Speaker
Description
Systems of intracellular biochemical reactions are highly complex, usually involving parts that cannot be directly measured. Representing these systems as networks, with nodes for biochemical species and edges, their reactions help to quantitatively characterize their function and the effects of dysregulation. Causal discovery methods can uncover interactions within these networks from observational data, detecting hidden effects from partial observations.
We benchmark state-of-the-art temporal causal discovery methods on time series data from simulations of biochemical kinetics models. Our results demonstrate good performance on toy models for this task, particularly when data is sampled in a way that is consistent with the timescales of the system. By omitting data, we consider the problem of reconstructing these networks in the presence of latent confounders and unobserved species participating in reactions. Causal discovery indicates time-uncorrelated confounders with bidirected edges and unobserved species through time-delayed edges, locating hidden effects, and estimating their typical timescales. Finally, we extend these benchmarks to the reconstruction of a model of the epidermal growth factor receptor signalling network, a well-studied system frequently dysregulated in cancer.
Altogether, our work showcases the feasibility and usefulness of causal discovery methods as part of the data-driven mathematical modelling pipeline for systems of biochemical reactions.
