An accurate and efficient estimation of enzyme kinetics using Bayesian approach

11 Jul 2018, 10:30
New Law School/--026 (University of Sydney)

New Law School/--026

University of Sydney

Oral Presentation Minisymposium: Multiscaling methods and parameter inference in stochastic biochemical networks Multiscaling methods and parameter inference in stochastic biochemical networks


Boseung Choi (Korea University Sejong Campus)


Characterizing enzyme kinetics is critical to understand cellular systems and to utilize enzymes in industry. To estimate enzyme kinetics from reaction progress curves of substrates, the Michaelis-Menten equation has been widely used for a century. However, this canonical approach works in a limited condition such as a large excess of substrate over enzyme. Even when such condition is satisfied, identifiability of parameters is not guaranteed, and criteria for the identifiability is often not easy to be tested. To overcome these limits of the canonical approach, here we propose a Bayesian inference based on an equation derived with the total quasi-steady state approximation. Estimates obtained with this approach have a little bias for any combination of enzyme and substrate concentrations in contrast to the canonical approach. Furthermore, with our new approach, an optimal experiment leading to maximal increase of the identifiability can be easily designed by simply analyzing scatter plots of estimates. Indeed, with this optimal protocol, kinetics of diverse enzymes with disparate catalytic efficiencies such as chymotrypsin, fumarase and urease can be accurately estimated from a minimal number of experimental data. A Bayesian inference computational package that performs such accurate and efficient enzyme kinetics is provided in this work.

Primary author

Boseung Choi (Korea University Sejong Campus)


Prof. Grzegorz Rempała (The Ohio State University) Dr Jae Kyoung Kim (Korea Advanced Institute of Science and Technology)

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