• Pachter, Thomson, Voorhees

New Faculty Spotlight

While Caltech is a small institution relative to other top universities across the nation, its influence on scientific research in a wide variety of fields is immeasurable. Part of what makes this possible is the rigorous recruitment and hiring of the most creative and cutting-edge faculty in the world. The Division of Biology and Biological Engineering is no exception and eagerly welcomes new faculty members praised for their enthusiasm, interdisciplinarity, and innovation.

Lior Pachter is a leading computational biologist working in genomics who will arrive in April 2017 from UC Berkeley to take up the position of Bren Professor of Computational Biology at Caltech. His career began in comparative genomics, initially in genome alignment, annotation, and the determination of conserved regions using phylogenetic methods. He contributed to the mouse, rat, chicken and fly genome sequencing consortia, and the pilot phase of the ENCODE project. More recently he has become focused on functional genomics, which includes answering questions about the function and interaction of DNA, RNA and protein products. He is particularly interested in applications of high-throughput sequencing to RNA biology. Pachter is a bona fide mathematician with a B.S. in mathematics from Caltech ('94), a Ph.D. in mathematics from MIT ('99) and initial tenure at Berkeley as a Professor of Mathematics. Lior's entry into biology came while a graduate student at MIT, which included significant interactions with the Broad Institute. Lior is noted for his ability to go from basic biology all the way to impactful, high-quality software that truly enables quantitative functional genomics research.

New Assistant Professor of Biology and Biological Engineering, Rebecca Voorhees, will arrive in July 2017 from the Medical Research Council Laboratory of Molecular Biology (MRC-LMB), Cambridge, England, where she is a prestigious Sir Henry Wellcome fellow. Rebecca received her B.S. and M.S. degrees in molecular biophysics and biochemistry from Yale in 2007, and her PhD in molecular biology from the University of Cambridge in 2011. Rebecca studies the chemical and molecular mechanisms of protein production, localization, and quality control. Her current research focuses on what happens to proteins after they are synthesized by the ribosome. First, how are they trafficked to different compartments within the cell, and second, what happens when these processes fail, this work is critical for understanding the molecular basis of numerous human diseases that affect protein folding and localization, such as cystic fibrosis and Alzheimer's disease. Among other things, Rebecca has used cryo-electron microscopy to study how the cell selectively recognizes hydrophobic sequences that must be delivered to the endoplasmic reticulum for their maturation. She made the unexpected discovery that the cell uses progressively more stringent filters for identifying these hydrophobic substrates, which ensures extremely high fidelity in membrane targeting and insertion, thereby preventing protein mislocalization and ultimately disease. For her future work, Rebecca plans to continue to utilize both structural and functional techniques, including X-ray crystallography and single-particle cryo-electron microscopy, to explore detailed structural and mechanistic problems in cell biology.

New Assistant Professor of Computational Biology, Matt Thomson, will arrive in January 2017 from UCSF where he is a Fellow with an independent laboratory. Matt received his undergraduate degree in Physics from Harvard University in 2001 and his PhD in Biophysics from Harvard in 2011. Matt's group is applying quantitative experimental and modeling approaches to gain programatic control over cellular differentiation. He is developing mathematical models to ask how cellular regulatory networks generate the vast diversity of cell-types that exists in the human body. He is applying models to engineer and rewire cellular physiology and to synthesize new types of cells that do not exist in nature. He is also developing simplified cellular systems in which physical models can be applied to control the geometry and morphology of different cell types. He uses a combination of approaches including mathematical modeling, machine learning, statistical analysis of high-throughput gene expression data, and single cell RNA sequencing experiments.  Recent accomplishments include: Engineering an all-optical differentiation system in which he could optically-deliver pulsed neural differentiation inputs to embryonic stem cells; creating new computational tools for deriving cell state trajectories from single cell RNA-Seq data; and developing a stochastic modeling framework for analyzing principles that enable robust self-organization of the mammary gland.