Abstract:Based on the idea that cellular metabolism is a survival-driven maneuvering of chemical reactions by (i) preferential synthesis of enzymes which catalyze reactions, and (ii) control of enzyme activities, models have been developed by the Ramkrishna research group for predicting the behavior of organisms and their genetic variants. These models have shown unprecedented capacity for prediction of performance as determined by comparison with observations on quantities that can be measured by experiment (see sample references below). With the advent of omic methodology data on biological systems contain information that must somehow be deciphered to make more detailed evaluation of such models.
Classical information theory (as assessed by STC experts) does not measure up to extracting information from data obtained of complex (biological) systems for various reasons. The prime objective of the center is to extend the boundaries of current information theory. Based on cybernetic formulations, our metabolic models can generate model-simulated data that can serve as a powerful testing ground for new information theories towards extracting underlying mechanisms. The same information theories can then be used to test detailed dynamic models from real omic data.
Nonlinear analysis of cybernetic models predict that cybernetic mechanisms lead to a large multiplicity of metabolic steady states for an organism in a given environment that may be reached depending on their paths to steady state behavior. A goal of information theory would be to secure proof of such multiplicities from gleaning data on enzyme expression profiles and thus the underlying cybernetic mechanisms.

The project will involve close collaboration among the following:Ramki, PI (ChE), Vernon (CS), Wojtek (CS), Aditya (CS). A graduate student from ChE will be recruited this Fall (2010).
References:Young, J. D., K. L. Henne, J. A, Morgan, A. E. Konopka and D. Ramkrishna, “Integrating Cybernetic Modeling with Pathway Analysis. A Dynamic Systems Level Description of Metabolic Control,” Biotechnol & Bioeng., 100, 542-559, 2008.
Song, Hyun-Seob and D. Ramkrishna, “Prediction of Metabolic Function from Limited Data: Lumped Hybrid Cybernetic Modeling (L-HCM), Biotechnol & Bioeng., 106, 271-284, 2010.
Contributed by Shankar Subramaniam.