Nanotech 2007 Vol. 1
Nanotech 2007 Vol. 1
Technical Proceedings of the 2007 NSTI Nanotechnology Conference and Trade Show, Volume 1

Computational Modeling in Life Sciences Chapter 9

Conditional Probabilistic Modeling of Carbohydrate Metabolism

Authors: I. Barjis and M. Sierra

Affilation: New York City College of Technology, United States

Pages: 642 - 645

Keywords: modeling, HMM, carbphydrate

Abstract:
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.

Conditional Probabilistic Modeling of Carbohydrate Metabolism

ISBN: 1-4200-6182-8
Pages: 726
Hardcopy: $139.95