Bayesian design strategies for synthetic biology
Bayesian design strategies for synthetic biology
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DOI：
10.1098/rsfs.2011.0056
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发表时间：
2011-12-06
期刊：
影响因子：
4.4
通讯作者：
Stumpf, Michael P. H.
中科院分区：
文献类型：
--
作者：
Barnes, Chris P.;Silk, Daniel;Stumpf, Michael P. H.
我们创造了新的生物系统。贝叶斯技术在系统生物学社区中得到了广泛的应用和接受，在那里它们被用于参数估计和模型选择。在这里，我们表明，同样的方法也可以用于工程合成生物系统，通过推断的结构和参数，最有可能引起的动态，我们需要一个系统来展示。在系统和合成生物学的应用程序之间共享的问题，包括巨大的潜在空间，需要寻找合适的模型和模型参数;复杂形式的似然函数;和噪声之间的相互作用在分子水平和非线性的动力学由于往往是复杂的反馈结构。为了应对这些挑战，我们必须开发合适的推理工具，在这里，特别是，我们说明了使用近似贝叶斯计算和无迹卡尔曼滤波为基础的方法。这些部分互补的方法使我们能够解决生物系统设计中经常出现的一些问题。在简单介绍了这两种方法之后，我们着重讨论了它们在振动系统中的应用。
We novel biological systems. Bayesian techniques have found widespread application and acceptance in the systems biology community, where they are used for both parameter estimation and model selection. Here we show that the same approaches can also be used in order to engineer synthetic biological systems by inferring the structure and parameters that are most likely to give rise to the dynamics that we require a system to exhibit. Problems that are shared between applications in systems and synthetic biology include the vast potential spaces that need to be searched for suitable models and model parameters; the complex forms of likelihood functions; and the interplay between noise at the molecular level and non-linearity in the dynamics owing to often complex feedback structures. In order to meet these challenges, we have to develop suitable inferential tools and here, in particular, we illustrate the use of approximate Bayesian computation and unscented Kalman filtering-based approaches. These partly complementary methods allow us to tackle a number of recurring problems in the design of biological systems. After a brief exposition of these two methodologies, we focus on their application to oscillatory systems.