A Bayesian Approach to Extracting Kinetic Information from Artificial Enzymatic Networks.

ABSTRACT: In order to create artificial enzymatic networks capable of increasingly complex behavior, an improved methodology in understanding and controlling the kinetics of these networks is needed. Here, we introduce a Bayesian analysis method allowing for the accurate inference of enzyme kinetic parameters and determination of most likely reaction mechanisms, by combining data from different experiments and network topologies in a single probabilistic analysis framework. This Bayesian approach explicitly allows us to continuously improve our parameter estimates and behavior predictions by iteratively adding new data to our models, while automatically taking into account uncertainties introduced by the experimental setups or the chemical processes in general. We demonstrate the potential of this a

SUBMITTER: Baltussen MG 

PROVIDER: S-EPMC9134183 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature