Michael White, Ph.D.

Michael White, Ph.D.

Assistant Professor of Genetics

The Edison Family Center for Genome Sciences & Systems Biology
Campus Box 8510
4515 MicKinley Ave.
St. Louis, MO 63110
Office: (314) 362-1034

My Work

Research Publications

Science column for Pacific Standard


Cis-Regulation in Photoreceptors

I am interested in the fundamental decision faced by a gene, which is to read the state of the world and determine whether or not to be expressed. How this decision is made explains one of the most mind-boggling facts in biology: Adult organisms consist of up to trillions of highly organized and extremely specialized cells that differ greatly from each other, and yet most of these cells have exactly the same DNA. This is possible only because genes are associated with regulatory DNA that ensures that they are expressed at the right place and time. My goal is to understand how gene expression decisions are encoded in the sequence of regulatory DNA.

Current areas of focus:

DNA sequence determinants of transcription factor binding in photoreceptors
The vertebrate transcription factor CRX drives the expression of genes necessary to maintain the cell identity of rod and cone photoreceptors. How CRX recognizes its functional targets amidst a vast genomic excess of potential binding sites is not understood. I use massively parallel reporter assay technology to discover the DNA sequence features that define a functional CRX binding site.

Cis-regulatory mechanisms of retinopathy mutations in CRX
Mutations in CRX cause several different inherited blinding diseases. How different mutations in this single gene lead to different pathologies is not known. We are using massively parallel reporter assay technology to discover how different disease mutations alter the activity of CRX.

Thermodynamic models of cis-regulation
The activities of cis-regulatory elements are determined by the transcription factors that bind them. The relationship between a DNA sequence, bound transcription factors, and levels of gene expression is often complex and non-intuitive. I use a statistical thermodynamic framework to model the relationship between DNA sequence and gene expression.

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