Soft constraints-based multiobjective framework for flux balance analysis

Abstract

The current state of the art for linear optimization in Flux Balance Analysis has been limited to single objective functions. Since mammalian systems perform various functions, a multiobjective approach is needed when seeking optimal flux distributions in these systems. In most of the available multiobjective optimization methods, 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 truly optimal solution. To address these limitations we developed a soft constraints based linear physical programming-based flux balance analysis (LPPFBA) framework to obtain a multiobjective optimal solutions. The developed framework was first applied to compute a set of multiobjective optimal solutions for various pairs of objectives relevant to hepatocyte function (urea secretion, albumin, NADPH, and glutathione syntheses) in bioartificial liver systems. Next, simultaneous analysis of the optimal solutions for three objectives was carried out. Further, this framework was utilized to obtain true optimal conditions to improve the hepatic functions in a simulated bioartificial liver system. The combined quantitative and visualization framework of LPPFBA is applicable to any large-scale metabolic network system, including those derived by genomic analyses.

Integrating tracer-based metabolomics data and metabolic fluxes in a linear fashion via Elementary Carbon Modes

“…In order to evaluate the effect of our novel constraints based on ECMs in reducing the underlying under-determination of metabolic networks at the flux level, we used here Flux Variability Analysis (FVA) (Mahadevan and Schilling, 2003). Note here that the integration of our set of constraints with others methods from CBM (Baughman et al, 2011;Chemler et al, 2010;Nagrath et al, 2010;Xu et al, 2011) can be easily done.…”

Advanced Stoichiometric Analysis of Metabolic Networks of Mammalian Systems

“…In this case, Pareto-optimal solutions satisfying all the objectives simultaneously can be analyzed. 117–119 Nagrath et al 117,118 developed a multi-objective optimization approach that couples the normalized normal constraint (NC) with both FBA and energy balance analysis (EBA) to obtain multi-objective Pareto-optimal solutions. They investigated the Pareto frontiers in gluconeogenic and glycolytic hepatocytes for various combinations of liver-specific objectives (e.g., albumin synthesis, glutathione synthesis, NADPH synthesis, ATP generation, and urea secretion).…”

Physical programming for preference driven evolutionary multi-objective optimization

“…Since it defines desirable, tolerable, and undesirable ranges for individual objectives, it becomes a potential technique to improve the pertinency of solutions in multi-objective optimisation. PP has been merged previously with classical optimisation techniques [1,36]; nevertheless, it remains an interesting topic to merge with MOEAs.…”