Koptic: A Novel Approach for in silico Prediction of Enzyme Kinetics and Regulation | AIChE

Koptic: A Novel Approach for in silico Prediction of Enzyme Kinetics and Regulation


Schroeder, W. - Presenter, The Pennsylvania State University
Saha, R., University of Nebraska-Lincoln
Computational modeling of metabolism enables the design of engineering interventions directed to the overproduction of a specific bioproduct or improvement of plant performance. Flux Balance Analysis (FBA) is the primary tool used for this purpose, but has significant limitations due to the lack of reaction kinetics, chemical species concentration, and metabolic regulation. In contrast, kinetic models of metabolism (kMMs) provide not only a more accurate method for designing novel biological systems but also for the characterization of reaction kinetics, metabolite concentration, and metabolic regulation in these systems; however, the multi-omics data required for their construction is prohibitive to their development and widespread use.

Here, we introduce Kinetic OPTimization using Integer Conditions (KOPTIC), which can circumvent the omics data requirement and semi-automate kMM construction by using reaction rates and concentration data derived from a metabolic network model to return plausible kinetic mechanisms through an optimization-based approach. Arabidopsis thaliana’s (hereafter Arabidopsis’s) prominent role in ‘omics’ and plant science research makes it an ideal organism for the verification of KOPTIC-predicted kinetic mechanisms. While, several metabolic network models for A. thaliana already exist, the core carbon metabolism of this organism was chosen as the test system. A four-tissues (leaf, root, seed, and stem) metabolic network model, was reconstructed for Arabidopsis (1015 reactions, 901 metabolites, 508 genes). FBA was performed at 71 time-points to simulate the Arabidopsis lifecycle, and KOPTIC was applied to the FBA data. In total KOPTIC predicted 3577 regulatory interactions with a median fit error of 13.44%. More than 30 verified by existing literature. This research showcases how an optimization-based approach can be used to create meaningful hypotheses of reaction kinetics and increase mechanistic understanding of metabolism.