Abstract
Being a comprehensive knowledge bases of cellular metabolism, Genome-scale metabolic models (GEMs) serve as mathematical tools for studying cellular flux states in various or-ganisms. However, analysis of large-scale GEMs, such as human models, still presents considerable challenges with respect to objective selection and reaction flux constraints. In this study, we introduce a model-based method, ComMet (Comparison of Metabolic states), for comprehensive analysis of large metabolic flux spaces and comparison of various metabolic states. ComMet allows (a) an in-depth characterisation of flux states achievable by GEMs, (b) comparison of flux spaces from several conditions of interest, (c) identification of metabolically distinct network modules and (d) visualisation of network modules as reaction and metabolic map. As a proof-of-principle, we employed ComMet to extract the biochemical differences in the human adipocyte network (iAdipocytes1809) arising due to unlimited/blocked uptake of branched-chain amino acids. Our study opens avenues for exploring several metabolic condi-tions of interest in both microbe and human models. ComMet is open-source and is available at https://github.com/macsbio/commet.
Competing Interest Statement
The authors have declared no competing interest.
Footnotes
Funding information, This research has been made possible with the support of the Dutch Province of Limburg, The Netherlands.