(714d) Extreme Pathways of Biochemical Reaction Networks Under Thermodynamic Constraints

Fung, H. K. - Presenter, Ecole Polytechnique Fédérale de Lausanne (EPFL)
Soh, K. C. - Presenter, Ecole Polytechnique Fédérale de Lausanne (EPFL)
Hatzimanikatis, V. - Presenter, Swiss Federal Institute of Technology (EPFL)

Extreme Pathways (EPs) and Elementary Flux Modes (EFMs) represent minimal sets of reactions in a metabolic network that can operate at steady state to allow all irreversible reactions to proceed in their thermodynamically feasible directions. They are key concepts used for the analysis of biochemical reaction networks. They have been successfully used for identifying routes for producing target bio-products, quantifying network flexibility, identifying pathways with the optimal yield, and measuring reaction or enzyme correlations. However, one key challenge in applying EPs or EFMs is that their number grows exponentially with the size of a network.

We present a novel optimization-based computational method to calculate the EPs of a metabolic network that are thermodynamically feasible. It is a mixed-integer linear programming (MILP) model that can operate with or without linear thermodynamic constraints, which we first employed in the genome-scale thermodynamic analysis of E. coli metabolism [1] and in the analysis of metabolic network diversity [2]. In our presentation, we will discuss: (i) formulation of the problem into the MILP model; (ii) generation of the results on both a medium-sized model for the central metabolism of E. coli, as well as a genome-scale model of the organism; (iii) the reduction in the number of EPs as well as the reactions that disappear in the EPs after we impose the thermodynamic constraints. We found that significant reduction in the EPs can result using our method, even for medium-sized metabolic networks.

[1] Henry, C. S., M. D. Jankowski, L. J. Broadbelt, and V. Hatzimanikatis. Genome-scale thermodynamic analysis of Escherichia coli metabolism. Biophys. J. 90: 1453-1461 (2006).

[2] Hatzimanikatis, V., C. Li, J. A. Ionita, C. S. Henry, M. D. Jankowski, and L. J. Broadbelt. Exploring the diversity of complex metabolic networks. Bioinformatics 21: 1603-1609 (2005).