Simultaneous application of enzyme and thermodynamic constraints to metabolic models using an updated Python implementation of GECKO

• Published in: Microbiology spectrum Vol. 11; no. 6; p. e0170523
• Main Authors: Carrasco Muriel, Jorge, Long, Christopher, Sonnenschein, Nikolaus
• Format: Journal Article
• Language: English
• Published: United States American Society for Microbiology 12.12.2023
• Subjects: constrained-based modeling; proteomics; Research Article; Systems Biology; thermodynamics; proteomics; thermodynamics; constrained-based modeling
• ISSN: 2165-0497; 2165-0497
• DOI: 10.1128/spectrum.01705-23

Genome-scale metabolic (GEM) models are knowledge bases of the reactions and metabolites of a particular organism. These GEM models allow for the simulation of the metabolism, for example, calculating growth and production yields—based on the stoichiometry, reaction directionality, and uptake rates of the metabolic network. Over the years, several extensions have been added to take into account other actors in metabolism, going beyond pure stoichiometry. One such extension is enzyme-constrained models, which enable the integration of proteomics data into GEM models containing the necessary k cat values for their enzymes. Given its relatively recent formulation, there are still challenges in standardization and data reconciliation between the model and the experimental measurements. In this work, we present geckopy 3.0 (genome-scale model with enzyme constraints, using Kinetics and Omics in Python), an actualization from scratch of the previous Python implementation of the same name. This update tackles the aforementioned challenges, to reach maturity in enzyme-constrained modeling. With the new geckopy, proteins are typed in the Systems Biology Markup Language (SBML) document, taking advantage of the SBML Groups extension, in compliance with community standards. In addition, a suite of relaxation algorithms—in the form of linear and mixed-integer linear programming problems—has been added to facilitate the reconciliation of raw proteomics data with the metabolic model. Several functionalities to integrate experimental data were implemented, including an interface layer with pytfa for the usage of thermodynamics and metabolomics constraints. Finally, the relaxation algorithms were benchmarked against public proteomics data sets in Escherichia coli for different conditions, revealing targets for improving the enzyme-constrained model and/or the proteomics pipeline. The metabolism of biological cells is an intricate network of reactions that interconvert chemical compounds, gathering energy, and using that energy to grow. The static analysis of these metabolic networks can be turned into a computational model that can efficiently output the distribution of fluxes in the network. With the inclusion of enzymes in the network, we can also interpret the role and concentrations of the metabolic proteins. However, the models and the experimental data often clash, resulting in a network that cannot grow. Here, we tackle this situation with a suite of relaxation algorithms in a package called geckopy. Geckopy also integrates with other software to allow for adding thermodynamic and metabolomic constraints. In addition, to ensure that enzyme-constrained models follow the community standards, a format for the proteins is postulated. We hope that the package and algorithms presented here will be useful for the constraint-based modeling community.