The ETFL formulation allows multi-omics integration in thermodynamics-compliant metabolism and expression models

• Published in: Nature communications Vol. 11; no. 1; pp. 30 - 17
• Main Authors: Salvy, Pierre, Hatzimanikatis, Vassily
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 13.01.2020; Nature Publishing Group; Nature Portfolio
• Subjects: 119/118; 631/114/2397; 631/114/2401; 631/114/794; 631/553/318; 631/61/318; Biomass; Computer simulation; E coli; Enzymes; Escherichia coli - metabolism; Fluxes; Gene expression; Gene Expression Regulation, Bacterial; Genomes; Humanities and Social Sciences; Integration; Metabolic Engineering; Metabolism; Metabolites; Metabolomics; Models, Biological; mRNA; multidisciplinary; Proteins; Proteome - metabolism; Proteomes; Proteomics; RNA - biosynthesis; RNA, Messenger - metabolism; Science; Science (multidisciplinary); Software; Solvers; Systems Biology - methods; Thermodynamics; Transcription
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-019-13818-7

Systems biology has long been interested in models capturing both metabolism and expression in a cell. We propose here an implementation of the metabolism and expression model formalism (ME-models), which we call ETFL, for Expression and Thermodynamics Flux models. ETFL is a hierarchical model formulation, from metabolism to RNA synthesis, that allows simulating thermodynamics-compliant intracellular fluxes as well as enzyme and mRNA concentration levels. ETFL formulates a mixed-integer linear problem (MILP) that enables both relative and absolute metabolite, protein, and mRNA concentration integration. ETFL is compatible with standard MILP solvers and does not require a non-linear solver, unlike the previous state of the art. It also accounts for growth-dependent parameters, such as relative protein or mRNA content. We present ETFL along with its validation using results obtained from a well-characterized E. coli model. We show that ETFL is able to reproduce proteome-limited growth. We also subject it to several analyses, including the prediction of feasible mRNA and enzyme concentrations and gene essentiality. Accounting for the effects of genetic expression in genome-scale metabolic models is challenging. Here, the authors introduce a model formulation that efficiently simulates thermodynamic-compliant fluxes, enzyme and mRNA concentration levels, allowing omics integration and broad analysis of in silico cellular physiology.