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Published on: February 20, 2009
Efficient parameter estimation for spatio-temporal models of pattern formation: case study of Drosophila melanogaster
Yves Fomekong-Nanfack1, Jaap A Kaandorp, Joke Blom
1Section Computational Science, Faculty of Science, University of van Amsterdam, Kruislaan 403, 1098 SJ Amsterdam, The Netherlands.
Bioinformatics (Oxford, England)
|September 26, 2007
Summary
Evolution strategies (ES) efficiently estimate parameters for gene regulatory network models. This computational method accelerates understanding of body plan formation in organisms like Drosophila melanogaster.
Area of Science:
- Developmental Biology
- Computational Biology
- Systems Biology
Background:
- Gene products are crucial for body plan formation.
- Quantitative simulation models aid understanding of spatio-temporal patterns.
- Parameter estimation for these models is computationally intensive.
Purpose of the Study:
- To investigate efficient parameter estimation for pattern formation models.
- To apply inverse modeling to a quantitative spatio-temporal model of Drosophila melanogaster development.
Main Methods:
- Utilized an evolution strategy (ES) for parameter estimation.
- Compared simulated results to experimental data from immunohistochemistry.
- Employed a (mu,lambda)-ES and multi-population ES.
Main Results:
- ES effectively estimates parameters for pattern formation models.
- A (mu,lambda)-ES finds good quality solutions for parameter estimation.
- Multi-population ES is significantly faster than parallel simulated annealing.
Conclusions:
- Evolution strategies offer an efficient method for parameter estimation in developmental models.
- Combining ES with local search enhances parameter estimation efficiency.
- This approach aids in understanding gene product roles in body plan formation.
