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Activity Number:
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343
- SPEED: Tests, Trials, Biomarkers, and Other Topics in Biometrics
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Type:
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Contributed
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Date/Time:
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Tuesday, July 31, 2018 : 10:30 AM to 12:20 PM
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Sponsor:
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Biometrics Section
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Abstract #328960
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Presentation
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Title:
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An Estimation Method for Enzyme Kinetic Model Parameters Based on Bayesian Approach
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Author(s):
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Boseung Choi* and Jae Kyoung Kim and Grzegorz A Rempala
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Companies:
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Korea University and Korea Advanced Institute of Science and Technology and The Ohio State University
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Keywords:
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Enzyme kinetics;
Michales-Menten equation;
sQSSA;
tQSSA;
Gillespie method;
MCMC
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Abstract:
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Michaelis-Menten equation derived using the standard quasi steady-state approximation (sQSSA) has been widely used for the estimation of enzyme kinetic parameters. However, such approximation is only valid when enzyme concentrations is low. Thus, we found that the estimation can be biased when enzyme concentration is high. On the other hand, we found that a newly reduced model using the total QSSA (tQSSA) provides the accurate estimation of parameters regardless of enzyme concentration. For the estimation of parameters, we utilized the Markov Chain Monte Carlo (MCMC) based on the Bayesian approach to estimate parameter using non-informative and informative priors.
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Authors who are presenting talks have a * after their name.
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