Dr. Plaisier’s laboratory is focused 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 Dr. 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, Dr. 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 Dr. Plaisier also compiled the Transcription Factor (TF) Target Gene database (http://tfbsdb.systemsbiology.net) 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.
Postdoctoral Fellow, Institute for Systems Biology, August 2009 – August 2012. Advisor: Nitin Baliga.
Doctor of Philosophy in Human Genetics, University of California, Los Angeles, June 2009. Advisor: Paivi Pajukanta, Dissertation Title: Genetical Genomics Approaches to Familial Combined Hyperlipidemia.
Masters of Science in Bioinformatics, University of California, Los Angeles, June 2009. Advisor: Steve Horvath, Thesis Title: Transcription Factor Binding in a Familial Combined Hyperlipidemia Weighted Gene Co-expression Network.
Bachelor of Science in Biology, University of Utah, May 2000.
STEM Fellow, Industry Expert
Washington Alliance for Better Schools
Sep 2013 – Jun 2014
Advised 10th grade teachers on developing new curriculum around antibiotic resistance that met Next Generation Science Standards (NGSS) for STEM (science, technology, engineering and math) learning in Washington classrooms.
Science Communication Fellowship
Pacific Science Center
Aug 2015 – August 2017
Science and Technology
Learned how to communicate my science to the broader public by developing my communication skills and an interactive activity and presenting it to visitors at the Pacific Science Center.
Big Data Mentor
Washington State Board for Community and Technical Colleges
Oct 2014 – August 2017
Worked with community college faculty to develop ideas of how they could incorporate quantitative reasoning and big data into their classes.
University of Cape Town
Aug 2015 – Aug 2015
Taught three day masterclass on patient stratification and biomarker discovery to 16 researchers from the University of Cape Town. This was a shortened form of the Systems Biology of Disease course that we have developed at the Institute for Systems Biology. It was tailored specifically to bring skills that would help the trainees meet their research goals.