(534c) Towards Genome-Scale Dynamic Modeling of Cellular Metabolism. the Cybernetic Approach

The hallmark of the cybernetic approach is its description of cellular regulation to provide for a dynamic framework of metabolic modeling. It views cells as optimal strategists frugally allocating internal resources among reactions in order to maximize a metabolic objective (e.g., carbon uptake rate or growth rate). Earlier development of cybernetic modeling had focused on the growth pattern on multiple substrates based on gross metabolic networks (e.g., Baloo and Ramkrishna 1991, Kompala et al. 1986, Ramakrishna et al. 1996, Turner et al. 1988). More recently, the hybrid cybernetic model (HCM) (Kim et al. 2008, Song et al. 2009, Song and Ramkrishna 2009) and lumped HCM (L-HCM) (Song and Ramkrishna 2010, 2011) has enabled consideration of more detailed networks through decomposition into elementary modes (EMs). Extension of this methodology to genome-scale networks, has, however, been challenged by difficulties associated with decomposition of large networks into EMs (Klamt and Stelling 2002). So far, genome-scale networks have been handled only by flux balance analysis (FBA) and its derivatives (Orth et al. 2010). Application of those constraint-based approaches to metabolic modeling and metabolic engineering is subject to limitations because of lack of attention to regulatory dynamics.

In this work, we present a methodology to incorporate genome-scale metabolic networks into the L-HCM framework. The L-HCMs describe cellular resources in terms of competition among lumped EMs (L-EMs) which are weighted averages of EMs in families classified according to similarity of metabolic function. As the weight takes a power-law form with an exponent of large constant, only a limited number of EMs play a role in computing L-EMs. Thus, instead of endeavoring to get the full set of EMs, we extract only these “essential” modes with an appreciable contribution to the averaging process. For this purpose, we have developed a MATLAB code based on the recursive mixed integer linear programming (MILP) algorithm originally proposed by Lee et al. (2000).  In various test examples, it is shown that the MILP code successfully smokes out essential pathways including multiple optima and suboptima which are essential to compute L-EMs. This development enables the cybernetic modeling approach to address genome-scale networks. Also possible is direct comparison of L-HCMs with constraint-based approaches using the same size of genome-scale network.

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