Teresa M. Przytycka, National Institutes of Health, USA

Network Biology Approach to Complex Diseases
Complex diseases are caused by a combination of genetic perturbations, and environmental factors among others. Such diseases tend to be very heterogeneous, in part because different disease cases might be caused by different genetic perturbations. In the recent years, systems biology approaches and more specifically network based approaches emerged as powerful tools for studying complex diseases. These approaches are often built on the knowledge of physical or functional interactions between molecules which are usually represented as an interaction network. An interaction network not only reports the binary relationships between individual nodes but also encodes hidden higher level organization of cellular communication.
We will discuss how these networks are used to leverage genotype, gene expression, and other types of data to identify dys-regulated pathways, infer the relationships between genotype and phenotype, and explain disease heterogeneity. We group the methods by common underlying principles and first discuss the high level description followed by more specific examples.

Literature:

Recent reviews:
Dong-Yeon Cho, Yoo-Ah Kim, Teresa M. Przytycka, Network Biology Approach to Complex Dieseses, to appear in the educational section of PloS Computatioanl Biology

Kim Y, Przytycka T (To appear) Bridging the Gap between Genotype and Phenotype via Network Approaches. Frontiers in Genetics Special issue on mapping complex disease traits with global gene expression.

Kim YA, Przytycki JH, Wuchty S, Przytycka TM (2011) Modeling information flow in biological networks. Phys Biol 8: 035012.

Primary literature
Ideker T, Ozier O, Schwikowski B, Siegel AF (2002) Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics 18 Suppl 1: S233-240.

Vandin F, Upfal E, Raphael BJ (2011) Algorithms for detecting significantly mutated pathways in cancer. J Comput Biol 18: 507-522

Mani KM, Lefebvre C, Wang K, Lim WK, Basso K, et al. (2008) A systems biology approach to prediction of oncogenes and molecular perturbation targets in B-cell lymphomas. Mol Syst Biol 4: 169.

Wang K NI, Banerjee N, Margolin AA, Califano A. Genome-wide Discovery of Modulators of Transcriptional Interactions in Human B Lymphocytes; 2006; Venice. pp. 348-362.

Chuang HY, Lee E, Liu YT, Lee D, Ideker T (2007) Network-based classification of breast cancer metastasis. Mol Syst Biol 3: 140.

Muller FJ, Laurent LC, Kostka D, Ulitsky I, Williams R, et al. (2008) Regulatory networks define phenotypic classes of human stem cell lines. Nature 455: 401-405.

Ulitsky I, Krishnamurthy A, Karp RM, Shamir R (2010) DEGAS: de novo discovery of dysregulated pathways in human diseases. PLoS One 5: e13367.

Chowdhury SA, Koyuturk M (2010) Identification of coordinately dysregulated subnetworks in complex phenotypes. Pac Symp Biocomput: 133-144.

Kim YA, Wuchty S, Przytycka TM (2011) Identifying causal genes and dysregulated pathways in complex diseases. PLoS Comput Biol 7: e1001095.

Kim YA, Salari R, Wuchty S, Przytycka TM (2013). Module Cover – a New Approach to Genotype-Phenotype Studies; Pacyfic Synposium on Biocomputing 18: 103-110.

Bailly-Bechet M, Borgs C, Braunstein A, Chayes J, Dagkessamanskaia A, et al. (2011) Finding undetected protein associations in cell signaling by belief propagation. Proc Natl Acad Sci U S A 108: 882-887.

Yeger-Lotem E, Riva L, Su LJ, Gitler AD, Cashikar AG, et al. (2009) Bridging high-throughput genetic and transcriptional data reveals cellular responses to alpha-synuclein toxicity. Nat Genet 41: 316-323.

Chowdhury SA, Nibbe RK, Chance MR, Koyuturk M (2011) Subnetwork state functions define dysregulated subnetworks in cancer. J Comput Biol 18: 263-281.