School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, UK.
Bioinformatics. 2013 Apr 1;29(7):910-6. doi: 10.1093/bioinformatics/btt069. Epub 2013 Feb 13.
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.
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.
http://homepages.inf.ed.ac.uk/gsanguin/software.html.
Supplementary data are available at Bioinformatics online.
基因调控网络动态的计算建模是系统生物学的一项核心任务。对于小规模/中等规模的网络，占主导地位的范例是由耦合非线性常微分方程（ODE）系统表示的。ODE 提供了很大的机械细节和灵活性，但将这些模型校准到数据通常是一个极其困难的统计问题。
在这里，我们为随机转录 - 翻译网络开发了一个通用的统计推断框架。我们使用粗粒度的方法，将系统表示为一个随机（二进制）启动子和（连续）蛋白质变量的网络。我们推导出一个精确的推断算法和一个有效的变分逼近，允许可扩展的推断和模型参数的学习。我们在两个生物学案例研究中展示了该方法的强大功能，表明该方法具有很高的灵活性，并能够进行可测试的新的生物学预测。
http://homepages.inf.ed.ac.uk/gsanguin/software.html。
补充数据可在 Bioinformatics 在线获得。
