ArticleNature communications2020
The ETFL formulation allows multi-omics integration in thermodynamics-compliant metabolism and expression models.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 46 papers.
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46 citing papers in PubMed.
- Thermo-flux: generation and analysis of thermodynamic-stoichiometric metabolic network models.Molecular systems biology · 2026Article
- Predictive Algal Systems Biology: Integrating Omics, Genome-Scale Metabolic Models, and Machine Learning.Bioengineering (Basel, Switzerland) · 2026Review
- A voyage of reprogrammable metabolic bioengineering reshapes plant defense: from editing tools to synthetic systems.Frontiers in plant science · 2026Review
- ThermOptCobra: Thermodynamically optimal construction and analysis of metabolic networks for reliable phenotype predictions.iScience · 2025Article
- The Dawn of High-Throughput and Genome-Scale Kinetic Modeling: Recent Advances and Future Directions.ACS synthetic biology · 2025Review
- New insights in metabolism modelling to decipher plant-microbe interactions.The New phytologist · 2025Review
- Article
- RBC-GEM: A genome-scale metabolic model for systems biology of the human red blood cell.PLoS computational biology · 2025Article
- Evolution and applications of genome-scale metabolic models in yeast systems biology studies.FEMS yeast research · 2025Review
- Sensitivities in protein allocation models reveal distribution of metabolic capacity and flux control.Bioinformatics (Oxford, England) · 2024Article
- Harnessing the optimization of enzyme catalytic rates in engineering of metabolic phenotypes.PLoS computational biology · 2024Article
- Integration of proteomic data with genome-scale metabolic models: A methodological overview.Protein science : a publication of the Protein Society · 2024Review
- Mitochondrial ATP generation is more proteome efficient than glycolysis.Nature chemical biology · 2024Article
- Current State, Challenges, and Opportunities in Genome-Scale Resource Allocation Models: A Mathematical Perspective.Metabolites · 2024Review
- Reconstruction, simulation and analysis of enzyme-constrained metabolic models using GECKO Toolbox 3.0.Nature protocols · 2024Review
- Genome-scale metabolic modelling of extremophiles and its applications in astrobiological environments.Environmental microbiology reports · 2024Review
- Generative machine learning produces kinetic models that accurately characterize intracellular metabolic states.Nature catalysis · 2024Article
- ICON-GEMs: integration of co-expression network in genome-scale metabolic models, shedding light through systems biology.BMC bioinformatics · 2023Article
- Simultaneous application of enzyme and thermodynamic constraints to metabolic models using an updated Python implementation of GECKO.Microbiology spectrum · 2023Article
- Improving pathway prediction accuracy of constraints-based metabolic network models by treating enzymes as microcompartments.Synthetic and systems biotechnology · 2023Article
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2 authors.
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Abstract
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.
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