Information about protein function and cellular pathways is central to the system-level understanding of living organisms. The current proposal discusses the development strategy of a set of algorithms aimed at the analysis of cellular pathways, gene regulation, and protein interaction maps. The proposed algorithms analyze different aspects of molecular networks and will be applied to a comprehensive database of human pathways that has been specifically compiled for these purposes. This study will serve two goals: 1) to develop, improve and validate the algorithms, and 2) to identify general principles and key regulators of cell signaling. The novelty of the proposed research stems from the combination of the state of the art statistical approaches and a unique database of cell signaling networks compiled for these purposes. Different topological features of biological networks will be studied: correlation profiles - to better understand the underlying complex system and to find general principles that distinguish the system in different states (e.g., normal vs. diseased cell, different organisms), and network motifs - to identify key regulators in cell signaling. Network information will be combined with gene expression and sequence data to build models aimed at the prediction of new regulatory links. This will include the application of knowledge networks, Bayesian nets, and other statistical tools. Information about existing pathways from the database will be used as a seed for such modeling algorithms and is instrumental to producing high quality predictions. The present project is proposed as a collaboration between a small business and the national laboratory. It is one of the project goals to foster and build on this collaboration to establish coherent information and idea exchange between molecular biologists and theoretical physicists. The longer-term plans include commercialization of developed tools and giving academic researchers access to validated databases. Finally, it is expected that developed approaches will have a substantial impact on in silico biology by accelerating validation of pharmaceutical drug targets.
| Ispolatov, Iaroslav; Maslov, Sergei (2008) Detection of the dominant direction of information flow and feedback links in densely interconnected regulatory networks. BMC Bioinformatics 9:424 |
| Maslov, Sergei; Sneppen, Kim; Ispolatov, I (2007) Spreading out of perturbations in reversible reaction networks. New J Phys 9:273 |
| Sivachenko, Andrey Y; Yuryev, Anton; Daraselia, Nikolai et al. (2007) Molecular networks in microarray analysis. J Bioinform Comput Biol 5:429-56 |
| Maslov, Sergei; Ispolatov, I (2007) Propagation of large concentration changes in reversible protein-binding networks. Proc Natl Acad Sci U S A 104:13655-60 |
| Daraselia, Nikolai; Yuryev, Anton; Egorov, Sergei et al. (2007) Automatic extraction of gene ontology annotation and its correlation with clusters in protein networks. BMC Bioinformatics 8:243 |
| Ispolatov, I; Mazo, I; Yuryev, A (2006) Finding mesoscopic communities in sparse networks. J Stat Mech 9:p09014 |
| Ispolatov, I; Krapivsky, P L; Yuryev, A (2005) Duplication-divergence model of protein interaction network. Phys Rev E Stat Nonlin Soft Matter Phys 71:061911 |
| Ispolatov, I; Krapivsky, Pl; Mazo, I et al. (2005) Cliques and duplication-divergence network growth. New J Phys 7:145 |
| Ispolatov, Iaroslav; Yuryev, Anton; Mazo, Ilya et al. (2005) Binding properties and evolution of homodimers in protein-protein interaction networks. Nucleic Acids Res 33:3629-35 |
