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Bioinformatics Advance Access published online on September 24, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm433
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© The Author (2007). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Efficient parameter estimation for spatio-temporal models of pattern formation: Case study of Drosophila melanogaster

Yves Fomekong Nanfack a,*, Jaap A. Kaandorp a and Joke Blom b

aUniversiteit van Amsterdam, Kruislaan 403, 1098 SJ Amsterdam, the Netherlands,
bCWI, Kruislaan 413, 1098 SJ Amsterdam, the Netherlands

*To whom correspondence should be addressed. Yves Fomekong Nanfack, E-mail: yvesf{at}science.uva.nl


  Abstract

Motivation: Diffusable and non-diffusable gene products play a major role in body plan formation. A quantitative understanding of the spatio-temporal patterns formed in body plan formation, by using simulation models is an important addition to experimental observation. The inverse modelling approach consists of describing the body plan formation by a rule-based model, and fitting the model parameters to real observed data. In body plan formation the data are usually obtained from fluorescent immunohistochemistry or in-situ hybridisations. Inferring model parameters by comparing such data to those from simulation is a major computational bottleneck. An important aspect in this process is the choice of method used for parameter estimation. When no information on parameters is available, parameter estimation is mostly done by means of heuristic algorithms.

Results: We show that parameter estimation for pattern formation models can be efficiently performed using an evolution strategy (ES). As a case study we use a quantitative spatio-temporal model of the regulatory network for early development in Drosophila melanogaster. In order to estimate the parameters, the simulated results are compared to a time series of gene products involved in the network obtained with immunohistochemistry. We demonstrate that a (µ, {lambda})-ES can be used to find good quality solutions in the parameter estimation. We also show that an ES with multiple populations is 5–140 times as fast as parallel simulated annealing for this case study, and that combining ES with a local search results in an efficient parameter estimation method.

Supplementary information and availability: Bioinformatics online; software: http://www.science.uva.nl/research/scs/3D-RegNet/fly_ea

Contact: yvesf{at}science.uva.nl

Associate Editor: Dr. Chris Stoeckert

Received on April 22, 2007; revised on August 16, 2007; accepted on August 17, 2007

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