Main method
Principal components analysis
singular value decomposition
multidimensional scaling
Hub node
H. Jeong et al, Lethality and centrality in protein networks. Nature.
Albert R et al, Error and attack tolerance of complex networks, Nature.
Erdos-Renyi random networks vs. Barabasi-Albert scale-free networks
Erdos's network -> passion distribution, Bar's network -> power low distribution.
Barabasi et al found that many real networks including the internet and the WWW are scale-free. this means that the connectivity distribution of nodes fits a power-law.
Barabasi, Physics World, July 2001
Jeong et al. Nature 407,651 (2000)
Jeong et al. Nature 411, 41 (2001)
Types of Biological Networks
duplication-divergence network growth model
Alexei Vazquez, modeling of protein interaction networks, science.
motifs are small circuits that are statistically more prevalent in real networks vs. motifs found in randomized networks
Milo et al. Science, 298, 824 (2002)
Evolutionary conservation of motif constituents in the yeast protein interaction network.
S Wuchty et al. Nature Genetics, 35, 176-179 (2003)
Ordered cyclic motifs contribute to dynamic stability in biological and engineered networks.
Ma' ayan et al. PNAS105:19235 (2010)
Magnetization Model
Ben D. MaeArt, micro dynamics and criticality of adaptive regulatory networks, Magnetization model
General topological properties of bimolecular networks
A- power-law connectivity distribution, B- party hubs and data hubs, C- multi-site and single-site hubs .....(eight types)
Ma'ayan A.J Biological Chem. 2009 284(9):5451
Nature 430, 88-93 (2004)
Kim et al. Science 314,1938 (2006)
Negative feedback loops appear to be abundant at early steps while positive feedback loops are overall more common than expected.
Ma' Alan et al. Science 309, 1078 (2005)
Cell signaling pathways
Signaling pathways are not isolated and can be merged into large networks.
Ma'ayan et al. Science 310, 1078 (2005)
Ma'ayan A et al. Sci Signal. 2:cm1 (2009)
Kinase-substrate network
focus on certain network
Tan et al. Sci Signal. 2009 Jul 28i; 2 (81): ra 39
Pseudo-nodes are used as place holders to fill-in unknown links and components.
Li et al. Plos Bio. 4: e312 (2006)
MacArthus et al., PloS ONE 3: e3086 (2008)
Nodes can be genes, transcription factors or signaling components
Albert R, J Their Biological. 2003 223 (1): 1-18.
Network Construction from Literature
1. Manual (i.e. Ingenuity, KEGG, STKE)
2.Semi-automated (i.e. preBIND)
3. Natural Language Processing (NLP) (i.e. PathwayStudio)
Epistasis Networks: Inferring Networks by Double Deletion mutants
Hin Yan Tong, Science 294:2364 (2001)
Inferring Networks from Time Series microarrays
Zou M, Bioinformatics. 2005 21(1): 71-9
Perturbations and bayesian networks
Sachs et al. Science. 2005 308:523-9
Drug-Target Networks
Drugs can be connected to their known protein targets
Ma'ayan et al. Mt Sinai j Med (2007) 74:27
Yildirim et al. Nat Biotechnol. (2007) 25: 1110
Disease Gene Networks
Each node corresponds to a distinct disorder.
Goh et al. Proc Natl Acad Sci USA. (2007) 104: 8685-90
Bipartite Networks for Data Integration
Tanay et al. PNAS (2004) 101: 2981
Network visualization-Genes2Networks, Text2Graph, yEd and Cytoscape.
idea:
Use known disease genes to build a network around known disease genes.
Identify new disease genes to build a network around known disease genes.
Whether new genes was found to be mutated in a subgroup of patients. (>5%)
Cordeddu V et al. Nature Genetics 41, 1022 (2009)
