Uncertainty reduction in biochemical kinetic models: Enforcing desired model properties.

ABSTRACT: A persistent obstacle for constructing kinetic models of metabolism is uncertainty in the kinetic properties of enzymes. Currently, available methods for building kinetic models can cope indirectly with uncertainties by integrating data from different biological levels and origins into models. In this study, we use the recently proposed computational approach iSCHRUNK (in Silico Approach to Characterization and Reduction of Uncertainty in the Kinetic Models), which combines Monte Carlo parameter sampling methods and machine learning techniques, in the context of Bayesian inference. Monte Carlo parameter sampling methods allow us to exploit synergies between different data sources and generate a population of kinetic models that are consistent with the available data and physicochemical law

SUBMITTER: Miskovic L 

PROVIDER: S-EPMC6716680 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature