{"doi":"10.1371/journal.pcbi.1011232","title":"Whole-cell modeling of E. coli colonies enables quantification of single-cell heterogeneity in antibiotic responses","abstract":"Antibiotic resistance poses mounting risks to human health, as current antibiotics are losing efficacy against increasingly resistant pathogenic bacteria. Of particular concern is the emergence of multidrug-resistant strains, which has been rapid among Gram-negative bacteria such as Escherichia coli. A large body of work has established that antibiotic resistance mechanisms depend on phenotypic heterogeneity, which may be mediated by stochastic expression of antibiotic resistance genes. The link between such molecular-level expression and the population levels that result is complex and multi-scale. Therefore, to better understand antibiotic resistance, what is needed are new mechanistic models that reflect single-cell phenotypic dynamics together with population-level heterogeneity, as an integrated whole. In this work, we sought to bridge single-cell and population-scale modeling by building upon our previous experience in \"whole-cell\" modeling, an approach which integrates mathematical and mechanistic descriptions of biological processes to recapitulate the experimentally observed behaviors of entire cells. To extend whole-cell modeling to the \"whole-colony\" scale, we embedded multiple instances of a whole-cell E. coli model within a model of a dynamic spatial environment, allowing us to run large, parallelized simulations on the cloud that contained all the molecular detail of the previous whole-cell model and many interactive effects of a colony growing in a shared environment. The resulting simulations were used to explore the response of E. coli to two antibiotics with different mechanisms of action, tetracycline and ampicillin, enabling us to identify sub-generationally-expressed genes, such as the beta-lactamase ampC, which contributed greatly to dramatic cellular differences in steady-state periplasmic ampicillin and was a significant factor in determining cell survival.","journal":"PLoS Computational Biology","year":2023,"id":344536,"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":13,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9523,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2023-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":1082961,"name":"Sean Cheah","orcid":"0000-0001-7433-1601","position":1,"is_corresponding":false},{"id":1083342,"name":"Mica Y. Yang","orcid":null,"position":2,"is_corresponding":false},{"id":1083343,"name":"Mattheus Wolff","orcid":null,"position":3,"is_corresponding":false},{"id":60874,"name":"Ryan Spangler","orcid":"0000-0002-6080-3142","position":4,"is_corresponding":false},{"id":811832,"name":"Lee Talman","orcid":null,"position":5,"is_corresponding":false},{"id":254608,"name":"Jerry H. Morrison","orcid":"0000-0001-9414-6999","position":6,"is_corresponding":false},{"id":339143,"name":"Shayn M. Peirce","orcid":"0000-0001-5857-5606","position":7,"is_corresponding":false},{"id":60825,"name":"Eran Agmon","orcid":"0000-0003-1279-2474","position":8,"is_corresponding":false},{"id":254609,"name":"Markus W. Covert","orcid":"0000-0002-5993-8912","position":9,"is_corresponding":false},{"id":566851,"name":"Christopher J. Skalnik","orcid":"0000-0002-6344-7331","position":0,"is_corresponding":true}],"reference_count":123,"raw_metadata":null,"created_at":"2026-07-19T01:11:30.951510Z","pmid":"37327241","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":[]}