(626c) Thermodynamics-Based Significance Ranking of Candidates for Metabolomics
Cellular metabolism is the driving force of all living cells and organisms. Numerous research projects undertaken over the past century have attempted to decipher the function and operating principles of metabolism with varying degrees of success. A wide spectrum of scientific disciplines have studied metabolic function and regulation from a variety of perspectives including, Biology, (Bio-) Chemistry, (Bio-) Engineering, Mathematics, Analytical Chemistry, Physics and Control Theory. These efforts have led to the development, amongst others, of a number of high throughput, –omics, techniques able to conduct measurements at the subcellular level and a number of accompanying mathematical and statistical tools for the refinement and evaluation of the collected information.
However the sheer amount of incoming information often obscures our vision by hiding significant systemic properties behind a multitude of seemingly less meaningful correlations. The ongoing increase in the popularity of Systems Biology over the past 15 years is a testament of the need for a systems level approach that can handle large datasets. Moreover the complete genome sequencing of an increasing number of cell types has increased the availability of genome scale metabolic reconstructions further promoting the expansion of the “systems” approach. Genome scale models, comprising the entirety of the metabolic reactions occurring within the cell, provide a system’s level overview of central carbon metabolism and offer valuable insight regarding optimal resource allocation towards a desired macroscopic behavior, usually maximizing the production of a valuable metabolite. Proper integration of experimental data to genome scale models still poses a significant challenge both from a data collection and a computational point of view.
Integration of experimental measurements of intracellular metabolite concentrations in genome scale models restricts the thermodynamically feasible flux space and reduces uncertainty regarding the net outcome of by-directional reactions. By combining Thermodynamics based Flux Balance Analysis (TFBA), Marcov Chain sampling, Experimental Design and Global Sensitivity Analysis we present an efficient algorithm to quantify the effect intracellular metabolites have on the thermodynamic flexibility of cellular metabolism. Metabolites are ranked based on the extent by which they reduce the thermodynamically feasible flux space when fixed at any value within their respective feasible bounds. The proposed methodology effectively defines the minimal amount of experimental information required to precisely describe the state of cellular metabolism (i.e the flux distribution) by providing a ranked list of targets for metabolomics.