Table_4_A Probabilistic Approach to Explore Signal Execution Mechanisms With Limited Experimental Data.XLSX

Mathematical models of biochemical reaction networks are central to the study of dynamic cellular processes and hypothesis generation that informs experimentation and validation. Unfortunately, model parameters are often not available and sparse experimental data leads to challenges in model calibration and parameter estimation. This can in turn lead to unreliable mechanistic interpretations of experimental data and the generation of poorly conceived hypotheses for experimental validation. To address this challenge, we evaluate whether a Bayesian-inspired probability-based approach, that relies on expected values for quantities of interest calculated from available information regarding the reaction network topology and parameters can be used to qualitatively explore hypothetical biochemical network execution mechanisms in the context of limited available data. We test our approach on a model of extrinsic apoptosis execution to identify preferred signal execution modes across varying conditions. Apoptosis signal processing can take place either through a mitochondria independent (Type I) mode or a mitochondria dependent (Type II) mode. We first show that in silico knockouts, represented by model subnetworks, successfully identify the most likely execution mode for specific concentrations of key molecular regulators. We then show that changes in molecular regulator concentrations alter the overall reaction flux through the network by shifting the primary route of signal flow between the direct caspase and mitochondrial pathways. Our work thus demonstrates that probabilistic approaches can be used to explore the qualitative dynamic behavior of model biochemical systems even with missing or sparse data.

生化反应网络的数学模型是动态细胞过程研究，以及为实验与验证提供支撑的假说生成工作的核心所在。但遗憾的是，模型参数往往难以获取，且实验数据匮乏会给模型校准与参数估计带来诸多挑战。这进而会导致对实验数据的机理解释缺乏可靠性，同时还会催生用于实验验证的不够严谨的假说。为解决这一难题，我们评估了一种基于贝叶斯思想的概率方法：该方法依托从反应网络拓扑结构与参数的现有信息中计算得到的目标量的期望，可在可用数据有限的场景下，定性探究生化网络的假想执行机制。我们依托外源性细胞凋亡（extrinsic apoptosis）执行模型对所提方法进行测试，以识别不同条件下的优势信号执行模式。细胞凋亡的信号传导可通过两种模式进行：不依赖线粒体的I型模式，或依赖线粒体的II型模式。我们首先证明，以模型子网络表征的计算机模拟敲除（in silico knockouts），可成功识别关键分子调控因子在特定浓度下的最可能执行模式。随后我们发现，分子调控因子浓度的改变，会通过改变信号流在直接半胱天冬酶（caspase）通路与线粒体通路之间的主要路径，进而改变整个网络的反应通量。综上，本研究证明，即便数据缺失或匮乏，基于概率的方法仍可用于探究生化系统模型的定性动态行为。