{"doi":"10.1101/2020.03.23.003236","title":"Computation of condition-dependent proteome allocation reveals variability in the macro and micro nutrient requirements for growth","abstract":"Abstract Sustaining a robust metabolic network requires a balanced and fully functioning proteome. In addition to amino acids, many enzymes require cofactors (coenzymes and engrafted prosthetic groups) to function properly. Extensively validated genome-scale models of metabolism and gene expression (ME-models) have the unique ability to compute an optimal proteome composition underlying a metabolic phenotype, including the provision of all required cofactors. Here we use the ME-model for Escherichia coli K-12 MG1655 to computationally examine how environmental conditions change the proteome and its accompanying cofactor usage. We found that: (1) The cofactor requirements computed by the ME model mostly agree with the standard biomass objective function used in models of metabolism alone (M models); (2) ME-model computations reveal non-intuitive variability in cofactor use under different growth conditions; (3) An analysis of ME-model predicted protein use in aerobic and anaerobic conditions suggests an enrichment in the use of prebiotic amino acids in the proteins used to sustain anaerobic growth (4) The ME-model could describe how limitation in key protein components affect the metabolic state of E. coli. Genome-scale models have thus reached a level of sophistication where they reveal intricate properties of functional proteomes and how they support different E. coli lifestyles.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2020,"id":123545,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":3,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9518,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":5439,"name":"Jonathan M. Monk","orcid":"0000-0002-3895-8949","position":1,"is_corresponding":false},{"id":314816,"name":"Laurence Yang","orcid":"0000-0001-6663-7643","position":2,"is_corresponding":false},{"id":234812,"name":"Ali Ebrahim","orcid":"0000-0002-4009-2128","position":3,"is_corresponding":false},{"id":5441,"name":"Bernhard Ø. Palsson","orcid":"0000-0003-2357-6785","position":4,"is_corresponding":false},{"id":566438,"name":"Colton J. Lloyd","orcid":"0000-0001-6556-6345","position":0,"is_corresponding":true}],"reference_count":45,"raw_metadata":null,"created_at":"2026-07-18T23:14:59.547352Z","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":[]}