Integrated Inference and Analysis of Regulatory Networks from Multi-Level Measurements [chapter]

Christopher S. Poultney, Alex Greenfield, Richard Bonneau
2012 Methods in Cell Biology  
Regulatory and signaling networks coordinate the enormously complex interactions and processes that control cellular processes (such as metabolism and cell division), coordinate response to the environment, and carry out multiple cell decisions (such as development and quorum sensing). Regulatory network inference is the process of inferring these networks, traditionally from microarray data but increasingly incorporating other measurement types such as proteomics, ChIPseq, metabolomics, and
more » ... s cytometry. We discuss existing techniques for network inference. We review in detail our pipeline, which consists of an initial biclustering step, designed to estimate coregulated groups; a network inference step, designed to select and parameterize likely regulatory models for the control of the co-regulated groups from the biclustering step; and a visualization and analysis step, designed to find and communicate key features of the network. Learning biological networks from even the most complete data sets is challenging; we argue that integrating new data types into the inference pipeline produces networks of increased accuracy, validity, and biological relevance.
doi:10.1016/b978-0-12-388403-9.00002-3 pmid:22482944 pmcid:PMC5615108 fatcat:p2bycgc4a5e7xixoqwy2nudzd4