Abstract
Dynamic Bayesian networks (DBNs) have been applied widely to reconstruct
the structure of regulatory processes from time series data,
and they have established themselves as
a standard modelling tool in computational
systems biology. The conventional approach is based on the
assumption of a homogeneous Markov chain, and many recent
research efforts have focused on relaxing this restriction.
An approach that enjoys particular popularity is based on a
combination of a DBN with a multiple changepoint process,
and the application of
a Bayesian inference scheme via reversible jump Markov chain
Monte Carlo (RJMCMC). In the present paper, we expand this
approach in two ways. First, we show that a dynamic programming
scheme allows the changepoints to be sampled from the correct
conditional distribution, which results in improved convergence
over RJMCMC. Second, we introduce a novel
Bayesian clustering and information sharing
scheme among nodes, which provides a mechanism for automatic
model complexity tuning.
We evaluate the dynamic programming scheme on
expression time series for {\em Arabidopsis thaliana} genes
involved in circadian regulation. In a simulation study
we demonstrate that the regularization scheme improves the
network reconstruction accuracy over that obtained with recently
proposed inhomogeneous DBNs.
For gene expression profiles from a synthetically designed
Saccharomyces cerevisiae strain under switching
carbon metabolism we show that the combination of both: dynamic
programming and regularization yields an inference procedure that
outperforms two alternative established network reconstruction methods
from the biology literature.
Year
2011
Category
Refereed journal