On the relationship between ODEs and DBNs

TL;DR

This paper explores the connection between differential equations and dynamic Bayesian networks, showing that for unequally spaced data, modeling Euler approximations can enhance gene network inference.

Contribution

It clarifies the relationship between DEs and DBNs and provides empirical evidence that Euler approximation modeling improves network reconstruction with uneven data.

Findings

Euler approximations and DBNs are equivalent for equally spaced data.

Modeling Euler approximations improves network inference for unequally spaced data.

Empirical results support the benefit of Euler-based modeling in certain data conditions.

Abstract

Recently, Li et al. (Bioinformatics 27(19), 2686-91, 2011) proposed a method, called Differential Equation-based Local Dynamic Bayesian Network (DELDBN), for reverse engineering gene regulatory networks from time-course data. We commend the authors for an interesting paper that draws attention to the close relationship between dynamic Bayesian networks (DBNs) and differential equations (DEs). Their central claim is that modifying a DBN to model Euler approximations to the gradient rather than expression levels themselves is beneficial for network inference. The empirical evidence provided is based on time-course data with equally-spaced observations. However, as we discuss below, in the particular case of equally-spaced observations, Euler approximations and conventional DBNs lead to equivalent statistical models that, absent artefacts due to the estimation procedure, yield networks with identical inter-gene edge sets. Here, we discuss further the relationship between DEs and conventional DBNs and present new empirical results on unequally spaced data which demonstrate that modelling Euler approximations in a DBN can lead to improved network reconstruction.