Maximizing information gain for the characterization of biomolecular circuits
Maximizing information gain for the characterization of biomolecular circuits
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DOI：
10.1145/3233188.3233217
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发表时间：
2018-09
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通讯作者：
Tim Prangemeier;C. Wildner;Maleen Hanst;H. Koeppl
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作者：
Tim Prangemeier;C. Wildner;Maleen Hanst;H. Koeppl
生物分子电路的定量预测模型是合成生物学和分子通信电路设计的重要工具。用于动力学参数推断的典型时移单胞数据的信息量不仅受到测量不确定度和固有随机性的限制，而且还受到所采用的摄动的限制。新型微流控装置能够合成时间化学浓度分布。扰动的信息量可以基于互信息来量化。我们提出了一种近似方法来进行此类微扰剖面的最优实验设计。为了估计互信息，我们对参数和观测值的联合分布进行多变量对数正态近似，并使用Metropolis-Hastings抽样扫描设计空间。该方法是通过为具有不同报告特征的基因表达模型的合成案例研究找到最优扰动序列来演示的。
Quantitatively predictive models of biomolecular circuits are important tools for the design of synthetic biology and molecular communication circuits. The information content of typical time-lapse single-cell data for the inference of kinetic parameters is not only limited by measurement uncertainty and intrinsic stochasticity but also by the employed perturbations. Novel microfluidic devices enable the synthesis of temporal chemical concentration profiles. The informativeness of a perturbation can be quantified based on mutual information. We propose an approximate method to perform optimal experimental design of such perturbation profiles. To estimate the mutual information we perform a multivariate log-normal approximation of the joint distribution over parameters and observations and scan the design space using Metropolis-Hastings sampling. The method is demonstrated by finding optimal perturbation sequences for synthetic case studies on a gene expression model with varying reporter characteristics.