Deciphering thermodynamics in metabolic networks: A priority

list of candidates for metabolomics.

A.Kiparissides, V. Hatzimanikatis

Laboratory of Computational Systems Biotechnology EPFL, CH-­�1015, Lausanne, Switzerland

Swiss Institute of Bioinformatics CH-­�1015, Lausanne, Switzerland

Abstract

Almost every process or action that occurs within a cell involves a metabolic reaction. Knowledge of the metabolic state of a cell and how it responds to various stimuli and extracellular conditions can offer significant insight in the regulatory functions and how to manipulate them. The increasing availability of large metabolomics datasets enhances the need for computational methodologies that can organize the data in a way that can lead to the inference of meaningful relationships. Metabolic models comprising the entirety of reactions known within a pathway and/or cell provide an increasingly popular and effective platform to study the internal states of a cell. Constraint based approaches are commonly employed to define a, usually near infinite, set of equally optimal feasible internal states the cell can operate in. 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. However a systematic methodology to prioritize and incorporate metabolomics data within constraint based metabolic models in order to refine the large solution space is currently lacking.
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 of intracellular metabolites on the thermodynamic flexibility of cellular metabolism. Metabolites are ranked based on their ability to constrain the range of possible solutions to a limited, thermodynamically consistent set of internal states. The proposed methodology effectively defines the amount of experimental information required to reduce uncertainty in defining the state of cellular metabolism (i.e the flux distribution
and displacement from thermodynamic equilibrium) by providing a ranked list of targets for metabolomics. The proposed approach is modular and can be applied to a single reaction, a metabolic pathway or an entire metabolic network. This is, to our knowledge, the first attempt to use metabolic modeling in order to provide a significance ranking of metabolites to guide experimental measurements.