Modeling cumulative biological phenomena with Suppes-Bayes Causal Networks

TL;DR

This paper introduces Suppes-Bayes Causal Networks, a probabilistic graphical model framework for understanding the accumulation of DNA changes in diseases like cancer and HIV, combining Bayesian inference with Suppes' causation theory.

Contribution

It presents the theoretical foundations of SBCNs and explores how poset structures and regularization strategies affect model inference, with an application to HIV data.

Findings

Model selection is influenced by the choice of poset and regularization.

SBCNs effectively capture the order of DNA change accumulation.

Application to HIV data yields valuable biological insights.

Abstract

Several diseases related to cell proliferation are characterized by the accumulation of somatic DNA changes, with respect to wildtype conditions. Cancer and HIV are two common examples of such diseases, where the mutational load in the cancerous/viral population increases over time. In these cases, selective pressures are often observed along with competition, cooperation and parasitism among distinct cellular clones. Recently, we presented a mathematical framework to model these phenomena, based on a combination of Bayesian inference and Suppes' theory of probabilistic causation, depicted in graphical structures dubbed Suppes-Bayes Causal Networks (SBCNs). SBCNs are generative probabilistic graphical models that recapitulate the potential ordering of accumulation of such DNA changes during the progression of the disease. Such models can be inferred from data by exploiting likelihood-based model-selection strategies with regularization. In this paper we discuss the theoretical foundations of our approach and we investigate in depth the influence on the model-selection task of: (i) the poset based on Suppes' theory and (ii) different regularization strategies. Furthermore, we provide an example of application of our framework to HIV genetic data highlighting the valuable insights provided by the inferred.