From network models to network responses: integration of thermodynamic and kinetic properties of yeast genome-scale metabolic networks

Abstract

Many important problems in cell biology arise from the dense nonlinear interactions between functional modules. The importance of mathematical modelling and computer simulation in understanding cellular processes is now indisputable and widely appreciated. Genome-scale metabolic models have gained much popularity and utility in helping us to understand and test hypotheses about these complex networks. However, there are some caveats that come with the use and interpretation of different types of metabolic models, which we aim to highlight here. We discuss and illustrate how the integration of thermodynamic and kinetic properties of the yeast metabolic networks in network analyses can help in understanding and utilizing this organism more successfully in the areas of metabolic engineering, synthetic biology and disease treatment.

Identification of metabolic engineering targets for the enhancement of 1,4-butanediol production in recombinant E. coli using large-scale kinetic models

“…We used the well-established ORACLE (Optimization and Risk Analysis of Complex Living Entities) methodology that integrates in a consistent way thermodynamic and physicochemical constraints of the cellular metabolism along with diverse experimental data (fluxomics, metabolomics, transcriptomics, proteomics, and kinetics) into mathematical descriptions of the responses of the cellular metabolism to genetic and environmental perturbations (Chakrabarti et al, 2013;Miskovic and Hatzimanikatis, 2010;Soh et al, 2012;Wang et al, 2004;Wang and Hatzimanikatis, 2006a;Wang and Hatzimanikatis, 2006b). ORACLE allows us to build a population of large-scale kinetic models that account for the uncertain and scarce information about the kinetic properties of enzymes.…”

“…While we can eliminate infeasible loops in the steady-state metabolic models with no information about thermodynamics (Lewis et al, 2010;Schellenberger et al, 2011a), the Thermodynamics-based Flux Balance Analysis (TFA) additionally allows to eliminate thermodynamically infeasible flux directionalities and to integrate metabolomics data in the constraint-based analyses Henry et al, 2006;Henry et al, 2007;Soh and Hatzimanikatis, 2010b;Soh and Hatzimanikatis, 2014;Soh et al, 2012). TFA also allows to integrate information about other factors that can affect the metabolic responses by altering the standard change of Gibbs free energy of reactions such as pH, ionic strength and temperature.…”

“…Within this thermodynamically feasible flux profile each reaction is unidirectional, therefore its flux solution space is convex. We sampled this flux space, and we performed the Principal Component Analysis (PCA) on the obtained samples to find the representative steady-state flux vector that would characterize the studied physiology (Jolliffe, 2002;Soh et al, 2012).…”

“…Available estimates of metabolite concentration ranges acquired from experiments under similar physiological conditions were used as the bounds for the computational sampling of the metabolite levels (Soh et al, 2012). Using the values of the Gibbs free energy we computed the displacement of the enzymatic reactions from the thermodynamic equilibrium, which were consistent with the previously determined metabolite levels and flux profiles.…”

“…In this contribution, we used the kinetic modeling framework ORACLE (Optimization and Risk Analysis of Complex Living Entities) (Chakrabarti et al, 2013;Miskovic and Hatzimanikatis, 2011;Soh et al, 2012;Wang et al, 2004;Wang and Hatzimanikatis, 2006a;Wang and Hatzimanikatis, 2006b) to analyze possible enhancements of an E. coli strain engineered for production of BDO. ORACLE framework allowed us to integrate thermodynamics and available omics and kinetic data into a population of large-scale kinetic models.…”