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Event Entry
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What and Who
Evaluation of Aggregation Techniques for the Enzyme-Catalyzed Substrate Conversion Modeling
Hassan Hatefiardakani
Saarbrücken Graduate School of Computer Science
PhD Application Talk
AG 1, AG 2, AG 3, AG 4, AG 5, SWS, RG1, MMCI
Public Audience
English
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Date, Time and Location
Friday, 15 October 2010
11:00
90 Minutes
E1 4
024
Saarbrücken
Abstract
Mathematical modeling of an Enzyme-Catalyzed Substrate Conversion usually leads to very large scale Continuous-Time Markov Chains (CTMC). If reaction rates in the conversion are different in orders of magnitude, the analysis of the model will require solving a stiff Ordinary Differential Equation (ODE) which is computationally expensive. One way to avoid the complexity of the exact model is to utilize an approximation (aggregation) technique to make the exact model smaller and non-stiff. In this work, we compare two general aggregation techniques, one based on system partitioning and, the other based on Michaelis-Menten Approximation. Results demonstrate the improvements of our proposed method based on system partitioning in terms of accuracy over Michaelis-Menten Approximation.
Keywords: Enzyme-Catalyzed Substrate Conversion, Continuous-Time Markov Chain (CTMC), Aggregation, Michaelis-Menten Approximation.
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Stephanie Jörg, 10/14/2010 14:23 -- Created document.