title: Ab initio prediction of thermodynamically feasible reaction directions from biochemical network stoichiometry.
abstract: Analysis of the stoichiometric structure of metabolic networks provides insights into the relationships between structure, function, and regulation of metabolic systems. Based on knowledge of only reaction stoichiometry, certain aspects of network functionality and robustness can be predicted. Current theories focus on breaking a metabolic network down into non-decomposable pathways able to operate in steady state. The physics underlying these theories is based on mass balance and the laws of thermodynamics. However, due to the inherent nonlinearity of the thermodynamic constraints on metabolic fluxes, computational analysis of large-scale biochemical systems can be expensive. In this study, it is shown how the feasible reaction directions may be determined by either computing the allowable ranges under the mass-balance and thermodynamic constraints or by analyzing the stoichiometric structure of the network. The computed reaction directions translate into a set of linear constraints necessary for thermodynamic feasibility. This set of necessary linear constraints is shown to be sufficient to guarantee feasibility in certain cases, thus translating the nonlinear thermodynamic constraints to linear. We show that for a reaction network of 44 internal reactions representing energy metabolism, the computed linear inequality constraints represent necessary and sufficient conditions for thermodynamic feasibility.
authors.affiliation: Biotechnology and Bioengineering Center, Department of Physiology, Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226, USA.
authors.forename: Feng
authors.initials: F
authors.lastname: Yang
authors.forename: Hong
authors.initials: H
authors.lastname: Qian
authors.forename: Daniel A
authors.initials: DA
authors.lastname: Beard
mesh_terms: D002468:Cell Physiological Phenomena; D019295:Computational Biology / Q000379:methods; D004734:Energy Metabolism / Q000502:physiology*; D008962:Models, Theoretical; D017711:Nonlinear Dynamics; D013816:Thermodynamics