Iterative design of training data to control intricate enzymatic reaction networks.

ABSTRACT: Kinetic modeling of in vitro enzymatic reaction networks is vital to understand and control the complex behaviors emerging from the nonlinear interactions inside. However, modeling is severely hampered by the lack of training data. Here, we introduce a methodology that combines an active learning-like approach and flow chemistry to efficiently create optimized datasets for a highly interconnected enzymatic reactions network with multiple sub-pathways. The optimal experimental design (OED) algorithm designs a sequence of out-of-equilibrium perturbations to maximize the information about the reaction kinetics, yielding a descriptive model that allows control of the output of the network towards any cost function. We experimentally validate the model by forcing the network to produce differen

SUBMITTER: van Sluijs B 

PROVIDER: S-EPMC10881569 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature