Explorations of causal probabilistic programming approaches for rule-based models of biological signaling pathways


Rule-based models handle the complexity of biological signaling pathways. Biological signalling pathways are complex systems that underlie many cellular process and whose dysregulation is the source of many morbidities. To address the combinatorial complexity of interactions, patterns of transitions between model states can be compactly represented as probabilistic events using rule-based models.

We implemented a simple rule-based model using three different causal PPLs and compared their advantages and limitations. Kappa is designed for rule-based modeling of signaling pathways and was recently extended for counterfactual inference. Omega is a causal PPL implemented in Julia and is designed for general counterfactual inference. Probability trees are among the simplest models of causal generative processes and can compactly represent conditional independencies as a probabilistic program.

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