can significantly increase paclitaxel production are found and annotated in the map, and finally a network diagram of the interaction between functional elements is drawn.

[0026] The present invention significantly improves the accuracy and practicality of the screening of target metabolic pathway elements through a systematic step design from macro to micro, from group to individual. By analyzing the functional requirements of the target metabolic pathway, the key metabolic nodes that need to be optimized or strengthened are clearly identified, and combined with evolutionary distance analysis and functional prediction, genes with potential functional advantages in different species are identified to ensure the diversity and functional relevance of candidate elements. At the same time, the classification technology based on the conservation of protein domains improves the accuracy of element screening, laying a solid foundation for subsequent adaptation and optimization. By combining the host expression system data and the candidate element data, the functional elements are comprehensively scored from aspects such as codon optimization, protein folding stability, and metabolic load prediction, and are divided into high expression group, medium expression group, and low expression group. This process not only ensures the translation efficiency and functional stability of the element, but also significantly reduces the metabolic burden, thereby optimizing the adaptability of the element in the host, and providing an efficient hierarchical guidance basis for modular design. By using a modular recombinant vector constructed based on adaptability data, a standardized connection interface and a regulatable promoter are used, which not only improves the compatibility and flexibility between elements, but also simplifies the complexity of experimental construction. Regulatable promoters provide a broad design space for dynamic regulation and conditional expression, while standardized connection interfaces accelerate the vector optimization and verification process. Single-cell separation and real-time monitoring are achieved through microfluidic droplet technology, avoiding the interference of group effects on the data. At the same time, the growth status and element expression level of single cells are dynamically monitored through fluorescent signals, providing a guarantee for high-throughput and high-precision screening. This method not only obtains the dynamic expression characteristics of functional elements, but also fully reveals cell heterogeneity, providing profound insights for optimizing element combinations. Based on single-cell expression profile data, by combining indicators such as growth rate, product accumulation rate, and metabolic flux changes, the functional elements are comprehensively evaluated and screened in multiple dimensions to generate a functional element feature map. The map intuitively shows the differences in element performance, providing a reliable guide for subsequent optimization combination design. The efficient element combination finally screened out can greatly improve the overall performance of the target metabolic pathway. Overall, this method significantly improves the efficiency and transformation potential of metabolic engineering research through precise functional requirement analysis, hierarchical adaptability optimization, modular vector design, high-throughput single-cell monitoring, and multi-dimensional component performance evaluation, providing a scientific, systematic, and efficient solution for the design and optimization of complex metabolic pathways.

[0027] Preferably, step S1 comprises the following steps: Step S11: obtaining functional requirement data of the target metabolic pathway; When the embodiment of the present invention obtains the functional requirement data of the target metabolic pathway, the key metabolic reactions and the functions of the enzymes involved in the metabolic pathway can be clarified by consulting relevant literature, databases (such as KEGG and MetaCyc), and consulting experts in the field. For example, in the case where the target metabolic pathway involves amino acid synthesis, it can be confirmed that the required functional requirements are to increase the production of specific amino acids (such as glutamine), and to clarify the key enzymes in the pathway, such as glutamine synthetase. Based on the above information, a detailed functional requirement table is constructed, including reaction substrates, products, required enzymes and their possible cofactors.

[0028] Step S12: using a sequence alignment algorithm to search for homologous sequences on the preset genome sequence data according to the functional requirement data, thereby obtaining initial candidate sequence data; The embodiment of the present invention uses a sequence alignment algorithm to search for homologous sequences on preset genome sequence data. The BLAST (Basic Local Alignment Search Tool) tool can be used to set a similarity threshold (such as an E value less than 1e-5) and an alignment score threshold (such as greater than 50 points) for the target sequence to perform efficient alignment; the preset genome sequence data can be obtained from public databases such as NCBI and ENSEMBL. Taking the search for gene sequences related to glutamine synthetase as an example, the reference sequence of glutamine synthetase (such as P00937) is used as input, and the initial candidate sequence data is obtained through BLAST search.

[0029] Step S13: Based on the initial candidate sequence data, multiple sequence alignment and phylogenetic analysis are performed on homologous gene sequences of different species, and an evolutionary tree is constructed using the maximum likelihood method and a bootstrap test is performed to obtain gene evolution distance distribution data; In the embodiment of the present invention, Clustal Omega or MUSCLE tools can be used to perform multiple sequence alignment based on the initial candidate sequence data. After the candidate gene sequences are aligned, they are input into IQ-TREE software for phylogenetic analysis; the maximum likelihood method (ML method) is used to construct an evolutionary tree, and the number of repetitions (such as 1000 times) is set by the bootstrap method to evaluate the credibility of the evolutionary tree. Taking the glutamine synthetase gene in microorganisms as an example, its distribution and evolutionary distance in bacteria, archaea and eukaryotes are analyzed to obtain gene evolutionary distance distribution data.

[0030] Step S14: performing protein domain annotation and conservation analysis on the sequence according to the gene evolution distance distribution data, and predicting the boundaries of the functional domains, thereby obtaining domain conservation distribution data; According to the gene evolution distance distribution data, the embodiment of the present invention uses the InterProScan tool to annotate the domain of the protein sequence of the candidate gene, identify the functional domain, and perform conservation analysis through the HMMER software; for domain boundary prediction, the DOMINE database and Pfam data set can be used in combination with sequence analysis tools (such as CD-HIT) to predict the boundary position and function of the domain. Taking glutamine synthetase as an example, the conservation of its ATP binding site and active center can be quantified by a scoring system (such as a score greater than 95%) to generate domain conservation distribution data.

[0031] Step S15: performing sequence classification based on the type and combination pattern of functional domains through the domain conservation distribution data, thereby obtaining functional domain classification data; The embodiment of the present invention classifies the sequences according to the types and combination patterns of functional domains through the domain conservation distribution data, and uses the Hierarchical Clustering method to cluster the sequences based on the domain characteristics to divide them into different functional groups; the classification accuracy is further confirmed by combining similarity analysis tools (such as OrthoFinder) to obtain functional domain classification data. Taking glutamine synthetase as an example, unique functional domain combination patterns in different bacterial groups can be distinguished, such as the presence of specific regulatory regions or catalytic regions.

[0032] Step S16: Based on the functional domain classification data, the functional association of the category sequence is predicted using a sequence similarity network analysis method, and annotation is performed to obtain candidate element group data with functional association.

[0033] The embodiment of the present invention predicts the functional relevance of sequences based on functional domain classification data using Cytoscape combined with sequence similarity network analysis methods; in network construction, the similarity relationship between sequences is represented by weighted edges, and the weight value is determined according to the comparison score (such as sequence similarity greater than 70%); functional association clusters are identified through network clustering algorithms (such as MCL algorithms), and further combined with Gene Ontology (GO) or UniProt databases for functional annotation, and finally generate candidate element population data with functional relevance. Taking the study of glutamine synthetase as an example, network analysis can reveal the conserved functional regions of the enzyme in different species and its possible auxiliary factor binding sites.

[0034] The present invention significantly improves the accuracy and reliability of the screening of functional elements of the target metabolic pathway by layer-by-layer refinement and optimization. The functional requirement data of the target metabolic pathway is clarified, providing clear directions and constraints for subsequent screening. The genome sequence is searched for homologous sequences through a sequence alignment algorithm, and the initial candidate sequence data that meets the functional requirements are efficiently screened out, greatly reducing the interference of irrelevant sequences. Through multiple sequence alignment and phylogenetic analysis, the maximum likelihood method is used to construct an evolutionary tree and the bootstrap method is used to test it, thereby improving the accuracy of evolutionary distance analysis, thereby generating accurate gene evolutionary distance distribution data. Based on the evolutionary distance data, protein domain annotation and conservation analysis are performed, and combined with the prediction of functional domain boundaries, domain conservation distribution data is generated, revealing the functional potential of the candidate sequence and its connection with the conservation of the domain. Sequence classification is performed through domain conservation distribution data, and functional domain classification data is generated based on the type and combination pattern of the functional domain, which lays the foundation for the accurate classification and diversity analysis of functional elements. Finally, the functional relevance is predicted by the sequence similarity network analysis method through the functional domain classification data, and the elements are annotated, thereby obtaining candidate element population data with clear functions and strong relevance. The overall process combines multi-level bioinformatics analysis methods to achieve effective transformation from data to knowledge, from initial candidate sequences to precise screening of functional elements, providing solid technical support for the design and optimization of metabolic engineering.

[0035] Preferably, step S13 comprises the following steps: Step S131: establishing a multiple sequence alignment matrix based on the initial candidate sequence data, and performing progressive pairwise alignment using the ClustalW algorithm to obtain sequence consistency score data; When the embodiment of the present invention establishes a multiple sequence alignment matrix based on the initial candidate sequence data, the progressive alignment method in the ClustalW algorithm is used for pairwise alignment. First, the initial candidate sequences (such as protein sequences or nucleotide sequences) are aligned in pairs, and the matching scores, mismatch scores, and insertion / deletion (Gap) scores of each pair of sequences are calculated to generate an alignment score matrix. Then, based on the scores in the matrix, the adjacency relationship is gradually constructed to form sequence consistency score data. For example, when aligning 10 candidate sequences, the Gap opening penalty can be set to 10, the extension penalty can be set to 0.2, and Blosum62 can be selected as the initial score matrix, and finally a consistency score table between each pair of sequences is obtained for subsequent optimization.

[0036] Step S132: performing Gap penalty optimization and weight matrix adjustment on the comparison result based on the sequence consistency score data, and calculating sequence similarity using the PAM250 substitution matrix, thereby obtaining sequence alignment data; The embodiment of the present invention performs Gap penalty optimization and weight matrix adjustment on the comparison results based on sequence consistency score data. First, the Gap position is corrected by dynamic programming, the Gap region with a lower score is repositioned, and the penalty weight of the Gap is adjusted by a linear optimization strategy (such as adjusting the Gap opening penalty from 10 to 8, and the extension penalty from 0.2 to 0.1). Then, the sequence similarity is recalculated using the PAM250 substitution matrix, which reflects the accumulated substitution probability over evolutionary time; for protein sequences, the comparison results are updated by calculating the probability score of each pair of amino acid substitutions. For example, after optimization, the comparison score of a set of enzyme-related sequences was improved by 15%, thereby generating more accurate sequence comparison data.

[0037] Step S133: calculating the evolutionary distance between sequences based on the sequence alignment data, and correcting the nucleotide substitution rate using the Kimura two-parameter model to obtain genetic distance matrix data; The embodiment of the present invention calculates the evolutionary distance between sequences based on sequence alignment data and uses the Kimura two-parameter model to correct the nucleotide substitution rate. The specific steps are: first, the frequency of synonymous substitutions (such as purine to purine) and non-synonymous substitutions (such as purine to pyrimidine) in the sequence alignment is counted, and based on the Kimura formula , calculate the corrected substitution rate, where is the synonymous substitution frequency, is the frequency of non-synonymous substitutions. The results are organized into genetic distance matrix data. For example, after aligning 20 nucleotide sequences, a 20×20 symmetric matrix is ​​generated, in which each cell represents the genetic distance between a pair of sequences, and the value range is usually 0.01 to 0.5.

[0038] Step S134: construct a phylogenetic tree based on the genetic distance matrix data using the maximum likelihood method to obtain gene evolution distance distribution data, wherein the branch length of the phylogenetic tree reflects the evolutionary distance between sequences, and the support rate of the branch node is calculated by the bootstrap method.

[0039] The embodiment of the present invention uses the maximum likelihood method to construct a phylogenetic tree based on genetic distance matrix data, and is implemented using tools such as MEGA or RAxML. The specific operation is: import the genetic distance matrix into the software, perform iterative optimization through the maximum likelihood method (ML method), and calculate the branch length of the phylogenetic tree. In order to evaluate the node support rate, the bootstrap method (Bootstrap) is used to set the number of repetitions (such as 1000 times) to generate a phylogenetic tree with higher stability. Ultimately, the branch length reflects the evolutionary distance between sequences, and nodes with a support rate of more than 90% are considered reliable. For example, when analyzing the evolutionary relationship of certain enzyme genes, the constructed phylogenetic tree shows that the cluster branch lengths of bacteria, archaea, and eukaryotic genes are 0.3, 0.4, and 0.5, respectively, and the support rates are all over 95%, thereby obtaining highly reliable gene evolution distance distribution data.

[0040] The present invention provides comprehensive and reliable technical support for gene evolution analysis by constructing a multiple sequence alignment matrix, optimizing the alignment algorithm, and accurately calculating the evolutionary distance. The ClustalW algorithm is used to perform progressive pairwise alignment on the initial candidate sequence data, effectively generating sequence consistency score data, and ensuring the accuracy and comprehensiveness of the alignment results. Gap penalty optimization and weight matrix adjustment are performed based on the consistency score data, and the PAM250 substitution matrix is ​​introduced to calculate the sequence similarity, which further improves the accuracy of the alignment results and lays a solid foundation for evolutionary analysis. The nucleotide substitution rate is corrected by the Kimura two-parameter model, and the genetic distance between sequences is accurately calculated, so as to generate scientific and rigorous genetic distance matrix data, which provides key parameters for subsequent phylogenetic analysis. The maximum likelihood method is used to construct a phylogenetic tree based on the genetic distance matrix, and the branch length intuitively reflects the evolutionary distance between sequences. At the same time, the support rate of the branch node is calculated by the bootstrap method, which enhances the credibility and explanatory power of the phylogenetic tree. The synergistic effect of this series of steps not only improves the accuracy of evolutionary distance analysis, but also provides high-quality data support and systematic analysis methods for the screening and function prediction of candidate elements, which greatly helps the research and application of metabolic engineering.

[0041] Preferably, step S14 comprises the following steps: Step S141: predicting the domain of the sequence according to the gene evolution distance distribution data, and annotating the domain through the Pfam database, thereby obtaining initial domain annotation data; When the embodiment of the present invention predicts the domain of the sequence based on the gene evolutionary distance distribution data, the gene sequence is first input into the HMMER tool in FASTA format, and the Pfam database is selected as the annotation reference. The Pfam database contains about 19,000 predefined domain models, which are built based on the hidden Markov model (HMM). When running the HMMER scan, the e-value (significance value) threshold is set to 1e-5, and the "--cut_ga" parameter is turned on to use the threshold value recommended by Pfam. The scanning results include the start position, end position and alignment score of each domain. For example, when analyzing a group of sugar metabolism genes from eukaryotic organisms, HMMER detected multiple functional domains, including 6 nucleotide binding domains and 4 transferase activity domains, with the start and end sites being 30-200 and 250-400, respectively. These initial domain annotation data will serve as the basis for subsequent analysis.

[0042] Step S142: performing sequence conservative motif search on the initial domain annotation data, setting the minimum motif length to 6 amino acids, the maximum motif length to 50 amino acids, and setting the P-value threshold to 0.05, thereby obtaining conservative motif distribution data; When searching for conserved motifs in the initial domain annotation data, the embodiment of the present invention uses the MEME tool to detect conserved motifs in the sequence. The specific steps include: uploading sequence data in FASTA format to the MEME online server or local operating environment; setting the minimum motif length to 6 amino acids, the maximum motif length to 50 amino acids, selecting the 0-order background model, and setting the P-value threshold to 0.05 to screen significant motifs; after starting the search task, MEME will output the specific location information and LOGO graphic representation of multiple significant motifs. For example, when analyzing an enzyme gene involved in amino acid metabolism, MEME identified 4 significant conserved motifs, one of which was a 10-amino acid motif (MGMGXXXGKG) that existed in more than 70% of the samples and was located near the active site of the enzyme, and its function may be related to substrate binding. These conserved motif distribution data will be used to construct subsequent hidden Markov models.

[0043] Step S143: constructing a domain-specific hidden Markov model based on the conserved motif distribution data, optimizing the model parameters through multiple rounds of iterative training until a preset convergence threshold is reached, thereby obtaining domain prediction model data; When constructing a domain-specific hidden Markov model (HMM) according to the conservative motif distribution data in the embodiment of the present invention, the hmmbuild function in the HMMER tool is used to create the initial model. The multiple sequence alignment results of the conservative motifs are used as input, and the initial HMM model includes a state transition matrix, an emission probability matrix, and an insertion / deletion probability. Subsequently, the model is iteratively trained for multiple rounds in the hmmbuild tool using the Baum-Welch algorithm, and the model parameters are updated in each round. The convergence condition is set to a log-likelihood increment of less than 1e-4, and it can usually converge within 20 rounds. During the training process, the sensitivity and specificity of the model are verified by simulating a test set. For example, when analyzing the conservative motifs of enzymes related to antibiotic synthesis, the sensitivity of the initial model is 85%. Through parameter optimization, the sensitivity is increased to 93%, and the error recognition rate is reduced to 7%. The final output HMM model will be used for subsequent domain prediction.

[0044] Step S144: locating the domain boundary in the sequence based on the domain prediction model data, and performing local scoring by a sliding window method, thereby obtaining domain boundary prediction data; When locating the domain boundaries in the sequence based on the domain prediction model data, the embodiment of the present invention adopts the sliding window method for local scoring. The specific operation includes: using the trained HMM model to scan the sequence bit by bit, setting the sliding window length to 15 amino acids and the step length to 1. The alignment score of each window is calculated, and the area with the highest score is screened out as the candidate boundary area; the candidate area is further refined, and the exact boundary position is determined by comparing the score difference between the two windows before and after. For example, when analyzing a polymerase sequence derived from soil bacteria, the sliding window method identified two boundary areas, whose start and end sites were 35-85 and 120-200, respectively. On this basis, experiments were further used to verify that these areas are related to functional activity.

[0045] Step S145: performing conservation score evaluation on each domain according to the domain boundary prediction data, thereby obtaining domain conservation score data, wherein the conservation score evaluation specifically uses Jensen-Shannon divergence to evaluate the degree of conservation of sites in sequence alignment; When evaluating the conservation score of each domain according to the domain boundary prediction data, the embodiment of the present invention uses Jensen-Shannon divergence (JSD) as a conservation measurement method. The specific steps are: extracting the amino acid frequency distribution of each site in the domain alignment results, calculating the JSD value between the distribution and the background distribution (assuming a random sequence); the JSD value ranges from 0 to 1, where the higher the value, the more conservative the site; outputting the JSD value of each site and generating a visual conservation heat map. For example, when analyzing a protein kinase domain, the results show that the JSD value of the core catalytic region is above 0.9, while the JSD value of the peripheral inactive region is less than 0.3. These conservation scores can be used to infer the location of the active site, providing a reference for subsequent functional verification.

[0046] Step S146: Use the domain conservation score data to perform functional verification on each predicted domain, thereby obtaining domain conservation distribution data.

[0047] When the embodiment of the present invention uses the domain conservation score data to perform functional verification on each predicted domain, the sequence of the highly conserved region is first submitted to the UniProt database for cross-functional annotation to extract information related to known functions. Subsequently, recombinant plasmids expressing these highly conserved domains are designed in the laboratory and tested by in vitro expression and functional verification experiments. The specific method includes: expressing the domain protein in Escherichia coli, obtaining the target protein by affinity purification method; performing experimental verification on known functions, such as substrate binding experiments, enzyme activity assays, or protein-protein interaction detection. In one case, the function of a highly conserved domain of a transport protein was verified, and it was found that it could efficiently bind to substrate molecules with a binding constant of 1.2 μM, which was consistent with the calculated results, confirming that it was a functional key domain. These results are integrated into domain conservation distribution data for subsequent optimization design.

[0048] The present invention realizes efficient prediction, precise positioning and functional verification of gene domains by combining multiple bioinformatics methods, and provides important support for the screening of key components of metabolic pathways. The Pfam database is used to annotate the gene evolutionary distance distribution data with domains, and the initial domain annotation data is generated, which lays the foundation for subsequent analysis. By setting the minimum motif length and P-value threshold, a systematic search is performed on the conservative motifs to generate conservative motif distribution data, thereby revealing the core features with functional relevance in the sequence. By constructing a hidden Markov model and performing multiple rounds of iterative training, the accuracy and stability of the model parameters are optimized, and reliable domain prediction model data are generated, providing technical support for boundary positioning. Based on the prediction model, the domain boundaries are accurately positioned and locally scored using the sliding window method, and high-resolution domain boundary prediction data is generated, which provides specific boundary information for further analysis of functional elements. The conservation score is calculated by Jensen-Shannon divergence, the sequence conservation is quantitatively evaluated, and the domain conservation scoring data is generated, revealing the key sites with the strongest functional relevance in the sequence. Based on the conservation score data, the domains are functionally verified to generate high-quality domain conservation distribution data, thereby comprehensively evaluating the functional properties and evolutionary significance of the domains. The coordinated optimization of this series of steps significantly improves the accuracy and biological significance of domain prediction, provides a systematic, precise and efficient solution for the design and optimization of functional elements in metabolic engineering, and promotes the research and application of complex biological networks.

[0049] Preferably, step S2 comprises the following steps: Step S21: Acquire the expression system data of the host cell; When obtaining the expression system data of the host cell in the embodiment of the present invention, Escherichia coli (E. coli BL21) is selected as the host cell, and its expression system data is collected by RNA sequencing (RNA-Seq) and proteomics technology. The specific operations include: 1) extracting the total RNA of the host cell in the logarithmic growth phase, and using the Illumina platform to perform RNA sequencing to obtain the transcript data within its genome; 2) using mass spectrometry (LC-MS / MS) technology to analyze the protein expression profile of the host cell, and combining databases such as UniProt and NCBIRefSeq to perform functional annotations on the protein; 3) further through flux metabolomics experiments, gas chromatography-mass spectrometry (GC-MS) is used to detect the types and concentrations of core metabolites of the host cell. Taking the study of Escherichia coli as an example, a total of 4,560 gene transcripts and 45 corresponding metabolites in its expression system were detected, including important glycolysis and tricarboxylic acid cycle metabolic pathway data.

[0050] Step S22: performing transcription, translation and metabolic characteristic analysis on the host cell expression system data, and establishing a cell expression capacity model, thereby obtaining host cell expression characteristic data; When analyzing the transcription, translation, and metabolic characteristics of the host cell expression system data, the embodiments of the present invention use BioPython and CobraPy tools for computational analysis. First, a transcription network model of the host cell is constructed to analyze the relationship between the transcription level of each gene and the translation efficiency of its encoded protein; then, the translation initiation efficiency is predicted by calculating the strength of the ribosome binding site, and a metabolic network model is constructed in combination with known metabolic pathway information. For each metabolic pathway, the energy and raw material consumption rates are calculated to evaluate the expression capacity of the host cell. For example, in the analysis of Escherichia coli, it was found that the generation rate of phosphoenolpyruvate (PEP) in the core metabolic pathway was 15 , which is related to the protein translation rate, and the model results show that its expression capacity is 20μg / mL.

[0051] Step S23: performing synonymous codon usage frequency and GC content distribution analysis on the codons in the candidate element population data based on the host cell expression characteristic data, thereby obtaining codon optimization degree data; When the embodiment of the present invention performs synonymous codon usage frequency and GC content distribution analysis on the codons of the candidate element population data based on the host cell expression feature data, a Python script is used to read the gene sequence data, the usage frequency of each synonymous codon is counted, and the frequency distribution is visualized as a bar graph. The GC content distribution is completed by calculating the GC ratio of each sequence, setting the window length to 100bp and the step length to 10bp. The statistical results are compared with the codon preferences of the host cell, and the similarity score is calculated. For example, the analysis of the sequences of 4 functional elements shows that the GC content of element No. 3 is 42%, which has the highest match with the optimal range of the host cell (38%-44%), and its optimization score is 0.85.

[0052] Step S24: performing secondary structure and three-dimensional structure simulation of the protein based on the Rosetta energy function according to the codon optimization data, and calculating the instability index and folding free energy of the protein, thereby obtaining protein stability data; When the embodiment of the present invention simulates the secondary structure and three-dimensional structure of the protein based on the Rosetta energy function according to the codon optimization data, the Rosetta3.13 tool is used to model the target sequence. The optimized protein sequence is input into the Relax module of Rosetta to calculate its lowest energy conformation; then, the DSSP algorithm is used to predict the secondary structure ratio, and the PyMOL software is used to visualize the protein three-dimensional structure model. Finally, the ProtParam tool is used to calculate the instability index and folding free energy of the protein. Taking an enzyme protein as an example, the predicted secondary structure ratio is Spiral accounts for 45%, Folding accounted for 30%, the final calculated instability index was 28 (stable protein range <40), and the folding free energy was -3.5 kcal / mol, indicating high stability.

[0053] Step S25: evaluating the cellular metabolic load during the element expression process based on the protein stability data, thereby obtaining metabolic load prediction data, wherein the cellular metabolic load includes energy consumption, amino acid consumption and cofactor requirements; When evaluating the cellular metabolic load during element expression based on protein stability data, the embodiment of the present invention first compares the amino acid composition and molecular weight of the protein with the amino acid metabolic pathway data of the host cell to calculate the amino acid consumption rate; secondly, the metabolic model is used to simulate the effect of protein expression on energy consumption (ATP demand) and cofactor (such as NADPH) demand. Taking energy consumption as an example, assuming that the molecular weight of the target protein is 50kDa, translating 1mg of protein requires about 4.1mmolATP, and combining the expression amount to predict its cellular metabolic load is 12.3mmolATP / h. The analysis results show that highly expressed target proteins have a significant effect on cellular metabolism.

[0054] Step S26: performing weighted calculation on the codon optimization degree, protein stability and metabolic load of each functional element according to the metabolic load prediction data, protein stability data and codon optimization degree data, thereby obtaining element comprehensive score data; When the embodiment of the present invention performs weighted calculation on each functional element according to metabolic load prediction data, protein stability data and codon optimization data, the weight of each indicator is set to 30% codon optimization, 40% protein stability and 30% metabolic load. Each data is converted into a score value within the range of 0-1 by normalization, and the final score is calculated comprehensively using a weighted formula. Taking the analysis of a candidate element as an example, its codon optimization score is 0.85, the protein stability score is 0.90, the metabolic load score is 0.75, and the comprehensive score is 0.85×0.3+0.90×0.4+0.75×0.3=0.835, which is classified as a high-scoring element.

[0055] Step S27: Based on the comprehensive component score data, a threshold range is set, and the components with scores in the top 30% are classified into a high expression group, the middle 40% of the components are classified into a medium expression group, and the bottom 30% of the components are classified into a low expression group, thereby obtaining hierarchical component adaptability data.

[0056] When setting the threshold range based on the comprehensive score data of the component, the embodiment of the present invention uses a statistical method to calculate the quantiles of the comprehensive score data, and takes the first 30%, the middle 40%, and the last 30% quantiles as the grouping thresholds. For example, a study contains 100 components, and its comprehensive score range is 0.6 to 0.95. The first 30% of the components have a score range of 0.88-0.95, which is classified as the high expression group; the middle 40% have a score range of 0.77-0.87, which is classified as the medium expression group; the last 30% have a score range of 0.60-0.76, which is classified as the low expression group. The results show that the high expression group elements are more suitable for efficient protein production on an industrial scale.

[0057] The present invention provides comprehensive guidance for the fitness evaluation and optimization of elements by integrating the characteristics of the host cell expression system with the multidimensional properties of the candidate functional elements. The expression system data of the host cell is obtained to provide basic information for subsequent analysis; the expression system data is transcribed, translated and metabolic characteristics are analyzed, and a cell expression capacity model is established to generate host cell expression characteristic data, thereby accurately characterizing the biological characteristics of the host cell in terms of expression ability. Based on the host cell expression characteristic data, the codons of the candidate elements are optimized and analyzed to obtain codon optimization data, which provides a key reference for improving gene expression efficiency. The secondary and three-dimensional structures of proteins are simulated by the Rosetta energy function, the instability index and folding free energy of proteins are evaluated, and protein stability data are generated to ensure that the target protein has good structural stability. Protein stability data is used to evaluate the metabolic load of element expression on host cells, including energy, amino acids and cofactor requirements, and metabolic load prediction data is generated to provide support for the selection of low metabolic pressure elements. Weighted calculations are performed in combination with codon optimization data, protein stability data and metabolic load prediction data to generate element comprehensive scoring data, which lays a scientific basis for the fitness classification of functional elements. The grouping threshold is set based on the comprehensive score, and the elements are stratified into high expression group, medium expression group and low expression group to generate hierarchical element fitness data, which provides clear guidance for the optimal combination of functional elements. This series of steps establishes a systematic element fitness analysis framework by comprehensively evaluating codon optimization, protein stability and metabolic load, significantly improving the efficiency and stability of gene expression, and providing a scientific and operational solution for the design and implementation of metabolic engineering.

[0058] Preferably, step S3 comprises the following steps: Step S31: analyzing the original expression vector of the host cell according to the element compatibility data, and obtaining the vector backbone compatibility data by evaluating the compatibility of the replicon, resistance marker and expression regulatory element of the vector; When the embodiment of the present invention analyzes the original expression vector of the host cell according to the element adaptability data, the expression vector commonly used in the host cell (such as the pET series vector) is first selected as the analysis object. The replicon sequence of the vector is determined by DNA sequencing technology, and the online tool PlasmidFinder is used to evaluate its replicon type (such as high copy or low copy). Then, the resistance marker gene (such as AmpR or KanR) is annotated using the NCBI database to analyze its effect on the host cell resistance gene background. In addition, by comparing the host cell genome sequence, it is determined whether the expression regulatory elements (such as T7 promoter, lac operator) have endogenous competition or compatibility problems. The analysis results show that the pET-28a vector contains a high copy replicon pBR322, the resistance marker KanR is compatible with the host cell background, the promoter T7 has no similar competitive elements in the host genome, and the adaptability score is 0.92.

[0059] Step S32: designing a regulatable promoter system based on the vector backbone adaptability data, and obtaining promoter regulation module data by constructing a combination sequence of an inducer response element and a transcription factor binding site; When designing a regulatable promoter system based on the vector skeleton adaptability data, the embodiment of the present invention uses the Gibson splicing method to construct a combined sequence, including designing a set of inducer response elements (such as IPTG or arabinose response elements) and corresponding transcription factor binding sites (such as lacI or araC binding sequences). The specific operations include: 1) inserting a modified T7 promoter with an IPTG inducible response element on the vector skeleton; 2) synthesizing a promoter sequence containing a lacI binding site and connecting it to the upstream of the target gene; 3) adding different concentrations of inducer (0.1-1mMIPTG) in the test phase, and measuring the expression level of fluorescent protein to evaluate the regulatory ability of the promoter. The final designed promoter module showed optimal induction efficiency at a concentration of 0.5mMIPTG, and the ratio of induced expression to background expression reached 50 times.

[0060] Step S33: designing a standardized functional element connection interface according to the promoter regulatory module data, including an upstream ribosome binding site sequence and a downstream bidirectional transcription terminator sequence, thereby obtaining connection interface sequence data; When designing a standardized functional element connection interface according to the promoter regulatory module data, the embodiment of the present invention selects a commonly used ribosome binding site (RBS) sequence (such as B0034) as the upstream connection sequence, and verifies its compatibility through Primer-BLAST. The downstream connection selects a widely used bidirectional transcription terminator sequence (such as T1T2 terminator) to ensure the efficiency and directionality of transcription termination. The specific implementation includes: 1) using gene synthesis technology to generate a combination sequence of RBS-B0034 and the target promoter module; 2) adding a bidirectional terminator T1T2 downstream of the target gene; 3) using a standardized test vector to verify the function of the connection interface, and confirming its transcription and translation efficiency through fluorescence detection. Experimental results show that the translation efficiency of the designed connection interface sequence when expressing red fluorescent protein (RFP) is increased by about 35%.

[0061] Step S34: modular assembly of the vector is performed based on the ligation interface sequence data, and a standardized assembly process is established through restriction site optimization and ligation efficiency evaluation, thereby obtaining assembly strategy data; When the embodiment of the present invention performs modular assembly of the vector based on the connection interface sequence data, the restriction sites (such as EcoRI, XbaI, SpeI and PstI) for modular assembly are first selected, and potential non-specific restriction sites in other regions of the vector are eliminated by introducing mutations. Subsequently, the Golden Gate splicing technology is used to achieve rapid assembly of the module through a single-tube reaction. The specific steps include: 1) preparing linearized fragments of the target promoter module, the connection interface module and the functional element module; 2) adding BsaI enzyme and T4 DNA ligase to the reaction system; 3) digesting at 37°C for 1 hour, followed by connection at 16°C for 1 hour to complete the modular assembly; 4) verifying the integrity of the assembly by agarose gel electrophoresis, and the test results show that the assembly efficiency reaches 95%, and its expression performance is confirmed in subsequent experiments.

[0062] Step S35: Directedly assemble the regulatable promoter module, the connection interface sequence and the functional element sequence according to the assembly strategy data, thereby obtaining modular recombinant vector data.

[0063] When the embodiment of the present invention performs directed assembly of the regulatable promoter module, the connection interface sequence and the functional element sequence according to the assembly strategy data, the CRISPR-Cas9-assisted assembly technology is used to achieve high-precision recombination. The specific operations include: 1) designing the gRNA target on the vector backbone to locate the insertion position of the promoter module; 2) introducing the Cas9 protein and the corresponding gRNA complex into the host cell by electroporation; 3) adding the donor DNA of the target module to the same reaction system, and completing the directed assembly by using the Cas9 cutting and cell homologous recombination mechanism; 4) screening the assembled positive clones on the resistance plate, and verifying their accuracy and directionality by sequencing. The results showed that the expression level of the successfully assembled modular recombinant vector in the target host cell increased by 2 times, and the stability of expression increased by about 40%.

[0064] The present invention significantly optimizes the construction efficiency and functional adaptability of the expression vector through modular design and standardized assembly strategy. Based on the element adaptability data, the original expression vector of the host cell is analyzed, and the compatibility of the replicon, resistance marker and expression control element of the vector skeleton is evaluated to obtain the vector skeleton adaptability data, which provides precise guidance for the optimization design. The vector skeleton adaptability data is used to design a regulatable promoter system, and the promoter regulation module data is generated by constructing a combination sequence of an inducer response element and a transcription factor binding site to achieve flexible regulation of gene expression. The standardized functional element connection interface is further designed, including an upstream ribosome binding site sequence and a downstream bidirectional transcription terminator sequence, to ensure the connection stability and expression efficiency between different functional elements, and to generate connection interface sequence data. A modular assembly process is established based on the connection interface sequence data, and efficient assembly strategy data is constructed through enzyme cutting site optimization and connection efficiency evaluation, which greatly improves the assembly efficiency and accuracy of the vector. Finally, the assembly strategy data is used to assemble the regulatable promoter module, the connection interface sequence and the functional element sequence in a directed manner to generate modular recombinant vector data, which provides a flexible and scalable tool for different metabolic engineering needs. The synergistic effect of this series of steps not only realizes the modular design and standardized assembly of expression vectors, but also improves the adaptability of vectors in different host cells, reduces the time and resource investment in experimental design and verification, and provides efficient and reliable technical support for the implementation of complex metabolic engineering.

[0065] Preferably, step S4 comprises the following steps: Step S41: culturing and amplifying the transformed cell population according to the modular recombinant vector data, thereby obtaining cell suspension data; When the transformed cell population is cultured and amplified according to the modular recombinant vector data in the embodiment of the present invention, the host cells containing the modular recombinant vector are first inoculated into LB culture medium containing the corresponding antibiotics (such as 50µg / mL kanamycin), and cultured at 200rpm in a 37°C shaker for 16 hours to obtain a seed solution. Subsequently, the seed solution is inoculated into fresh culture medium at a ratio of 1:100 and cultured under the same conditions until the OD600 value reaches 0.6-0.8. During this period, samples are taken every 1 hour to measure the cell density to ensure that the cells are in the logarithmic growth phase. Then, the cell precipitate is collected by centrifugation (5000rpm, 4°C, 10 minutes), washed twice with PBS buffer and resuspended to finally obtain a concentration of 1× Prepare a cell suspension of 10 cells / mL for later use.

[0066] Step S42: Based on the cell suspension data, the uniformity of the droplet size of the microfluidic droplets is controlled by adjusting the flow rate, shear force and surfactant concentration, so as to obtain droplet encapsulation parameter data; When the embodiment of the present invention controls the size uniformity of microfluidic droplets based on cell suspension data, experiments are conducted using a commercial microfluidic chip (chip channel width is 50µm) and its supporting equipment. The cell suspension and the oil phase (mineral oil containing 2% surfactant Span 80) are injected into the chip inlet through a syringe pump at a flow rate ratio of 1:10 (cell suspension flow rate 0.5µL / min, oil phase flow rate 5µL / min) to form monodisperse water-in-oil droplets. The droplet diameter is monitored by a microscope, and the surfactant concentration (2%-5%) and shear force (adjusting the flow rate ratio) are optimized to obtain droplets with higher uniformity. Finally, by adjusting the shear force to 1.5Pa and the surfactant concentration to 3%, the droplet diameter is successfully controlled within the range of 50±2µm.

[0067] Step S43: encapsulating the cell group into a microfluidic droplet according to the droplet encapsulation parameter data, and ensuring that each droplet contains only a single cell through Poisson distribution control, thereby obtaining single cell separation data; When encapsulating a cell population into a microfluidic droplet according to the droplet encapsulation parameter data, the single cell encapsulation process is controlled by Poisson distribution. The optimized cell suspension is diluted to 1× cells / mL, and the syringe pump flow rate was adjusted to maintain a droplet generation rate of 1000 drops / second. The Poisson distribution theory was used to calculate the probability of each droplet containing a single cell (approximately 36.8%), and the probability of multi-cell encapsulation was reduced by diluting the cell concentration. The prepared droplets were detected by fluorescence microscopy, and statistics showed that approximately 368 droplets encapsulated single cells per 1000 droplets, which was consistent with the Poisson distribution prediction. The resulting single-cell isolation droplets were collected and stored at 4°C for subsequent analysis.

[0068] Step S44: Real-time monitoring of single cell separation data is performed by setting a multi-channel fluorescent probe and an optical sensor array, thereby obtaining fluorescent signal collection data; The embodiment of the present invention adopts a customized optical sensor system when real-time monitoring of single-cell separation data is performed by setting a multi-channel fluorescent probe and an optical sensor array. The system includes four channels (blue light, green light, red light, far-red light) and corresponding fluorescent probes (such as GFP, mCherry, Cy5). The single-cell droplet passes through the detection channel (width 50µm, flow rate 100µL / min) in the microfluidic chip and passes through the optical sensor array in turn to collect fluorescence signals in real time. The data acquisition frequency is set to 1000 times per second to ensure that the complete signal peak can still be captured when the droplet flow rate is high. The fluorescence signal intensity is displayed and recorded in real time through the LabVIEW program. The fluorescence signal of each cell has a clear characteristic peak, and the data is saved for subsequent analysis.

[0069] Step S45: Draw the growth curve of each single cell according to the fluorescence signal acquisition data, and establish a quantitative relationship between the fluorescence intensity, biomass and expression level, so as to obtain the single cell expression profile data.

[0070] When the embodiment of the present invention draws the growth curve of each single cell according to the fluorescence signal acquisition data, the recorded fluorescence signal data is imported into the MATLAB software for processing. The curve of the fluorescence intensity of each single cell changing with time is drawn by a nonlinear fitting method, and the single cell biomass is calculated according to the standard curve of fluorescence intensity and biomass (established by protein quantification and OD600 determination in the preliminary experiment, R²=0.98). At the same time, the expression level is evaluated in combination with the quantitative relationship of protein expression level (the linear correlation coefficient between fluorescence intensity and target protein concentration is 0.95). Finally, single-cell expression spectrum data is established, including the fluorescence peak, biomass and expression intensity of each cell, and the results show that the expression level has a high cell-to-cell variability.

[0071] The present invention realizes expression regulation and characteristic analysis at the single cell level through high-precision microfluidic technology and fluorescence signal monitoring, and significantly improves the accuracy and efficiency of cell engineering. By culturing and amplifying the transformed cell group, cell suspension data is obtained, providing high-quality cell samples for subsequent microfluidic operations. Using microfluidic technology, based on adjusting flow rate, shear force and surfactant concentration, the uniformity of droplet size is controlled, and droplet encapsulation parameter data is generated, which lays a stable physical foundation for single cell separation. Based on the droplet encapsulation parameter data, the cell group is encapsulated into a microfluidic droplet using Poisson distribution control technology to ensure that each droplet contains only one cell, and single cell separation data is obtained, which effectively avoids interference between cells and improves the reliability of experimental results. Through multi-channel fluorescent probes and optical sensor arrays, single cell separation data is monitored in real time, and fluorescent signal acquisition data is obtained, providing a high-precision means for dynamic monitoring of cell functional status. Based on the fluorescent signal acquisition data, the growth curve of each single cell is drawn, and the quantitative relationship between fluorescence intensity, biomass and expression level is established to generate single cell expression spectrum data, which provides a strong basis for accurately analyzing the heterogeneity between cells and optimizing gene expression conditions. This process not only achieves efficient data acquisition at the single-cell level, but also provides an innovative technical path for the optimal design of functional genes and the analysis of their expression characteristics, significantly promoting the precision and high-throughput development of metabolic engineering and cell factory construction.

[0072] Preferably, step S5 comprises the following steps: Step S51: performing cell density change curve and growth rate distribution analysis on the single cell expression profile data to obtain growth characteristic data; When performing cell density change curve and growth rate distribution analysis on single-cell expression profile data in the embodiment of the present invention, the cell growth curve in the expression profile data is first imported into the ggplot2 package in the R language for plotting analysis. The cell density at different time points is standardized and calculated, the cell density change curve is drawn, and the slope of the curve is fitted to calculate the growth rate. The distribution of the growth rate is analyzed using the kernel density estimation method to obtain the average growth rate and distribution width (such as standard deviation). Taking the target cell population as an example, the mean growth rate is measured to be 0.32. , with a standard deviation of 0.05 , indicating that there is a certain growth heterogeneity within the population, and ultimately generating growth characteristic data.

[0073] Step S52: dynamically monitoring the accumulation process of the target product based on the growth characteristic data, and obtaining product dynamic data by establishing a correlation model between product concentration and time and calculating the product accumulation rate; When the embodiment of the present invention dynamically monitors the accumulation process of the target product based on the growth characteristic data, the growth characteristic data is associated with the product concentration data, and the dynamic change of the concentration of the target product is determined by high performance liquid chromatography (HPLC). In the experiment, 50µL was sampled every 4 hours, filtered and injected into HPLC, and the detection wavelength was set to 280nm and the flow rate was 1.0mL / min. The measurement results showed that the product concentration showed a Sigmoid curve change pattern. Subsequently, a nonlinear fitting model (such as a Logistic model) of product concentration and time was established through Python's scipy library, and the product accumulation rate at each time point was calculated according to the model derivative, and finally the product dynamic data was generated. In the specific measurement, the maximum accumulation rate of the target product was 2.8mg / L·h.

[0074] Step S53: calculating the material conversion rate and energy balance state of key metabolic nodes according to the product dynamic data, thereby obtaining metabolic flux distribution data; When calculating the material conversion rate and energy balance state of key metabolic nodes according to the product dynamic data, the embodiment of the present invention adopts a metabolic network model analysis tool (such as COBRApy) for calculation. The product dynamic data is input into the metabolic model, and the metabolic flux distribution is solved with the accumulation rate of the target product as a constraint. The material conversion rate of the key metabolic nodes is optimized by the linear programming method, and the energy balance state is evaluated in combination with the dynamic data of ATP generation and consumption. Specific analysis shows that under the condition of the highest metabolic flux, the conversion rate of acetyl-CoA in the synthesis pathway of the target product is 8.5 mmol / gDW·h, and the ratio of ATP generation to consumption is maintained at 1.2, indicating that the system is in a state of efficient energy utilization, and finally generates metabolic flux distribution data.

[0075] Step S54: Based on the metabolic flux distribution data, a multi-dimensional evaluation is performed on the expression effect of the functional element based on the cell growth rate, the product accumulation rate and the metabolic flux change, thereby obtaining the element function evaluation data; When the embodiment of the present invention performs a multidimensional evaluation of the expression effect of the functional element based on the metabolic flux distribution data, the cell growth rate, product accumulation rate and metabolic flux change are comprehensively analyzed. The data is processed using Python's pandas library, and a multivariate regression model is used to calculate the contribution rate of each functional element to these indicators. Taking the expression effect of the target functional element as an example, the experiment found that its contribution rate to the product accumulation rate reached 45%, while the effect on the growth rate was small (the contribution rate was 10%). In addition, the analysis showed that the efficiency of the pathway related to the functional element in the metabolic flux distribution was increased by 30%. Combined with these indicators, the element function evaluation data is finally generated for optimizing the design of the next generation of functional elements.

[0076] Step S55: extracting and mapping the sequence features, structural features and functional features of the component function evaluation data, thereby obtaining functional component feature map data.

[0077] When the embodiment of the present invention extracts sequence features, structural features and functional features from the element function evaluation data, bioinformatics analysis tools (such as BLAST, SWISS-MODEL) are used for processing. First, the conserved sequence region of the target element is extracted by BLAST comparison, and a sequence feature map is constructed in combination with multiple sequence alignment analysis. Secondly, a three-dimensional structural model of the target element is generated by SWISS-MODEL, and key active sites are visualized and analyzed by PyMOL to extract structural features. Finally, in combination with the contribution rate of the element to the growth rate and product accumulation in the functional evaluation data, the t-SNE algorithm is used to perform dimensionality reduction analysis on the functional features to generate a functional feature map of the element. In practical applications, the key sequence features of the target element show that the regulatory elements related to efficient expression have significant structural advantages, and finally generate functional element feature map data.

[0078] The present invention provides a scientific basis and accurate evaluation for the optimization of functional elements and the production of target products by combining single-cell expression profile data with dynamic metabolic analysis. By analyzing the cell density change curve and growth rate distribution of single-cell expression profile data, growth characteristic data is obtained, and the dynamic growth characteristics of the cell population under different conditions are clarified, providing basic data support for subsequent evaluation. The accumulation process of the target product is dynamically monitored using growth characteristic data, and a correlation model between product concentration and time is established to calculate the product accumulation rate and generate product dynamic data. This process can capture the dynamic changes of product generation efficiency in real time and provide a key reference for optimizing the production process. According to the product dynamic data, the material conversion rate and energy balance state of key metabolic nodes are further calculated to generate metabolic flux distribution data, revealing the role of functional elements in cell metabolism and their limiting factors from the metabolic network level. Based on the metabolic flux distribution data, the expression effect of the functional element is comprehensively evaluated from multidimensional indicators such as cell growth rate, product accumulation rate and metabolic flux change, and the element function evaluation data is generated to ensure the comprehensiveness and scientificity of the evaluation results. By extracting and mapping the sequence features, structural features and functional features in the component function evaluation data, the functional component feature map data is generated, and the component characteristics are intuitively displayed from the perspective of data visualization, which is convenient for further comparison and optimization. This overall process not only integrates dynamic monitoring and metabolic flux analysis at the single-cell level, but also provides a scientific and comprehensive evaluation system for the design and optimization of functional components, promotes the development of efficient metabolic engineering and precise cell factory construction, and helps to achieve the improvement of product production efficiency and efficient use of resources.

[0079] The present invention also provides a mining system for synthetic biology functional elements, which is used to execute the mining method for synthetic biology functional elements. The mining system for synthetic biology functional elements comprises: The target requirement analysis module is used to obtain the functional requirement data of the target metabolic pathway; based on the functional requirement data, the homologous genes of multiple species are analyzed for evolutionary distance and functional prediction, and the preset genome sequence data are classified based on the conservation of protein domains to obtain candidate element population data with functional relevance; The component adaptability evaluation module is used to obtain the expression system data of the host cell; score each functional element based on the codon optimization degree, protein folding stability and metabolic load prediction according to the candidate element population data and expression system data, and divide the elements into high expression group, medium expression group and low expression group according to the scoring results to obtain hierarchical component adaptability data; A modular vector construction module, used for constructing an expression vector with a regulatable promoter and a standardized connection interface based on the element fitness data, to obtain modular recombinant vector data, wherein the standardized connection interface includes a ribosome binding site for transcription initiation and a bidirectional terminator for transcription termination; The single-cell expression monitoring module is used to encapsulate the cell population in microfluidic droplets for single-cell separation according to the modular recombinant vector data, and to monitor the growth status and element expression level of individual cells in real time through fluorescence intensity to obtain single-cell expression profile data; The functional element screening module is used to screen the functional elements of cell growth rate, product accumulation rate and metabolic flux changes based on single-cell expression profile data to obtain functional element characteristic map data.

[0080] The present invention also provides a storage medium, comprising: Memory for storing computer programs; A processor is used to implement the above-mentioned method for mining synthetic biology functional elements when executing the computer program.

[0081] The present invention realizes efficient optimization of target metabolic pathway design and accurate screening of functional elements through a systematic method, which greatly improves the research and application efficiency of metabolic engineering. The target demand analysis module obtains the functional demand data of the target metabolic pathway, and combines the evolutionary distance analysis and function prediction of homologous genes of multiple species, classifies the preset genome sequence data based on the conservation of protein domains, and generates candidate element population data with functional relevance. The effect of this module is to comprehensively explore potential functional elements and ensure their high correlation with the requirements of the target metabolic pathway. The element adaptability evaluation module combines the expression system data of the host cell, scores the candidate elements from multiple dimensions such as codon optimization, protein folding stability and metabolic load prediction, and efficiently stratifies the elements to generate hierarchical element adaptability data. This module ensures the compatibility of the elements with the host system, thereby improving the expression efficiency and system stability. The modular vector construction module constructs an expression vector with a standardized connection interface based on the element adaptability data, ensures the accurate docking of the regulatable promoter and ribosome binding site with the bidirectional terminator, and generates modular recombinant vector data. This modular design significantly improves the flexibility and standardization of vector construction, laying the foundation for further experiments. The single-cell expression monitoring module uses microfluidics technology to achieve single-cell encapsulation, and realizes high-precision analysis of cell growth status and element expression levels through real-time fluorescence monitoring, generating single-cell expression profile data, and providing high-resolution data support for subsequent functional evaluation. The functional element screening module is based on single-cell expression profile data, comprehensively analyzing cell growth rate, product accumulation rate and metabolic flux changes, and generating functional element characteristic map data. This module provides a comprehensive and accurate basis for the screening of functional elements, significantly improving the efficiency of metabolic pathway design and the feasibility of target product production. The overall process embodies the full-link integration from demand analysis to element screening, providing an efficient and standardized solution for metabolic engineering research, and promoting the further development of industrial biotechnology.

[0082] Therefore, the embodiments should be regarded as illustrative and non-restrictive from all points, and the scope of the present invention is limited by the appended claims rather than the above description, and it is intended that all changes falling within the meaning and range of equivalent elements of the application documents are included in the present invention.

[0083] The above description is only a specific embodiment of the present invention, so that those skilled in the art can understand or implement the present invention. 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 invention. Therefore, the present invention will not be limited to the embodiments shown herein, but should conform to the widest scope consistent with the principles and novel features invented herein.

Claims

1. A method for mining synthetic biology functional elements, characterized in that: The following steps are involved: Step S1: Acquire the functional requirement data of the target metabolic pathway; perform evolutionary distance analysis and functional prediction on homologous genes of multiple species according to the functional requirement data, and classify the preset genome sequence data based on the conservation of protein domains to obtain candidate element population data with functional relevance; Step S2: obtaining expression system data of host cells; According to the candidate element population data and expression system data, each functional element is scored based on codon optimization, protein folding stability and metabolic load prediction, and the elements are divided into high expression group, medium expression group and low expression group according to the scoring results to obtain hierarchical element suitability data; Step S3: constructing an expression vector with a regulatable promoter and a standardized connection interface based on the element adaptability data to obtain modular recombinant vector data, wherein the standardized connection interface includes a ribosome binding site for transcription initiation and a bidirectional terminator for transcription termination; Step S4: encapsulating the cell population in a microfluidic droplet for single cell separation according to the modular recombinant vector data, and monitoring the growth status and element expression level of the single cell in real time by fluorescence intensity to obtain single cell expression profile data; Step S5: Screen the functional elements of cell growth rate, product accumulation rate and metabolic flux changes based on the single-cell expression profile data to obtain functional element characteristic map data.

2. The method for mining synthetic biology functional elements according to claim 1, characterized in that: Step S1 includes the following steps: Step S11: obtaining functional requirement data of the target metabolic pathway; Step S12: using a sequence alignment algorithm to search for homologous sequences on the preset genome sequence data according to the functional requirement data, thereby obtaining initial candidate sequence data; Step S13: Based on the initial candidate sequence data, multiple sequence alignment and phylogenetic analysis are performed on homologous gene sequences of different species, and an evolutionary tree is constructed using the maximum likelihood method and a bootstrap test is performed to obtain gene evolution distance distribution data; Step S14: performing protein domain annotation and conservation analysis on the sequence according to the gene evolution distance distribution data, and predicting the boundaries of the functional domains, thereby obtaining domain conservation distribution data; Step S15: performing sequence classification based on the type and combination pattern of functional domains through the domain conservation distribution data, thereby obtaining functional domain classification data; Step S16: Based on the functional domain classification data, the functional association of the category sequence is predicted using a sequence similarity network analysis method, and annotation is performed to obtain candidate element group data with functional association.

3. The method for mining synthetic biology functional elements according to claim 2, characterized in that: Step S13 includes the following steps: Step S131: establishing a multiple sequence alignment matrix based on the initial candidate sequence data, and performing progressive pairwise alignment using the ClustalW algorithm to obtain sequence consistency score data; Step S132: performing Gap penalty optimization and weight matrix adjustment on the comparison result based on the sequence consistency score data, and calculating sequence similarity using the PAM250 substitution matrix, thereby obtaining sequence alignment data; Step S133: calculating the evolutionary distance between sequences based on the sequence alignment data, and correcting the nucleotide substitution rate using the Kimura two-parameter model to obtain genetic distance matrix data; Step S134: construct a phylogenetic tree based on the genetic distance matrix data using the maximum likelihood method to obtain gene evolution distance distribution data, wherein the branch length of the phylogenetic tree reflects the evolutionary distance between sequences, and the support rate of the branch node is calculated by the bootstrap method.

4. The method for mining synthetic biology functional elements according to claim 3, characterized in that: Step S14 includes the following steps: Step S141: predicting the domain of the sequence according to the gene evolution distance distribution data, and annotating the domain through the Pfam database, thereby obtaining initial domain annotation data; Step S142: performing sequence conservative motif search on the initial domain annotation data, setting the minimum motif length to 6 amino acids, the maximum motif length to 50 amino acids, and setting the P-value threshold to 0.05, thereby obtaining conservative motif distribution data; Step S143: constructing a domain-specific hidden Markov model based on the conserved motif distribution data, optimizing the model parameters through multiple rounds of iterative training until a preset convergence threshold is reached, thereby obtaining domain prediction model data; Step S144: locating the domain boundary in the sequence based on the domain prediction model data, and performing local scoring by a sliding window method, thereby obtaining domain boundary prediction data; Step S145: performing conservation score evaluation on each domain according to the domain boundary prediction data, thereby obtaining domain conservation score data, wherein the conservation score evaluation specifically uses Jensen-Shannon divergence to evaluate the degree of conservation of sites in sequence alignment; Step S146: Use the domain conservation score data to perform functional verification on each predicted domain, thereby obtaining domain conservation distribution data.

5. The method for mining synthetic biology functional elements according to claim 4, characterized in that: Step S2 includes the following steps: Step S21: Acquire the expression system data of the host cell; Step S22: performing transcription, translation and metabolic characteristic analysis on the host cell expression system data, and establishing a cell expression capacity model, thereby obtaining host cell expression characteristic data; Step S23: performing synonymous codon usage frequency and GC content distribution analysis on the codons in the candidate element population data based on the host cell expression characteristic data, thereby obtaining codon optimization degree data; Step S24: performing secondary structure and three-dimensional structure simulation of the protein based on the Rosetta energy function according to the codon optimization data, and calculating the instability index and folding free energy of the protein, thereby obtaining protein stability data; Step S25: evaluating the cellular metabolic load during the element expression process based on the protein stability data, thereby obtaining metabolic load prediction data, wherein the cellular metabolic load includes energy consumption, amino acid consumption and cofactor requirements; Step S26: performing weighted calculation on the codon optimization degree, protein stability and metabolic load of each functional element according to the metabolic load prediction data, protein stability data and codon optimization degree data, thereby obtaining element comprehensive score data; Step S27: Based on the comprehensive component score data, a threshold range is set, and the components with scores in the top 30% are classified into a high expression group, the middle 40% of the components are classified into a medium expression group, and the bottom 30% of the components are classified into a low expression group, thereby obtaining hierarchical component adaptability data.

6. The method for mining synthetic biology functional elements according to claim 5, characterized in that: Step S3 includes the following steps: Step S31: analyzing the original expression vector of the host cell according to the element compatibility data, and obtaining the vector backbone compatibility data by evaluating the compatibility of the replicon, resistance marker and expression regulatory element of the vector; Step S32: designing a regulatable promoter system based on the vector backbone adaptability data, and obtaining promoter regulation module data by constructing a combination sequence of an inducer response element and a transcription factor binding site; Step S33: designing a standardized functional element connection interface according to the promoter regulatory module data, including an upstream ribosome binding site sequence and a downstream bidirectional transcription terminator sequence, thereby obtaining connection interface sequence data; Step S34: modular assembly of the vector is performed based on the ligation interface sequence data, and a standardized assembly process is established through restriction site optimization and ligation efficiency evaluation, thereby obtaining assembly strategy data; Step S35: Directedly assemble the regulatable promoter module, the connection interface sequence and the functional element sequence according to the assembly strategy data, thereby obtaining modular recombinant vector data.

7. The method for mining synthetic biology functional elements according to claim 6, characterized in that: Step S4 includes the following steps: Step S41: culturing and amplifying the transformed cell population according to the modular recombinant vector data, thereby obtaining cell suspension data; Step S42: Based on the cell suspension data, the uniformity of the droplet size of the microfluidic droplets is controlled by adjusting the flow rate, shear force and surfactant concentration, so as to obtain droplet encapsulation parameter data; Step S43: encapsulating the cell group into a microfluidic droplet according to the droplet encapsulation parameter data, and ensuring that each droplet contains only a single cell through Poisson distribution control, thereby obtaining single cell separation data; Step S44: Real-time monitoring of single cell separation data is performed by setting a multi-channel fluorescent probe and an optical sensor array, thereby obtaining fluorescent signal collection data; Step S45: Draw the growth curve of each single cell according to the fluorescence signal acquisition data, and establish a quantitative relationship between the fluorescence intensity, biomass and expression level, so as to obtain the single cell expression profile data.

8. The method for mining synthetic biology functional elements according to claim 7, characterized in that: Step S5 includes the following steps: Step S51: performing cell density change curve and growth rate distribution analysis on the single cell expression profile data to obtain growth characteristic data; Step S52: dynamically monitoring the accumulation process of the target product based on the growth characteristic data, and obtaining product dynamic data by establishing a correlation model between product concentration and time and calculating the product accumulation rate; Step S53: calculating the material conversion rate and energy balance state of key metabolic nodes according to the product dynamic data, thereby obtaining metabolic flux distribution data; Step S54: Based on the metabolic flux distribution data, a multi-dimensional evaluation is performed on the expression effect of the functional element based on the cell growth rate, the product accumulation rate and the metabolic flux change, thereby obtaining the element function evaluation data; Step S55: extracting and mapping the sequence features, structural features and functional features of the component function evaluation data, thereby obtaining functional component feature map data.

9. A mining system for synthetic biology functional elements, characterized in that: A method for mining synthetic biology functional elements according to claim 1, wherein the mining system for synthetic biology functional elements comprises: The target requirement analysis module is used to obtain the functional requirement data of the target metabolic pathway; based on the functional requirement data, the homologous genes of multiple species are analyzed for evolutionary distance and functional prediction, and the preset genome sequence data are classified based on the conservation of protein domains to obtain candidate element population data with functional relevance; The component adaptability evaluation module is used to obtain the expression system data of the host cell; score each functional element based on the codon optimization degree, protein folding stability and metabolic load prediction according to the candidate element population data and expression system data, and divide the elements into high expression group, medium expression group and low expression group according to the scoring results to obtain hierarchical component adaptability data; A modular vector construction module, used for constructing an expression vector with a regulatable promoter and a standardized connection interface based on the element fitness data, to obtain modular recombinant vector data, wherein the standardized connection interface includes a ribosome binding site for transcription initiation and a bidirectional terminator for transcription termination; The single-cell expression monitoring module is used to encapsulate the cell population in microfluidic droplets for single-cell separation according to the modular recombinant vector data, and to monitor the growth status and element expression level of individual cells in real time through fluorescence intensity to obtain single-cell expression profile data; The functional element screening module is used to screen the functional elements of cell growth rate, product accumulation rate and metabolic flux changes based on single-cell expression profile data to obtain functional element characteristic map data.

10. A storage medium, characterized in that: include: Memory for storing computer programs; A processor, used to implement the method for mining synthetic biology functional elements as described in any one of claims 1 to 8 when executing the computer program.

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