This article is part of the supplement: Otto Warburg International Summer School and Workshop on Networks and Regulation .

Review

Inferring cellular networks – a review

Florian Markowetz^1,2 and Rainer Spang^1,3

¹  Max Planck Institute for Molecular Genetics, Ihnestrasse 63-73, 14195 Berlin, Germany

²  Princeton University, Lewis-Sigler Institute for Integrative Genomics and Dept. of Computer Science, Princeton, NJ 08544, USA

³  Present affiliation: University Regensburg, Institute of Functional Genomics, Josef-Engert-Str. 9, 93053 Regensburg, Germany

author email corresponding author email

BMC Bioinformatics 2007, 8(Suppl 6):S5doi:10.1186/1471-2105-8-S6-S5

Published:	27 September 2007

Abstract

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 developing, we show that most currently used methods can be organized by a few key concepts. The first part of the review deals with conditional independence models including Gaussian graphical models and Bayesian networks. The second part discusses probabilistic and graph-based methods for data from experimental interventions and perturbations.