# Benchmarking Evolutionary Algorithms on Convenience Kinetics Models of the Valine and Leucine Biosynthesis in C. glutamicum

### Abstract

An important problem in systems biology is parameter estimation for biochemical system models. Our work concentrates on the metabolic subnetwork of the valine and leucine biosynthesis in Corynebacterium glutamicum, an anaerobic actinobacterium of high biotechnological importance. Using data of an in vivo experiment measuring 13 metabolites during a glucose stimulus-response experiment we investigate the performance of various Evolutionary Algorithms on the parameter inference problem in biochemical modeling. Due to the inconclusive information on the reversibility of the reactions in the pathway, we develop both a reversible and an irreversible differential equation model based on the recent convenience kinetics approach. As the reversible model allows better approximation on the whole, we use it to analyze the impact of different settings on four especially promising EAs. We show that Particle Swarm Optimization as well as Differential Evolution are useful methods for parameter estimation on convenience kinetics models outperforming Genetic Algorithm and Evolution Strategy approaches and nearly reaching the quality of independent spline approximations on the raw data.

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### BibTeX

@inproceedings{Draeger2007b,
author = {Dr\"ager, Andreas and Kronfeld, Marcel and Supper, Jochen and Planatscher,
Hannes and Magnus, J{\o}rgen B. and Oldiges, Marco and Zell, Andreas},
title = {{Benchmarking Evolutionary Algorithms on Convenience Kinetics Models
of the Valine and Leucine Biosynthesis in \emph{C.~glutamicum}}},
booktitle = {IEEE Congress on Evolutionary Computation (CEC 2007)},
year = {2007},
editor = {Srinivasan, Dipti and Wang, Lipo},
pages = {896--903},
month = sep,
organization = {IEEE Computational Intelligence Society},
publisher = {IEEE Press},
abstract = {An important problem in systems biology is parameter estimation for
biochemical system models. Our work concentrates on the metabolic
subnetwork of the valine and leucine biosynthesis in \emph{Corynebacterium
glutamicum}, an anaerobic actinobacterium of high biotechnological
importance. Using data of an in vivo experiment measuring 13 metabolites
during a glucose stimulus-response experiment we investigate the
performance of various Evolutionary Algorithms on the parameter inference
problem in biochemical modeling. Due to the inconclusive information
on the reversibility of the reactions in the pathway, we develop
both a reversible and an irreversible differential equation model
based on the recent convenience kinetics approach. As the reversible
model allows better approximation on the whole, we use it to analyze
the impact of different settings on four especially promising EAs.
We show that Particle Swarm Optimization as well as Differential
Evolution are useful methods for parameter estimation on convenience
kinetics models outperforming Genetic Algorithm and Evolution Strategy
approaches and nearly reaching the quality of independent spline
approximations on the raw data.},
doi = {10.1109/CEC.2007.4424565},
isbn = {1-4244-1340-0},
keywords = {systems biology, mathematical modeling, benchmark, evolutionary algorithms,
valine and leucine biosynthesis, Corynebacterium glutamicum, convenience
kinetics},
notes = {CEC 2007 - A joint meeting of the IEEE, the EPS, and the IET. IEEE
Catalog Number: 07TH8963C},
pdf = {http://www.cogsys.cs.uni-tuebingen.de/publikationen/2007/Draeger2007b.pdf},
url = {http://dx.doi.org/10.1109/CEC.2007.4424565}
}