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. Mar-Apr 2012;19(2):171-5.
doi: 10.1136/amiajnl-2011-000490. Epub 2011 Nov 10.

Using Systems and Structure Biology Tools to Dissect Cellular Phenotypes

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Free PMC article

Using Systems and Structure Biology Tools to Dissect Cellular Phenotypes

Aris Floratos et al. J Am Med Inform Assoc. .
Free PMC article

Abstract

The Center for the Multiscale Analysis of Genetic Networks (MAGNet, http://magnet.c2b2.columbia.edu) was established in 2005, with the mission of providing the biomedical research community with Structural and Systems Biology algorithms and software tools for the dissection of molecular interactions and for the interaction-based elucidation of cellular phenotypes. Over the last 7 years, MAGNet investigators have developed many novel analysis methodologies, which have led to important biological discoveries, including understanding the role of the DNA shape in protein-DNA binding specificity and the discovery of genes causally related to the presentation of malignant phenotypes, including lymphoma, glioma, and melanoma. Software tools implementing these methodologies have been broadly adopted by the research community and are made freely available through geWorkbench, the Center's integrated analysis platform. Additionally, MAGNet has been instrumental in organizing and developing key conferences and meetings focused on the emerging field of systems biology and regulatory genomics, with special focus on cancer-related research.

Conflict of interest statement

Competing interests: None.

Figures

Figure 1
Figure 1
Visualization of the signalome-transfactome network by integrating Modulator Inference by Network Dynamics (MINDy) predictions with the Protein–Protein Interface Database (PPIDB) and Kinase–Substrate Interactions (KSIDB) Database. Two types of interactions are represented in the network: (1) MINDy predicted signaling protein (SP)–transcription factor (TF) interactions supported by PPIDB or KSIDB (ie, modulatory interactions predicted by MINDy that have physical interaction evidence); (2) MINDy predicted SP–SP interactions supported by PPIDB or KSIDB (ie, between modulators predicted by MINDy of the same TF, and are supported by physical interaction evidence). Depending on the source of evidence, these interactions can be either undirected (supported by known PPIs) or directed (supported by NetworKIN, ie, kinase -> substrate).
Figure 2
Figure 2
First three panels: Hox/Exd dimers in the presence of the fkh250, an Scr/Exd specific binding site, which has a narrow minor groove (small arrows) and negative electrostatic potential (pink dashes) in the center of the binding site. Fourth panel: fkh250con, a non-specific Hox/Exd binding site, which does not have these characteristics. Scr/Exd, but not other Hox/Exd dimers, can effectively bind to the fkh250 binding site because Exd positions a normally unstructured peptide so that the basic side chains (short blue lines) can insert into the negative pocket formed by the narrow minor groove. In contrast, because fkh250con does not have this negative pocket, it is less selective and can bind multiple Hox/Exd dimers.
Figure 3
Figure 3
‘Driver mutation’, a genetic alteration that provides the tumor cell with a growth advantage during carcinogenesis or tumor progression. We reasoned that driver mutations might leave a genomic ‘footprint’ that can assist in distinguishing between driver and passenger mutations based on the following assumptions: (A) a driver mutation should occur in multiple tumors more often than would be expected by chance; (B) a driver mutation may be associated (correlated) with the expression of a group of genes that form a ‘module’; (C) copy number aberrations often influence the expression of genes in the module via changes in expression of the driver; (D) gene expression is particularly useful for identifying candidate drivers within large amplified or deleted regions of a chromosome, whereas genes located in a region of genomic copy gain/loss are indistinguishable in copy-number aberrations, expression permits the ranking of genes based on how well they correspond with the phenotype. Reprinted with permission from Akavia et al.

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