2007 NSTI Nanotechnology Conference and Trade Show - Nanotech 2007 - 10th Annual

Conditional Probabilistic Modeling of Carbohydrate Metabolism

I. Barjis and M. Sierra
New York City College of Technology, US

modeling, HMM, carbphydrate

The ongoing processes of metabolic network are very complex, and consequently, difficult to understand and teach; furthermore, it is impossible to predict and analyze it when unpredictable changes are made. Because of the complexity of metabolic networks and their regulation, formal modeling is a useful method to improve the understanding of these systems. To achieve our goal, we’ve used probabilistic modeling methods to model, analyze, and simulate the process of carbohydrate metabolism in a very compact notation. In particular our research is directed to the development of new probabilistic model of complex biological process such as carbohydrate metabolism. In this paper we use Hidden Markov Models (HMMs) and conditional statistics to model and simulate the process of carbohydrate metabolism.

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