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Toward a complete in silico, multi‐layered embryonic stem cell regulatory network

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Abstract Recent efforts in systematically profiling embryonic stem (ES) cells have yielded a wealth of high‐throughput data. Complementarily, emerging databases and computational tools facilitate ES cell studies and further pave the way toward the in silico reconstruction of regulatory networks encompassing multiple molecular layers. Here, we briefly survey databases, algorithms, and software tools used to organize and analyze high‐throughput experimental data collected to study mammalian cellular systems with a focus on ES cells. The vision of using heterogeneous data to reconstruct a complete multi‐layered ES cell regulatory network is discussed. This review also provides an accompanying manually extracted dataset of different types of regulatory interactions from low‐throughput experimental ES cell studies available at http://amp.pharm.mssm.edu/iscmid/literature. Copyright © 2010 John Wiley & Sons, Inc. This article is categorized under: Analytical and Computational Methods > Computational Methods

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Initial literature‐based regulatory network for mES cells self‐renewal and pluripotency. The network contains 121 nodes representing genes or gene‐encoding proteins with 187 edges involved in mouse ES cell self‐renewal. Edge type: directed (positive/negative effects); undirected (neutral effect, i.e., protein–protein interactions). Edge color: dark blue (transcriptional positive); green (transcriptional negative); black (signaling positive); gray (signaling negative). Edge style: solid (direct); dashed (indirect). Node color: yellow (extracellular ligand); gray (membrane protein); cyan (cytosolic protein); red (nuclear proteins/genes); green (sarcoplasmic reticulum). A web‐based system to navigate through components and their interactions is provided at http://amp.pharm.mssm.edu/iscmid/literature.

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Pipeline process for systematic studies of ES cells starting with experimental methods to characterize the state of the cell at different regulatory layers. Then, data from such experiments are stored in public repositories for data consolidation and reuse. Such databases are analyzed by algorithms implemented in software tools that can make predictions and build networks from the data. Different types of networks from the different regulatory layers can be combined, whereas lists of genes/proteins can be probed for functional enrichment using integrated computational platforms.

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Nodes from the literature‐based network shown in Figure 2 are color coded based on changes in expression after Nanog down‐regulation using shRNA in mES cells. Green represents down‐regulation, red up‐regulation. Data for gray nodes was not obtained.

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