LANS Informal Seminar: Hyun-Seob Song

Seminar Title: Towards Genome-Scale Based Dynamic Modeling of Cellular Metabolism. The Cybernetic Approach
Speaker: Hyun-Seob Song, Research Scientist, ECN, Purdue University

Date/Time: 2011-11-22 15:00
Location: Building 240, Conference Center 1404-1405

Description:
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., Kompala et al. 1986). 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. 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, a methodology is presented 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, only these ‘dominant’ modes with an appreciable contribution to the averaging process are extracted. For this purpose, a MATLAB code is developed based on the 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.

References:
Kim JI, Varner JD, Ramkrishna D. 2008. A Hybrid Model of Anaerobic E. coli GJT001: Combination of Elementary Flux Modes and Cybernetic Variables. Biotechnology Progress 24(5):993-1006.
Kompala DS, Ramkrishna D, Jansen NB, Tsao GT. 1986. Investigation of Bacterial-Growth on Mixed Substrates – Experimental Evaluation of Cybernetic Models. Biotechnology and Bioengineering 28(7):1044-1055.
Lee S, Phalakornkule C, Domach MM, Grossmann IE. 2000. Recursive MILP model for finding all the alternate optima in LP models for metabolic networks. Computers & Chemical Engineering 24(2-7):711-716.
Orth JD, Thiele I, Palsson BO. 2010. What is flux balance analysis? Nature Biotechnology 28(3):245-248.
Song HS, Morgan JA, Ramkrishna D. 2009. Systematic Development of Hybrid Cybernetic Models: Application to Recombinant Yeast Co-Consuming Glucose and Xylose. Biotechnology and Bioengineering 103(5):984-1002.
Song HS, Ramkrishna D. 2009. Reduction of a Set of Elementary Modes Using Yield Analysis. Biotechnology and Bioengineering 102(2):554-568.
Song HS, Ramkrishna D. 2010. Prediction of Metabolic Function From Limited Data: Lumped Hybrid Cybernetic Modeling (L-HCM). Biotechnology and Bioengineering 106(2):271-284.
Song HS, Ramkrishna D. 2011. Cybernetic Models Based on Lumped Elementary Modes Accurately Predict Strain-Specific Metabolic Function. Biotechnology and Bioengineering 108(1):127-140.