An automated computational approach to kinetic model discrimination and parameter estimation
Connor J. Taylor, Hikaru Seki, Friederike M. Dannheim, Mark J. Willis, Graeme Clemens, Brian Taylor, Thomas W. Chamberlain, Richard A. Bourne
Abstract
) and corresponding time-series concentration data to determine the kinetic information of the chemistry of interest. This is performed with minimal human interaction and several case studies were performed to show the wide scope and applicability of this process development tool. The approach described herein can be employed using experimental data from any source and the code for this methodology is also provided open-source.
Topics & Concepts
Identification (biology)Estimation theoryComputer scienceBiological systemComputational modelArtificial intelligenceAlgorithmBiologyBotanyComputational Drug Discovery MethodsMachine Learning in Materials ScienceMicrobial Metabolic Engineering and Bioproduction