cplaisie

Christopher Plaisier

Asst Professor
Sch Biological & Hlth Sys Engr
Associate Faculty
Biodesign BSS

Location: TEMPE

Contact Information
plaisier@asu.edu
(480) 965-6832

Education

  • Postdoctoral fellow, Institute for Systems Biology. Advisor:  Nitin Baliga (August 2009 ? August 2012)
  • Ph.D. Human Genetics, University of California-Los Angeles 2009. Dissertation Title: Genetical Genomics Approaches to Familial Combined Hyperlipidemia. Advisor: Paivi Pajukanta.
  • M.S. Bioinformatics, University of California-Los Angeles 2009. Thesis: Transcription Factor Binding in a Familial Combined Hyperlipidemia Weighted Gene Co-expression Network. Advisor: Steve Horvath. 
  • B.S. Biology, University of Utah 2000

Bio

Christopher Plaisier?s laboratory focuses on constructing gene regulatory networks from patient data that can be used to discover diagnostic and prognostic biomarkers as well as novel drug targets. To accomplish this they integrate genetic, transcriptional, functional and clinical data together into one comprehensive gene regulatory network. They then design and conduct experiments which validate the predictions from these gene regulatory networks using in vitro cell culture models.

In 2012, Plaisier described a cancer miRNA regulatory network (http://cmrn.systemsbiology.net) which required the development of a novel tool the miRvestigator (http://mirvestigator.systemsbiology.net) and provided a comprehensive picture of miRNA mediated regulation for 46 cancer sub-types. More recently, Plaisier described the development and application of the Systems Genetics Network AnaLysis (SYGNAL) pipeline to the deadly brain cancer glioblastoma multiforme (http://glioma.systemsbiology.net). In the process of building the SYGNAL pipeline, Plaisier also compiled the Transcription Factor (TF) Target Gene database, which houses a very powerful TF to target interactions inferred from human genome sequence, a compendium of TF DNA binding motifs, and ENCODE digital genomic footprints. The SYGNAL pipeline is powerful in that it extends gene regulatory network inferrence by discovering both TF and miRNA regulatory influences by integrating across genetic, mRNA and miRNA expression, as well as clinical data from The Cancer Genome Atlas (TCGA). He demonstrated that the resulting gene regultory network can be used to discover novel biological findings and synergistic drug combinations for glioblastoma multiforme. Most importantly the SYGNAL pipeline is generalizable to any mamalian system (currently human and mouse) with a minimum data requirement of transcriptional profiles across relevant conditions.