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

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

The inference of genetic interactions from measured expression data is one of the most
challenging tasks of modern functional genomics. When successful, the learned network of …

Systems biology is a multi-disciplinary approach to the study of the interactions of various
cellular mechanisms and cellular components. Owing to the development of new …

Probabilistic Boolean network (PBN) modelling is a semi-quantitative approach widely used
for the study of the topology and dynamic aspects of biological systems. The combined use …

Motivation: The immune response to bacterial infection represents a complex network of
dynamic gene and protein interactions. We present an optimized reverse engineering …

Motivation: We propose a reverse engineering scheme to discover genetic regulation from
genome-wide transcription data that monitors the dynamic transcriptional response after a …

Genes interact with each other in complex networks that enable the processing of
information and the metabolism of nutrients inside the cell. A novel inference algorithm …

A major problem associated with the reverse engineering of genetic networks from micro-
array data is how to reliably find genetic interactions when faced with a relatively small …

Learning regulatory interactions between genes from microarray measurements presents
one of the major challenges in functional genomics. This article studies the suitability of …