Two related developments are currently changing traditional approaches to computational
systems biology modelling. First, stochastic models are being used increasingly in …

In this review we give an overview of computational and statistical methods to reconstruct
cellular networks. Although this area of research is vast and fast develo**, we show that …

In this paper, Hamilton's (1988, 1989) Markov-switching model is extended to a general
state-space model. This paper also complements Shumway and Stoffer's (1991) dynamic …

While single-cell gene expression experiments present new challenges for data processing,
the cell-to-cell variability observed also reveals statistical relationships that can be used by …

Leaf senescence is an essential developmental process that impacts dramatically on crop
yields and involves altered regulation of thousands of genes and many metabolic and …

Real world entities work in concert as a system and not in isolation. Understanding the
associations between these entities from their digital signatures can provide novel system …

The elucidation of gene regulatory networks is one of the major challenges of systems
biology. Measurements about genes that are exploited by network inference methods are …

Motivation: Multi-series time-course microarray experiments are useful approaches for
exploring biological processes. In this type of experiments, the researcher is frequently …

Bayesian networks (BNs) provide a neat and compact representation for expressing joint
probability distributions (JPDs) and for inference. They are becoming increasingly important …

Motivation: Microarray gene expression data has increasingly become the common data
source that can provide insights into biological processes at a system-wide level. One of the …