Implications of the Assumptions on Intracellular Metabolic Operational States in Metabolic Control Analysis

Modeling and analysis of metabolic networks is inherently biased by assumptions regarding the intracellular metabolite and flux states. Despite the advanced metabolomics and fluxomics methods at our disposal, the sheer complexity of metabolic networks prevents us from determining their exact operational state. To address this shortcoming, we have developed a novel framework called Flux Directionality Profile Analysis (FDPA) for characterizing systematically all possible intracellular flux and thermodynamic states using available experimental information, such as metabolite concentration measurements and flux split ratios. In the current study, we demonstrated the utility of this framework in elucidating the different physiologically relevant, yet operationally different, functioning states of wild-type E. coli under optimal aerobic growth condition. Using the available information, we explored the set of possible states to identify the most important and consistent internal operational states for interpreting the observed physiology. To that end, we used the ORACLE framework to study the implications of the choice of different operational paradigms by analyzing their effects on control coefficients. Further analysis of the control coefficients allows us to classify the enzymes in terms of their impact across all, or only specific operational configurations. Using this approach, we can knowledgeably identify the right choice of enzyme targets, which shall help us in meeting metabolic engineering specifications.