Hybrid regulatory models: a statistically tractable approach to model regulatory network dynamics
Hybrid regulatory models: a statistically tractable approach to model regulatory network dynamics
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DOI：
10.1093/bioinformatics/btt069
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发表时间：
2013-04-01
期刊：
影响因子：
5.8
通讯作者：
Sanguinetti, Guido
中科院分区：
文献类型：
--
作者：
Ocone, Andrea;Millar, Andrew J.;Sanguinetti, Guido
动机：基因调控网络动力学的计算建模是系统生物学的中心任务。对于中小规模的网络，主要的范例是由耦合的非线性常微分方程（ODE）的系统。ODE提供了很大的机械细节和灵活性，但将这些模型与数据进行校准通常是一个极其困难的统计问题。结果：在这里，我们为随机转录-翻译网络开发了一个通用统计推理框架。我们使用一个粗粒度的方法，它表示系统作为一个网络的随机（二进制）启动子和（连续）蛋白质变量。我们推导出一个精确的推理算法和一个有效的变分近似，允许可扩展的推理和学习的模型参数。我们展示了两个生物案例研究的方法的力量，表明该方法允许高度的灵活性，并能够测试新的生物预测。
Motivation: Computational modelling of the dynamics of gene regulatory networks is a central task of systems biology. For networks of small/medium scale, the dominant paradigm is represented by systems of coupled non-linear ordinary differential equations (ODEs). ODEs afford great mechanistic detail and flexibility, but calibrating these models to data is often an extremely difficult statistical problem.Results: Here, we develop a general statistical inference framework for stochastic transcription-translation networks. We use a coarse-grained approach, which represents the system as a network of stochastic (binary) promoter and (continuous) protein variables. We derive an exact inference algorithm and an efficient variational approximation that allows scalable inference and learning of the model parameters. We demonstrate the power of the approach on two biological case studies, showing that the method allows a high degree of flexibility and is capable of testable novel biological predictions.