Geckopy 3.0: enzyme constraints, thermodynamics constraints and omics integration in python
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 - e.g. 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-constraint models, which enable the integration of kinetic data and proteomics data into GEM models. 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 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, in an effort to reach maturity in enzyme-constraint modeling. With the new geckopy, proteins are typed in the SBML document, taking advantage of the SBML Groups extension, in compliance with community standards. Additionally, a suite of relaxation algorithms - in the form of linear and mixed-integer linear programming problems - has been added to facilitate 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 datasets in Escherichia coli for different conditions, revealing targets for improving the enzyme constrained model and/or the proteomics pipeline. IMPORTANCEThe 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 which is able to 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 relaxations algorithms in a package called geckopy. Additionally, to ensure that enzyme-constraint models follow the community standards, a format for the proteins is postulated. Geckopy also integrates with other software to allow for adding thermodynamic and metabolomic constraints. We hope that the package and algorithms presented here will serve useful for the constraint-based modeling community.