Metabolic pathway analysis using a nash equilibrium approach

被引:2
作者
Lucia, Angelo [1 ]
DiMaggio, Peter A. [2 ]
Alonso-Martinez, Diego [2 ]
机构
[1] Univ Rhode Isl, Dept Chem Engn, Kingston, RI 02881 USA
[2] Imperial Coll London, Dept Chem Engn, London SW7 2AZ, England
关键词
Metabolic pathway analysis; Nash equilibrium; Flux balance analysis; Krebs cycle; FLUX BALANCE ANALYSIS; ESCHERICHIA-COLI; NETWORKS;
D O I
10.1007/s10898-018-0605-6
中图分类号
C93 [管理学]; O22 [运筹学];
学科分类号
070105 ; 12 ; 1201 ; 1202 ; 120202 ;
摘要
A novel approach to metabolic network analysis using a Nash Equilibrium (NE) formulation is proposed in which enzymes are considered players in a multi-player game. Each player has its own payoff function with the objective of minimizing the Gibbs free energy associated with the biochemical reaction(s) it catalyzes subject to elemental mass balances while the network objective is to find the best solution to the sum of the player payoff functions. Consequently, any NE solution may not be best solution for all players. Key advantages of the NE approach include the ability to account for (1) aqueous electrolyte behavior, (2) the consumption/production of co-factors, and (3) charge balancing. However, the proposed Nash equilibrium formulation results in a set of nonlinear programming sub-problems that are more demanding to solve than conventional flux balance analysis (FBA) formulations which rely on linear programming. A direct substitution solution methodology for pathways with feedback is described. The Krebs cycle is used to demonstrate the efficacy of the NE approach while comparisons with both FBA and experimental data are used to show that it represents a paradigm shift in metabolic network analysis.
引用
收藏
页码:537 / 550
页数:14
相关论文
共 27 条
[11]   Advances in flux balance analysis [J].
Kauffman, KJ ;
Prakash, P ;
Edwards, JS .
CURRENT OPINION IN BIOTECHNOLOGY, 2003, 14 (05) :491-496
[12]   Systematic assignment of thermodynamic constraints in metabolic network models [J].
Kuemmel, Anne ;
Panke, Sven ;
Heinemann, Matthias .
BMC BIOINFORMATICS, 2006, 7 (1)
[13]   Construction and completion of flux balance models from pathway databases [J].
Latendresse, Mario ;
Krummenacker, Markus ;
Trupp, Miles ;
Karp, Peter D. .
BIOINFORMATICS, 2012, 28 (03) :388-396
[14]   Recursive MILP model for finding all the alternate optima in LP models for metabolic networks [J].
Lee, S ;
Phalakornkule, C ;
Domach, MM ;
Grossmann, IE .
COMPUTERS & CHEMICAL ENGINEERING, 2000, 24 (2-7) :711-716
[15]  
Lucia A., 2016, LNCS, P1
[16]  
Machado D., 2016, CURRENT CHALLENGES M
[17]   Dynamic flux balance analysis of diauxic growth in Escherichia coli [J].
Mahadevan, R ;
Edwards, JS ;
Doyle, FJ .
BIOPHYSICAL JOURNAL, 2002, 83 (03) :1331-1340
[18]   Capturing the essence of a metabolic network: A flux balance analysis approach [J].
Murabito, Ettore ;
Simeonidis, Evangelos ;
Smallbone, Kieran ;
Swinton, Jonathan .
JOURNAL OF THEORETICAL BIOLOGY, 2009, 260 (03) :445-452
[19]   Pareto Optimal Design for Synthetic Biology [J].
Patane, Andrea ;
Santoro, Andrea ;
Costanza, Jole ;
Carapezza, Giovanni ;
Nicosia, Giuseppe .
IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS, 2015, 9 (04) :555-571
[20]  
Rafelson M. E, 1965, BASIC BIOCH