Parameter estimation and inference for stochastic reaction-diffusion systems: application to morphogenesis in D. melanogaster
Parameter estimation and inference for stochastic reaction-diffusion systems: application to morphogenesis in D. melanogaster
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DOI：
10.1186/1752-0509-4-21
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发表时间：
2010-03-10
影响因子：
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通讯作者：
Sanguinetti, Guido
中科院分区：
文献类型：
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作者：
Dewar, Michael A.;Kadirkamanathan, Visakan;Sanguinetti, Guido
背景：反应扩散系统在系统生物学中经常被用来模拟发育和信号过程。在许多应用中，扩散分子种类的计数非常低，导致需要使用随机方法明确地模拟固有的可变性。尽管它们的重要性和频繁的使用，参数估计确定性和随机反应扩散系统仍然是一个具有挑战性的问题。结果：我们提出了一种贝叶斯推理方法来解决随机反应扩散系统的参数估计和状态估计问题。这允许确定参数的完整后验分布（期望值和不确定性）。我们通过一个简单的合成实验来验证该方法。然后，我们在黑腹果蝇体内形态原Bicoid扩散的真实数据上对该方法进行了测试。结果表明，参数推断的精度变化很大，表明推断参数的完全后验分布的能力可以对实验设计产生重要影响。结论：所得结果证明了贝叶斯方法应用于随机反应扩散系统参数估计的可行性和潜在优势。特别是，估计与参数估计相关的可信区间的能力对于实验设计是非常宝贵的。然而，需要进一步的工作来确保该方法可以扩展到更大的问题。
Background: Reaction-diffusion systems are frequently used in systems biology to model developmental and signalling processes. In many applications, count numbers of the diffusing molecular species are very low, leading to the need to explicitly model the inherent variability using stochastic methods. Despite their importance and frequent use, parameter estimation for both deterministic and stochastic reaction-diffusion systems is still a challenging problem.Results: We present a Bayesian inference approach to solve both the parameter and state estimation problem for stochastic reaction-diffusion systems. This allows a determination of the full posterior distribution of the parameters (expected values and uncertainty). We benchmark the method by illustrating it on a simple synthetic experiment. We then test the method on real data about the diffusion of the morphogen Bicoid in Drosophila melanogaster. The results show how the precision with which parameters can be inferred varies dramatically, indicating that the ability to infer full posterior distributions on the parameters can have important experimental design consequences.Conclusions: The results obtained demonstrate the feasibility and potential advantages of applying a Bayesian approach to parameter estimation in stochastic reaction-diffusion systems. In particular, the ability to estimate credibility intervals associated with parameter estimates can be precious for experimental design. Further work, however, will be needed to ensure the method can scale up to larger problems.