Treffer: Multi-omics data integration in constraint-based modeling of metabolic networks to study the metabolism of adipose-derived stem cells

Background: Genome-scale metabolic networks (GSMNs) are models representing all known metabolic processes occurring within a given organism as an interconnected network of metabolites, biochemical reactions, enzymes, and enzyme-coding genes. Through the integration of experimental data and the use of constraint-based modeling algorithms, these models can be used to simulate the metabolism of cells in various experimental conditions. Within the scope of the ASCending project, we aim to study the metabolic changes occurring in adipose-derived stem cells (ASCs) obtained from different donors over the course of a cell culture process. The final goal of this project is to adjust the culture parameters in order to optimize the proliferation and potency of the ASCs for the production of cellular therapy to treat medical conditions such as Alzheimer's disease. Results: For the purpose of this project, we used the previously published DEXOM algorithm [1] for con-straint-based modeling of metabolic networks, which was integrated into the larger OCMMED work-flow [2]. This workflow, which was originally designed for the reconstruction of cell-specific metabolic networks based on transcriptomics data, was adapted in order to add constraints on the model derived from the cell population doubling times, cell culture medium composition, and time-series exometabo-lomics data. We used these constraints to reconstruct metabolic networks for the cells in each experi-mental condition. We then examined the metabolic changes occurring over the course of the cell cul-ture process by comparing the contents of the different metabolic networks using both network-based approaches and methods for binary matrix comparisons.Conclusions: The simultaneous inclusion of different types of experimental data in our constraint-based modeling workflow presents a challenge, both due to the necessity of adapting our data processing strategy, and due to the stark increase in the number of constraints of differing natures on the meta-bolic model. However, these additional constraints allow for more precise modeling of cellular metabo-lism in various experimental conditions and better comparisons between different experimental condi-tions. With the adapted OCMMED workflow, we generate cell-specific models with which we can ex-amine the metabolic changes undergone by ASCs during their cell culture process, and which can then be used to predict metabolic reaction fluxes in simulated new medium conditions.References1.Rodríguez-Mier P, Poupin N, de Blasio C, Le Cam L, Jourdan F. DEXOM: Diversity-based enumeration of optimal context-specific metabolic networks. PLoS Comput Biol 2021;17:e1008730.2.Available from: https://forgemia.inra.fr/metexplore/cbm/ocmmed