Factored expectation propagation for input-output FHMM models in systems biology

TL;DR

This paper introduces a novel structured variational inference method using factored expectation propagation for input-output factorial hidden Markov models, enabling effective modeling of metabolic and gene expression data in systems biology.

Contribution

It presents a new inference approach combining expectation propagation with variational methods for input-output FHMMs, improving modeling of biological systems.

Findings

Validated through extensive simulations

Demonstrated applicability on bacterial data

Improved inference accuracy in biological models

Abstract

We consider the problem of joint modelling of metabolic signals and gene expression in systems biology applications. We propose an approach based on input-output factorial hidden Markov models and propose a structured variational inference approach to infer the structure and states of the model. We start from the classical free form structured variational mean field approach and use a expectation propagation to approximate the expectations needed in the variational loop. We show that this corresponds to a factored expectation constrained approximate inference. We validate our model through extensive simulations and demonstrate its applicability on a real world bacterial data set.