Regulating Phenotypic Variations Using Integrated Flux and Energy Balance Analysis Based Multiobjective Framework

Deepak Nagrath1, Marco Avila1, Francois Berthiaume2, and Martin L. Yarmush3. (1) Massachusetts General Hospital/Shriners Burn Hospital/Harvard Medical School, 51 Blossom Street, Boston, MA 02114, (2) Center for Engineering in Medicine, Harvard Medical School, 51 Blossom St, Boston, MA 02114, (3) Center for Engineering in Medicine, 51 Blossom Street, Boston, MA 02114

The characterization of intermediary metabolism in hepatocytes is important in for the development of bioartificial liver devices, for studying the intracellular activity of liver under various physiological and disease states and for the preconditioning and preservation of doner livers . Flux balance analysis (FBA) provides a framework for the estimation and distribution of intracellular fluxes. FBA has been limited to single objective functions. Thus, the optimal solutions obtained tend to be sub-optimal when several objectives are desired to be optimal. Further, there is a lack of understanding of when to use a particular objective, and how to combine and/or prioritize mutually competing objectives to achieve a true optimal solution. In this study, these limitations are addressed by presenting a constraint-based multiobjective framework to quantify the optimal fluxes in intracellular pathways of hepatic systems. First, a set of Pareto solutions are generated for various liver specific bi-objectives in bioartificial liver and hypermetabolic liver systems using physical programming and normal constraint-based multiobjective framework. Next, simultaneous analysis of the optimal solutions for four objectives are presented for bioartificial liver and hypermetabolic systems. The novel multiobjective optimization approach is coupled with energy balance analysis which ensures the thermodynamic feasibility of the computed optimal fluxes.