(237c) Novel Framework for Identifying Objective Functions of Biological Systems

Much work has been done on the application of optimization theory to biological systems. In particular, flux balance analysis (FBA) has been used extensively to analyze flux distributions in metabolic networks at steady state [1]. In FBA, a linear programming (LP) problem is constructed wherein an objective function, e.g., the maximization of biomass, is optimized over stoichiometric constraints. Despite the success of FBA [2], identifying objective functions of biological systems from experimental flux data remains challenging.

We present a novel framework for identifying objective functions of biological systems from experimental flux data in which a new reaction constituting a putative objective function is added to the set of stoichiometric constraints and subsequently maximized as part of a LP problem. A previous method attempts to identify weightings on reaction fluxes within a network while minimizing the difference between the resultant flux distributions and known experimental fluxes [3]. However, it yields objectives that are difficult to relate to the underlying biology. Our new formulation refines a putative objective function while minimizing the difference between the resultant flux distributions and the experimental fluxes. The approach yields a stoichiometrically-weighted sink reaction for which a given system optimizes. This proposed framework is verified with an existing reconstruction of Escherichia coli metabolism [4].

This technique offers a means for gaining insight into the functional organization of large-scale biochemical networks. It facilitates the interrogation of the fundamental basis of cellular objectives and can give practical insight for metabolic engineering and optimization-based system analyses.

References:

1. Kauffman, K.J., et al. (2003) Advances in flux balance analysis. Curr Opin Biotechnol 14, 491-496

2. Edwards, J.S., et al. (2001) In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data. Nat Biotechnol 19, 125-130

3. Burgard, A.P., and Maranas, C.D. (2003) Optimization-based framework for inferring and testing hypothesized metabolic objective functions. Biotechnol Bioeng 82, 670-677

4. Reed, J.L., et al. (2003) An expanded genome-scale model of Escherichia coli K-12 (iJR904 GSM/GPR). Genome Biol 4, R54