Erratum to: A coupled thermodynamic and metabolic control analysis methodology and its evaluation on glycerol biosynthesis in Saccharomyces cerevisiae

Abstract

A coupled in silico thermodynamic and probabilistic metabolic control analysis methodology was verified by applying it to the glycerol biosynthetic pathway in Saccharomyces cerevisiae. The methodology allows predictions even when detailed knowledge of the enzyme kinetics is lacking. In a metabolic steady state, we found that glycerol-3-phosphate dehydrogenase operates far from thermodynamic equilibrium (D r G 0 1 -15.9 to -47.5 kJ mol -1 , where D r G 0 1 is the transformed Gibbs energy of the reaction). Glycerol-3-phosphatase operates in modes near the thermodynamic equilibrium, far from the thermodynamic equilibrium or in between (D r G 0 2 & 0 to -23.7 kJ mol -1 ). From the calculated distribution of the scaled flux control coefficients (median = 0.81), we inferred that the pathway flux is primarily controlled by glycerol-3-phosphate dehydrogenase. This prediction is consistent with previous findings, verifying the efficacy of the proposed methodology.

iSCHRUNK – In Silico Approach to Characterization and Reduction of Uncertainty in the Kinetic Models of Genome-scale Metabolic Networks

“…This inspired the development of new modeling frameworks that exploit the sets of additional thermodynamic and physicochemical 6 constraints and integrate available data coming from several levels to reduce the space of admissible parameter values (Chakrabarti et al, 2013;Jamshidi and Palsson, 2010;Miskovic and Hatzimanikatis, 2010;Miskovic and Hatzimanikatis, 2011;Soh et al, 2012;Tran et al, 2008;Wang et al, 2004;Wang and Hatzimanikatis, 2006a;Wang and Hatzimanikatis, 2006b). Some of these approaches use Monte Carlo sampling techniques to extract populations of parameter sets capable of reproducing the observed physiology (Birkenmeier et al, 2015a;Birkenmeier et al, 2015b;Chakrabarti et al, 2013;Miskovic and Hatzimanikatis, 2010;Murabito et al, 2014;Soh et al, 2012;Tran et al, 2008;Wang et al, 2004;Wang and Hatzimanikatis, 2006a;Wang and Hatzimanikatis, 2006b). However, the sheer size of the admissible space that spans through the spaces of kinetic parameters, metabolite concentrations and metabolic fluxes along with the intrinsic nonlinearities of enzyme kinetics require tailored formulations and efficient parameter estimation techniques that are scalable and that can ultimately provide a detailed description of the metabolism.…”

Control Theory Concepts for Modeling Uncertainty in Enzyme Kinetics of Biochemical Networks

“…Furthermore, the efficient sampling procedure here discussed lends itself to application of Bayesian inference when evaluating the likelihood function is computationally intractable, and instead, approximate Bayesian computation (ABC) can be used to approximate this function . To this end, the sampling procedure is employed to generate the prior distribution of kinetic parameters for any of the methods based on Monte Carlo sampling. ,,− Then, parameter vectors from the prior distribution that gave rise to undesired model properties are rejected, and the kept samples form the approximate posterior distribution of kinetic parameters with desired model properties.…”

“…The formalism, coupled with the network information obtained from methods such as TFA, allow us to predict the responses of biochemical networks to genetic and environmental variations. Efficient sampling procedures for generating missing kinetic data used in the formalism represent a valuable tool for methods that use Monte Carlo sampling to generate populations of large-scale kinetic models. ,,,− …”

Control Theory Concepts for Modeling Uncertainty in Enzyme Kinetics of Biochemical Networks

“…42, we obtain for the denominator 1 − a b Õ Ö × Ø , and the terms of the numerators are provided in Table 2. 36,37,[49][50][51][52][53][54][55][56][57][58][59][60][61][62] .…”