Maheshwari Parul, Assmann Sarah M, Albert Reka
Department of Physics, Penn State University, University Park, PA, United States.
Biology Department, Penn State University, University Park, PA, United States.
Front Genet. 2022 Jun 16;13:836856. doi: 10.3389/fgene.2022.836856. eCollection 2022.
Biological systems contain a large number of molecules that have diverse interactions. A fruitful path to understanding these systems is to represent them with interaction networks, and then describe flow processes in the network with a dynamic model. Boolean modeling, the simplest discrete dynamic modeling framework for biological networks, has proven its value in recapitulating experimental results and making predictions. A first step and major roadblock to the widespread use of Boolean networks in biology is the laborious network inference and construction process. Here we present a streamlined network inference method that combines the discovery of a parsimonious network structure and the identification of Boolean functions that determine the dynamics of the system. This inference method is based on a causal logic analysis method that associates a logic type (sufficient or necessary) to node-pair relationships (whether promoting or inhibitory). We use the causal logic framework to assimilate indirect information obtained from perturbation experiments and infer relationships that have not yet been documented experimentally. We apply this inference method to a well-studied process of hormone signaling in plants, the signaling underlying abscisic acid (ABA)-induced stomatal closure. Applying the causal logic inference method significantly reduces the manual work typically required for network and Boolean model construction. The inferred model agrees with the manually curated model. We also test this method by re-inferring a network representing epithelial to mesenchymal transition based on a subset of the information that was initially used to construct the model. We find that the inference method performs well for various likely scenarios of inference input information. We conclude that our method is an effective approach toward inference of biological networks and can become an efficient step in the iterative process between experiments and computations.
生物系统包含大量具有多样相互作用的分子。理解这些系统的一条富有成效的途径是用相互作用网络来表示它们，然后用动态模型描述网络中的流动过程。布尔建模是生物网络最简单的离散动态建模框架，已在概括实验结果和进行预测方面证明了其价值。布尔网络在生物学中广泛应用的第一步也是主要障碍是费力的网络推断和构建过程。在此，我们提出一种简化的网络推断方法，该方法结合了简约网络结构的发现以及确定系统动态的布尔函数的识别。这种推断方法基于一种因果逻辑分析方法，该方法将一种逻辑类型（充分或必要）与节点对关系（促进或抑制）相关联。我们使用因果逻辑框架来吸收从扰动实验中获得的间接信息，并推断尚未经过实验记录的关系。我们将这种推断方法应用于植物中一个经过充分研究的激素信号传导过程，即脱落酸（ABA）诱导气孔关闭的信号传导。应用因果逻辑推断方法显著减少了网络和布尔模型构建通常所需的人工工作。推断出的模型与人工整理的模型一致。我们还通过基于最初用于构建模型的部分信息重新推断一个代表上皮-间质转化的网络来测试该方法。我们发现该推断方法在各种可能的推断输入信息场景下都表现良好。我们得出结论，我们的方法是推断生物网络的有效方法，并且可以成为实验与计算之间迭代过程中的一个有效步骤。
