Pierre Salvy

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… Mehr anzeigen

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. Weniger anzeigen

Summary

pyTFA and matTFA are the first published implementations of the original TFA paper. Specifically, they include explicit formulation of Gibbs energies and metabolite concentrations, which enables straightforward integration of metabolite concentration measurements.
Motivation

High-throughput analytic technologies provide a wealth of omics data that can be used to perform thorough analyses for a multitude of studies in the areas of Systems Biology and Biotechnology… Mehr anzeigen

Summary

pyTFA and matTFA are the first published implementations of the original TFA paper. Specifically, they include explicit formulation of Gibbs energies and metabolite concentrations, which enables straightforward integration of metabolite concentration measurements.
Motivation

High-throughput analytic technologies provide a wealth of omics data that can be used to perform thorough analyses for a multitude of studies in the areas of Systems Biology and Biotechnology. Nevertheless, most studies are still limited to constraint-based Flux Balance Analyses (FBA), neglecting an important physicochemical constraint: thermodynamics. Thermodynamics-based Flux Analysis (TFA) in metabolic models enables the integration of quantitative metabolomics data to study their effects on the net-flux directionality of reactions in the network. In addition, it allows us to estimate how far each reaction operates from thermodynamic equilibrium, which provides critical information for guiding metabolic engineering decisions.
Results

We present a Python package (pyTFA) and a Matlab toolbox (matTFA) that implement TFA. We show an example of application on both a reduced and a genome-scale model of E. coli., and demonstrate TFA and data integration through TFA reduce the feasible flux space with respect to FBA.
Availability and implementation

Documented implementation of TFA framework both in Python (pyTFA) and Matlab (matTFA) are available on www.github.com/EPFL-LCSB/. Weniger anzeigen

Since the introduction of metabolic models and flux balance analysis (FBA) in systems biology, several attempts have been made to add expression data. However, directly accounting for enzyme and mRNA production in the mathematical programming formulation is challenging because of macromolecules, which introduces a bilinear term in the mass-balance equations that become harder to solve than linear formulations like FBA. Furthermore, there have been no attempts to include thermodynamic… Mehr anzeigen

Since the introduction of metabolic models and flux balance analysis (FBA) in systems biology, several attempts have been made to add expression data. However, directly accounting for enzyme and mRNA production in the mathematical programming formulation is challenging because of macromolecules, which introduces a bilinear term in the mass-balance equations that become harder to solve than linear formulations like FBA. Furthermore, there have been no attempts to include thermodynamic constraints in these formulations, which would yield an even more complex mixed-integer non-linear problem.

We propose here a new framework, called Expression and Thermodynamics Flux (ETFL), as a new ME-model implementation. ETFL is a top-down model formulation, from metabolism to RNA synthesis, that simulates thermodynamic-compliant intracellular fluxes as well as enzyme and mRNA concentration levels. The formulation results in a mixed-integer linear problem (MILP) that enables both relative and absolute metabolite, protein, and mRNA concentration integration. The proposed formulation is compatible with mainstream MILP solvers and does not require a non-linear solver. It also accounts for growth-dependent parameters, such as relative protein or mRNA content.

We present here the formulation of 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, which FBA cannot. We also subject it to different analyses, including the prediction of feasible mRNA and enzyme concentrations in the cell, and propose ETFL-based adaptations of other common FBA-based procedures.

The software is available on our public repository at https://github.com/EPFL-LCSB/etfl. Weniger anzeigen