(613c) Predicting Enzyme Kinetics and Regulations from Dynamic Flux Balance Analysis through Kinetic Optimization Using Integer Conditions

Stoichiometric models, or Genome-Scale Models (GSMs), are now an indispensable tool for systems biology, yet are limited in their predictive capabilities by their linear natures and general non-consideration of enzyme kinetics and regulation. In contrast, kinetic Models of Metabolism (kMMs) provide not only a more accurate method for designing novel biological systems but also characterization of system regulations; however, the multi-‘omics’ data required has proven prohibitive to the development of kMMs larger than a few hundred reactions (as opposed to GSMs which may have as many as ten thousand or more reactions). To address the lack of multi-‘omics’ data required to produce large kMMs, introduce here is a new approach named Kinetic OPTimization using Integer Conditions (KOPTIC) to make plausible reaction mechanism, kinetics, and regulation predictions. KOPTIC can circumvent the multi-‘omics’ data requirement and semi-automate kMM construction using an optimization-based approach which relies on in silico reaction flux data and metabolite concentration estimates from a dynamic Flux Balance Analysis (dFBA) of a stoichiometric model. As a benchmark for the performance of KOPTIC, a previously published, stochiometric, four-tissue (leaf, root, seed, and stem) metabolic model of Arabidopsis thaliana was used, consisting of the core metabolism of Arabidopsis, named p-ath773 (1251 reactions, 1155 metabolites, and 773 genes). The p-ath773 was subject to dFBA using the ORKA approach which simulated plant biomass, reaction rates, and metabolite concentration at each hour throughout a 61-day lifecycle of an Arabidopsis plant. This data was then used as the basis for the application KOPTIC. KOPTIC was applied in three distinct iterations to the p-ath773 data where each represents a different set of allowable regulators. The first allowed only within-path regulation, the second within subcellular-compartment, and the third within cell. This resulted in thousands of kinetic predictions, of which some have been verified by biochemical data. In summary, this demonstration of KOPTIC shows that KOPTIC can be useful in the addressing of knowledge gaps by circumventing them using dFBA and optimization.

W. L. Schroeder and Rajib Saha. “KOPTIC: a novel approach for in silico prediction of enzyme kinetics and regulation” BioRxiv, Oct. 17, 2019. Available: https://www.biorxiv.org/content/10.1101/807628v1 (doi: https://doi.org/10.1101/807628)