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

Christopher S. Poultney, Alex Greenfield, Richard Bonneau

Research output: Contribution to journalArticlepeer-review


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, ChIP-seq, metabolomics, and mass cytometry. We discuss existing techniques for network inference. We review in detail our pipeline, which consists of an initial biclustering step, designed to estimate co-regulated 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.

Original languageEnglish (US)
Pages (from-to)19-56
Number of pages38
JournalMethods in Cell Biology
StatePublished - 2012


  • Biclustering
  • Network
  • Proteomics
  • Signaling
  • Temporal
  • Visualization

ASJC Scopus subject areas

  • Cell Biology


Dive into the research topics of 'Integrated Inference and Analysis of Regulatory Networks from Multi-Level Measurements'. Together they form a unique fingerprint.

Cite this