Efficient parameter estimation for spatio-temporal models of pattern formation:: case study of Drosophila melanogaster
Efficient parameter estimation for spatio-temporal models of pattern formation:: case study of Drosophila melanogaster
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DOI：
10.1093/bioinformatics/btm433
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发表时间：
2007-12-15
期刊：
影响因子：
5.8
通讯作者：
Blom, Joke
中科院分区：
文献类型：
--
作者：
Fomekong-Nanfack, Yves;Kaandorp, Jaap A.;Blom, Joke
动机：可扩散和不可扩散的基因产物在身体计划的形成中起着重要作用。通过使用仿真模型，对身体计划形成中形成的时空模式的定量理解是对实验观察的重要补充。逆建模方法包括由基于规则的模型描述的身体计划形成，并拟合模型参数的真实的观测数据。在体平面形成中，数据通常从荧光免疫组织化学或原位杂交获得。通过将这些数据与模拟数据进行比较来推断模型参数是一个主要的计算瓶颈。在这个过程中的一个重要方面是用于参数估计的方法的选择。当没有关于参数的信息是可用的，参数估计主要是通过启发式algorithm.Results：我们表明，模式形成模型的参数估计可以有效地使用进化策略（ES）。作为一个案例研究，我们使用的调控网络在果蝇早期发展的定量时空模型。为了估计的参数，模拟结果进行比较的时间序列的基因产物参与的网络与免疫组织化学。我们证明了（μ，λ）-ES可以用来找到高质量的解决方案中的参数估计。我们还表明，与多个人口的ES是5-140倍的速度为并行模拟退火这种情况下的研究，并结合ES与局部搜索结果在一个有效的参数估计方法。
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 hybridizations. 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 (mu, 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.