(658e) Koptic: A Novel Approach for in silico Prediction of Enzyme Kinetics and Regulation | AIChE

(658e) 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

KOPTIC: A novel approach for in silico
prediction of enzyme kinetics and regulation

Schroeder and Rajib Saha

University of Nebraska – Lincoln, Lincoln, NE

modeling of metabolism is now an indispensable tool to drive the processes of
understanding and redesigning of biological systems. These tools enable the
design of engineering interventions directed to the overproduction of a
specific bioproduct or improvement of plant performance, particularly in the
presence of feedback regulation. Although Flux Balance Analysis (FBA) is the
primary tool used for this purpose, it 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.


(hereafter Arabidopsis) was chosen to verify KOPTIC. Arabidopsis
is a model system for modern plant science due to its small genome, short
lifecycle, and ease of genetic manipulation. Its prominent role in omics and
plant science research makes it an ideal organism for the creation and
integration of emerging omics tools, including computational modeling of
metabolism. Indeed, several metabolic network models for A. thaliana
already exist, and many regulatory interactions are well studied and
documented. To verify KOPTIC, a four-tissue (leaf, root, seed, and stem) metabolic
network model, consisting of the major primary carbon metabolism pathways (1015
reactions, 901 metabolites, 508 genes), was reconstructed for Arabidopsis. FBA
was performed at 71 time-points in the of Arabidopsis
lifecycle to simulate 50 days of growth and 20 days of plant senescence and
death. KOPTIC was applied to the FBA data and predicted 3577 regulatory
interactions involving metabolic, allosteric, and transcriptional regulatory
mechanisms (with more than 30 verified by existing literature) and with a
median fit error of 13.44%. This research showcases how an optimization-based
approach can be used to create meaningful hypotheses of reaction kinetics, as
well as discover new biosynthetic pathways, regulations, and genes.
Additionally, KOPTIC serves as a novel tool to increase mechanistic
understanding of plant-scale regulatory mechanisms and answer biologically
relevant questions related to plant-scale metabolism, kinetics, and regulation.