Mitchell Guttman is an Assistant Professor in the Division of Biology and Biological Engineering at the California Institute of Technology. His previous work explored unknown regions of the genome, identifying genes that do not produce proteins but do function in other important ways. His research has defined a new class of players in the genome and shed light on how they work.
Guttman described a new class of genes called lincRNAs, short for large intergenic noncoding RNA. These genes perform many jobs in the cell, among them regulating the plasticity of embryonic stem cells, controlling how they become any other kind of cell. Guttman and his colleagues defined lincRNAs by exploiting chromatin signatures, chemical changes in the way DNA wraps around partner proteins. They showed that these RNA molecules can physically interact with protein complexes that control gene regulation. His work elucidated a potential role for lincRNAs as key organizers of protein complexes.
Guttman's work bridges experimental and computational aspects of biological research. His published work includes numerous papers integrating experimental approaches and computational approaches. Guttman has also published several key papers reporting new computational and statistical methods. As a graduate student, Guttman developed one of the first methods to reconstruct a mammalian transcriptome from RNA-Seq data. As an undergraduate, Guttman developed one of the first statistical methods for identifying recurrent driver mutations in cancer genomes.
Guttman received his PhD from the Department of Biology at MIT. He established his lab as an independent Fellow at the Broad Institute of MIT and Harvard prior to joining the faculty at Caltech in June 2013. He is a recipient of the 2012 NIH Director's Early Independence Award and was named one of Forbes magazine's "30 under 30" in science. Guttman also holds two degrees from the University of Pennsylvania: a Bachelor's degree in molecular biology and computational biology and a Master's degree in computational biology and bioinformatics.