Causal Queries from Observational Data in Biological Systems via Bayesian Networks: An Empirical Study in Small Networks

TL;DR

This paper explores the use of Bayesian networks to infer causal relationships in biological systems from observational data, demonstrating their effectiveness on small simulated and real gene expression networks.

Contribution

It introduces a causal formalism tailored for biological networks and empirically evaluates its ability to uncover causal information flow from observational data.

Findings

Bayesian networks can effectively infer causal interactions in small biological networks.

The approach successfully distinguishes causal relationships in simulated and real gene expression data.

Causal formalism enhances understanding of complex biological systems.

Abstract

Biological networks are a very convenient modelling and visualisation tool to discover knowledge from modern high-throughput genomics and postgenomics data sets. Indeed, biological entities are not isolated, but are components of complex multi-level systems. We go one step further and advocate for the consideration of causal representations of the interactions in living systems.We present the causal formalism and bring it out in the context of biological networks, when the data is observational. We also discuss its ability to decipher the causal information flow as observed in gene expression. We also illustrate our exploration by experiments on small simulated networks as well as on a real biological data set.