In order to understand the functioning of organisms on the molecular level, we need to know
which genes are expressed, when and where in the organism, and to which extent. The …

Systems biology aims to develop mathematical models of biological systems by integrating
experimental and theoretical techniques. During the last decade, many systems biological …

Motivation: Our goal is to construct a model for genetic regulatory networks such that the
model class:(i) incorporates rule-based dependencies between genes;(ii) allows the …

A guide to machine learning approaches and their application to the analysis of biological
data. An unprecedented wealth of data is being generated by genome sequencing projects …

We present a method (the Inferelator) for deriving genome-wide transcriptional regulatory
interactions, and apply the method to predict a large portion of the regulatory network of the …

In this paper, we propose a new approach to investigate the controllability and reachability
of probabilistic Boolean control networks (PBCNs) with forbidden states. We first give a …

Microarray technologies, which enable the simultaneous measurement of all RNA
transcripts in a cell, have spawned the development of algorithms for reverse-engineering …

The enormous complexity of biological systems at the molecular level must be answered
with powerful computational methods. Computational biology is a young field, but has seen …

We propose a dynamic Bayesian network and nonparametric regression model for
constructing a gene network from time series microarray gene expression data. The …

Motivation: We have used state-space models to reverse engineer transcriptional networks
from highly replicated gene expression profiling time series data obtained from a well …