Efficient parameter estimation for spatio-temporal models of pattern formation: Case study of Drosophila melanogaster
Bioinformatics , Volume 23 - Issue 24 p. 3356- 3363
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, ¸)-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.
|Energy (theme 4)|
|Simulation of developmental regulatory networks|
Fomekong Nanfack, Y, Kaandorp, J.A, & Blom, J.G. (2007). Efficient parameter estimation for spatio-temporal models of pattern formation: Case study of Drosophila melanogaster. Bioinformatics, 23(24), 3356–3363.