Systematic reduction of genome-scale models for the study of metabolic phenotypes of human cells

In the last years, the analysis of cellular metabolism has sparked new interest in systems biology and metabolic modelling. In particular, modelling the phenotypes of healthy and diseased cells will help to understand the main metabolic characteristics of disease development and progression. It will also be key to design more effective therapies. The reconstruction of genome-scale models (GEMs) enables the computation of phenotypic traits based on the genetic composition of a target organism. To overcome the well-known challenges when working with large networks, we generate systematically reduced models around specific subsystems. Within this framework, we curate the GEMs to include the thermodynamic properties of the network metabolites and reactions. Furthermore, we consider the composition and utilization of the extracellular-medium metabolites and the synthesis of the biomass precursor metabolites. The reduced models can be used for a broad range of applications extending from omics data integration to kinetic models. We demonstrate here that the study of reduced human GEMs can provide insight and a systematic framework to compare different versions of GEMs (Recon 2 versus Recon 3) as well as different cellular physiologies, such as diseased versus healthy phenotypes.

Presented at:
Constraint-Based Reconstruction and Analysis (COBRA) 2018, Seattle, USA, 14-16 October 2018

 Record created 2018-12-05, last modified 2019-04-15

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