Optimal Finite-Horizon Perturbation Policy for Inference of Gene Regulatory Networks
Optimal Finite-Horizon Perturbation Policy for Inference of Gene Regulatory Networks
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DOI：
10.1109/mis.2020.3017155
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发表时间：
2021-01-01
影响因子：
6.4
通讯作者：
Ghoreishi, Seyede Fatemeh
中科院分区：
文献类型：
--
作者：
Imani, Mahdi;Ghoreishi, Seyede Fatemeh
系统生物学的一个主要目标是精确地模拟基因调控网络（GRNs）的复杂动力学行为。尽管在GRNs的推理方面已经取得了一些进展，但两个主要问题仍然使问题具有挑战性：1）参数的不可识别性和2）有限的数据量。因此，有必要在实验上扰动或激发系统进入不同的状态。这种扰动过程在每个时间点将基因的表达从活性破坏为非活性，反之亦然。另一个问题是基因状态的部分可观测性，这必须从有噪声的基因表达测量中间接推断。在这篇文章中，后一个问题是占部分观察布尔动态系统信号模型的数据和应用最优状态估计。然后，最优的有限时域扰动策略，以实现最大后验估计在小的扰动成本下的最高可能的期望性能。通过使用众所周知的p53-MDM 2负反馈环调节模型和合成GRNs的数值实验来评估性能。
A major goal of systems biology is to model accurately the complex dynamical behavior of gene regulatory networks (GRNs). Despite several advancements that have been made in inference of GRNs, two main issues continue to make the problem challenging: 1) nonidentifiability of parameters and 2) limited amounts of data. Thus, it becomes necessary to experimentally perturb or excite the system into different states. This perturbation process disrupts the expression of genes from active to inactive, or vice versa, at each time point. Another issue is the partial observability of the gene states, which must be inferred indirectly from noisy gene expression measurements. In this article, this latter issue is accounted for by employing the partially observed Boolean dynamical system signal model for the data and applying optimal state estimation. Then, the optimal finite-horizon perturbation policy is derived to achieve the highest possible expected performance for the maximum a posteriori estimator under a small perturbation cost. Performance is assessed through numerical experiments using the well-known p53-MDM2 negative-feedback loop regulatory model and synthetic GRNs.