a scalable algorithm to explore the gibbs energy landscape of genome-scale metabolic networks一个可扩展的算法探索景观公司的吉布斯能量代谢网络.pdfVIP

A Scalable Algorithm to Explore the Gibbs Energy Landscape of Genome-Scale Metabolic Networks 1 1 1,2. 1. Daniele De Martino , Matteo Figliuzzi , Andrea De Martino *, Enzo Marinari ` ` 1 Dipartimento di Fisica, Sapienza Universita di Roma, Roma, Italy, 2 CNR-IPCF, Unita di Roma-Sapienza, Roma, Italy Abstract The integration of various types of genomic data into predictive models of biological networks is one of the main challenges currently faced by computational biology. Constraint-based models in particular play a key role in the attempt to obtain a quantitative understanding of cellular metabolism at genome scale. In essence, their goal is to frame the metabolic capabilities of an organism based on minimal assumptions that describe the steady states of the underlying reaction network via suitable stoichiometric constraints, specifically mass balance and energy balance (i.e. thermodynamic feasibility). The implementation of these requirements to generate viable configurations of reaction fluxes and/or to test given flux profiles for thermodynamic feasibility can however prove to be computationally intensive. We propose here a fast and scalable stoichiometry-based method to explore the Gibbs energy landscape of a biochemical network at steady state. The method is applied to the problem of reconstructing the Gibbs energy landscape underlying metabolic activity in the human red blood cell, and to that of identifying and removing thermodynamically infeasible reaction cycles in the Escherichia coli metabolic network (iAF1260). In the former case, we produce consistent predictions for chemical