{"doi":"10.3390/biom12010065","title":"ECMpy, a Simplified Workflow for Constructing Enzymatic Constrained Metabolic Network Model","abstract":"<jats:p>Genome-scale metabolic models (GEMs) have been widely used for the phenotypic prediction of microorganisms. However, the lack of other constraints in the stoichiometric model often leads to a large metabolic solution space being inaccessible. Inspired by previous studies that take an allocation of macromolecule resources into account, we developed a simplified Python-based workflow for constructing enzymatic constrained metabolic network model (ECMpy) and constructed an enzyme-constrained model for Escherichia coli (eciML1515) by directly adding a total enzyme amount constraint in the latest version of GEM for E. coli (iML1515), considering the protein subunit composition in the reaction, and automated calibration of enzyme kinetic parameters. Using eciML1515, we predicted the overflow metabolism of E. coli and revealed that redox balance was the key reason for the difference between E. coli and Saccharomyces cerevisiae in overflow metabolism. The growth rate predictions on 24 single-carbon sources were improved significantly when compared with other enzyme-constrained models of E. coli. Finally, we revealed the tradeoff between enzyme usage efficiency and biomass yield by exploring the metabolic behaviours under different substrate consumption rates. Enzyme-constrained models can improve simulation accuracy and thus can predict cellular phenotypes under various genetic perturbations more precisely, providing reliable guidance for metabolic engineering.</jats:p>","journal":"Biomolecules","year":2022,"id":654882,"datarank":0.5570358100056463,"base_score":3.713572066704308,"endowment":3.713572066704308,"self_citation_contribution":0.5570358100056463,"citation_network_contribution":0.0,"self_endowment_contribution":0.5570358100056463,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":40,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":null,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":null,"fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":305657,"name":"Xin Zhao","orcid":"0000-0001-8502-0139","position":1,"is_corresponding":false},{"id":625167,"name":"Xue Yang","orcid":"0000-0003-2768-1865","position":2,"is_corresponding":false},{"id":1709249,"name":"Peiji Zhang","orcid":null,"position":3,"is_corresponding":false},{"id":1709250,"name":"Jiawei Du","orcid":null,"position":4,"is_corresponding":false},{"id":225863,"name":"Qianqian Yuan","orcid":"0000-0002-0749-0749","position":5,"is_corresponding":false},{"id":225843,"name":"Hongwu Ma","orcid":"0000-0001-5325-2314","position":6,"is_corresponding":false},{"id":1709248,"name":"Zhitao Mao","orcid":"0000-0002-8160-2585","position":0,"is_corresponding":false}],"reference_count":0,"raw_metadata":{"has_enrichment":true,"resolved":true,"title":"ECMpy, a Simplified Workflow for Constructing Enzymatic Constrained Metabolic Network Model","abstract":"<jats:p>Genome-scale metabolic models (GEMs) have been widely used for the phenotypic prediction of microorganisms. However, the lack of other constraints in the stoichiometric model often leads to a large metabolic solution space being inaccessible. Inspired by previous studies that take an allocation of macromolecule resources into account, we developed a simplified Python-based workflow for constructing enzymatic constrained metabolic network model (ECMpy) and constructed an enzyme-constrained model for Escherichia coli (eciML1515) by directly adding a total enzyme amount constraint in the latest version of GEM for E. coli (iML1515), considering the protein subunit composition in the reaction, and automated calibration of enzyme kinetic parameters. Using eciML1515, we predicted the overflow metabolism of E. coli and revealed that redox balance was the key reason for the difference between E. coli and Saccharomyces cerevisiae in overflow metabolism. The growth rate predictions on 24 single-carbon sources were improved significantly when compared with other enzyme-constrained models of E. coli. Finally, we revealed the tradeoff between enzyme usage efficiency and biomass yield by exploring the metabolic behaviours under different substrate consumption rates. Enzyme-constrained models can improve simulation accuracy and thus can predict cellular phenotypes under various genetic perturbations more precisely, providing reliable guidance for metabolic engineering.</jats:p>","is_dataset_classified":null,"base_score":3.713572066704308,"endowment":3.713572066704308,"datacite_reuse_total":0,"file_count":0,"downloads":0,"views":0,"has_version_chain":false,"is_dataset":false,"is_oa":false,"pmid":"35053213","pmcid":"PMC8773657","openalex_id":"https://openalex.org/W4225897641","authors":[],"funders":[{"funder_name":"the International Partnership Program of Chinese Academy of Sciences","grant_id":"153D31KYSB20170121","title":null},{"funder_name":"the National Key Research and Development Program of China","grant_id":"2018YFA0900300","title":null}],"total_grants":2,"fwci":2.9037,"citation_percentile":0.92411312,"influential_citations":0,"citation_trend":[{"year":2022,"count":9},{"year":2023,"count":7},{"year":2024,"count":13},{"year":2025,"count":8},{"year":2026,"count":3}],"oa_status":"gold","license":"cc-by","oa_locations":[{"url":"https://www.mdpi.com/2218-273X/12/1/65/pdf?version=1641111139","host_type":"journal"},{"url":"https://www.mdpi.com/2218-273X/12/1/65/pdf?version=1641111139","host_type":"publisher"},{"url":"https://www.mdpi.com/2218-273X/12/1/65/pdf","host_type":"publisher"},{"url":"https://doi.org/10.3390/biom12010065","host_type":"journal"},{"url":"https://pubmed.ncbi.nlm.nih.gov/35053213","host_type":"repository"},{"url":"https://doaj.org/article/6c53b2f79a4641908986d350e692457b","host_type":"repository"},{"url":"https://www.ncbi.nlm.nih.gov/pmc/articles/8773657","host_type":"repository"},{"url":"https://europepmc.org/articles/PMC8773657","host_type":"Europe_PMC"},{"url":"https://europepmc.org/articles/PMC8773657?pdf=render","host_type":"Europe_PMC"}],"fields_of_study":["Microbial Metabolic Engineering and Bioproduction","Biofuel production and bioconversion","Gene Regulatory Network Analysis","Computer Simulation","Escherichia coli","Escherichia coli Proteins","Metabolic Engineering","Metabolic Networks and Pathways","Models, Biological","Saccharomyces cerevisiae","Saccharomyces cerevisiae Proteins"],"mesh_terms":["Computer Simulation","Escherichia coli","Models, Biological","Saccharomyces cerevisiae","Saccharomyces cerevisiae Proteins","Escherichia coli Proteins","Metabolic Networks and Pathways","Metabolic Engineering"],"keywords":["Metabolic network","Metabolic engineering","Flux balance analysis","Workflow","Escherichia coli","Computer science","Saccharomyces cerevisiae","Systems biology","Constraint (computer-aided design)","Enzyme","Computational biology","Biological system","Biochemical engineering","Chemistry","Biology","Biochemistry","Mathematics","Gene","Engineering","Enzyme kinetics","Protein subunit","Overflow Metabolism","Enzyme-constrained Model"],"sdg_mappings":[{"sdg_number":0,"sdg_label":"Responsible consumption and production"}],"linked_datasets":[],"clinical_trials":[],"software_tools":[],"database_accessions":[{"name":"doi"}],"source":"live","citation_network_status":"fetched"},"created_at":"2026-08-11T08:45:35.300612Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}