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对于动态细胞过程和假说生成的研究至关重要，这些信息为实验和验证提供了信息。不幸的是，模型参数通常不可用，并且稀疏的实验数据导致模型校准和参数估计方面的挑战。反过来，这可能导致对实验数据进行不可靠的机械解释，并产生用于实验验证的构思欠佳的假设。为了应对这一挑战，我们评估了贝叶斯启发式基于概率的方法，依赖于从有关反应网络拓扑和参数的可用信息中计算出的感兴趣数量的期望值的“期望值”，可以用于在有限的可用数据的背景下定性地探索假设的生化网络执行机制。我们在外部细胞凋亡执行模型上测试了我们的方法，以识别各种条件下的首选信号执行模式。凋亡信号处理可通过线粒体非依赖性（I型）模式或线粒体非依赖性（II型）模式进行。我们首先证明 凋亡信号处理可通过线粒体非依赖性（I型）模式或线粒体非依赖性（II型）模式进行。我们首先证明 凋亡信号处理可通过线粒体非依赖性（I型）模式或线粒体非依赖性（II型）模式进行。我们首先证明在计算机上以模型子网为代表的基因敲除成功地确定了特定浓度的关键分子调节剂最可能的执行模式。然后，我们表明分子调节剂浓度的变化通过在直接胱天蛋白酶和线粒体途径之间转移信号流的主要途径，改变了通过网络的总体反应通量。因此，我们的工作表明，即使数据丢失或稀疏，概率方法也可用于探索模型生化系统的定性动态行为。