Modern high-throughput experiments generate datasets of molecular interactions at the systems level, for example protein interaction networks and expression arrays. Our work on networks has focused on the identification of structural elements that may point to functional units (such as clusters and motifs) and on global similarity analysis by alignment of networks. An important part is to develop stochastic models of networks and their evolution.
An alignment showing structural similarities between networks.
Significance analysis and statistical mechanics: an application to clustering
M. Luksza, M. Lässig, and J. Berg, Phys. Rev. Lett. 105, 220601 (4 pages), (2010)
Bayesian analysis of biological networks: Clusters, motifs, cross-species correlations
J. Berg and M. Lässig, in Statistical and evolutionary analysis of biological networks, ed. M.P.H. Stumpf and C. Wiuf, Imperial College Press, London (2009)
From protein interactions to functional annotation: graph alignment in Herpes
M. Kolar, Lässig, and J. Berg, BMC Syst Biol. 2, 90, (2008)
Cross-species analysis of biological networks by Bayesian alignment
J. Berg and M. Lässig, Proc. Natl. Acad. Sci. 103, 10967, (2006)
Structure and evolution of protein networks: a statistical model for link dynamics and gene duplications
J. Berg, M. Lässig, and A. Wagner, BMC Evol. Biol. 4, 51, (2004)
Local graph alignment and motif search in biological networks
J. Berg and M. Lässig, Proc. Natl. Acad. Sci. 101, 14689, (2004)
Correlated Random Networks
J. Berg and M. Lässig, Phys. Rev. Lett. 89, 228701, (2002)