Speaker
Description
Dynamical systems in biochemistry are complex, and one often does not have comprehensive knowledge about the interactions involved. Chemical reaction network (CRN) inference aims to identify, from observing time-series of species concentrations, the unknown reactions between the species. Most frequentist approaches to CRN inference focus on identifying a single, most likely CRN, without addressing uncertainty about the network structure. On the other hand, Bayesian treatments of CRN inference typically involve trans-dimensional and multimodal posterior distributions, which are computationally challenging to deal with. This poster illustrates how Bayesian CRN inference can be tackled with tempered spike-and-slab distributions, with applications to population models in ecology. Results are benchmarked against approaches that exhaustively consider all networks to evaluate how well our method explores the relevant networks.
