Calendar of Relevant Events at Carnegie Mellon University
Host: Dr. Jian Ma, Associate Professor, Computational Biology Department
Abstract: Arbor Biotechnologies is a venture-backed startup at the intersection of technology and biology, harnessing nature’s proteins to transform industries. This career panel will feature three speakers from Arbor, who will talk about their career paths, their experience of working at and hiring for a rapidly growing startup, and how they are applying computer science and engineering to the biological sciences. Come learn how lessons from Goldman Sachs are being used to make sense of terabases of biological data. Hear tips from a former scientific editor, who is now back to writing papers. Gain insight into a research career in biotech. This will be an interactive session with plenty of opportunities to ask questions of the panelists.
About the panelists:
David Cheng, Interim CEO & Director of Search
David built electronic trading systems and strategies from the ground up for Goldman Sachs and JP Morgan before joining the founding team at Arbor as employee number three. He is a computer scientist from MIT, and also spent some time at Google before it was a public company.
Craig Mak, Director of Strategy
Craig was the founding editor of Cell Systems and senior editor at Nature Biotechnology before joining the Arbor team. He has degrees in computer science and biology from MIT and the University of California San Diego.
Roy Ziblat, Senior Scientist
Roy studied lipid membranes, often developing new experimental setups and methods, as a research fellow in the School of Engineering and Applied Sciences at Harvard and during his PhD in Chemistry at the Weizmann Institute of Science. In addition to being a structural biologist, he is a physicist and mathematician.
Systematic Mapping of Cellular States and Regulatory Circuits
Guo Cheng Yuan
Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute and Harvard Chan School of Public Health
A multi-cellular organism contains diverse cell types, each specializing in distinct biological functions. The behavior of individual cells (aka cell states) also changes dynamically due to intrinsic and extrinsic variation. The maintenance of cell type identity and cell state dynamics is essential for normal physiology; disruption may lead to diseases or even embryonic death. In recent years, single-cell analysis has emerged as a powerful tool for systematic characterization of cellular heterogeneity, reconstructing developmental trajectories, and dissecting the complexity in human diseases. Both the intrinsic regulatory network and spatial environment are contributors of cellular identity and result in cell state variations. However, their individual contributions remain poorly understood. In this talk, I will present several methods our groups have developed for characterizing cellular states from single cell data. I will also talk about our work for understanding the gene regulatory mechanisms underlying cell-state maintenance and transitions and touch upon the issue of cell-environment interactions.
Assistant Professor of Computational Genomics
Professor of Biology
Dr. Fairbrother majored in Chemistry at Oberlin College (Oberlin, OH) and received his PhD from Columbia University in 2000. Dr. Fairbrother was a PhRMA Post-doctoral Fellow in Informatics at Massachusetts Institute of Technology (MIT) under mentorship of Christopher Burge and Nobel Laureate Phillip Sharp. Dr Fairbrother is currently a tenured, associate professor in the MCB Department and the Director of Graduate Studies for the Center for Computational Molecular Biology at Brown. His research has focused on precision medicine and RNA genomics. the Fairbrother lab is using high-throughput biochemical screens and computational methods to understand the specificity of RNA processing. Results from Dr. Fairbrother’s lab suggest 1/3 of all hereditary disease mutations affect the processing of genes. More recently, Dr. Fairbrother and his laboratory have become interested in developing methods for analyzing clinical sequencing experiments (e.g., whole-genome and whole-exome sequencing data). To this end, he is active with the Mendelian Genetics Research Group at Harvard.
Ralph S. O’Connor Associate Professor of Biology
Associate Professor of Computer Science
Our lab’s research is in genome informatics, the use of computational and statistical approaches to understand genomes. Our ultimate goal is to achieve a complete understanding of the structure and function of genomes. Specifically, how information is encoded in genomes and how this encoding allows for precise reproducible biological processes and developmental programs, yet is harnessed by evolution to generate remarkable diversity. We work toward this goal both through the study of genome function and evolution, and through the development of tools that support the broader genomics community. For more information, please visit: http://bio.jhu.edu/directory/james-taylor/
About James: James Taylor is the Ralph S. O’Connor Associate Professor of Biology and associate professor of computer science at Johns Hopkins University. Until 2014, he was an associate professor in the departments of biology and mathematics and computer science at Emory University. He is one of the original developers of the Galaxy platform for data analysis, and his group continues to work on extending the Galaxy platform. His group also works on understanding genomic and epigenomic regulation of gene transcription through integrated analysis of functional genomic data. James received a Ph.D. in computer science from Penn State University, where he was involved in several vertebrate genome projects and the ENCODE project.
Workflows for high-resolution structure determination by single particle Cryo-EM
Associate Professor of Computer Science and Biochemistry
Algorithm and method development for high-resolution structure determination of protein structure using single-particle cryo-electron microscopy, cryo-electron tomography and sub-volume averaging.
Recent advances in direct electron detector technology combined with effective strategies for image analysis have enabled the routine use of single particle cryo-Electron Microscopy (EM) to determine the structure of a variety of protein complexes at near-atomic resolution. The increased availability and access to cryo-EM resources within the structural biology community, has highlighted the need for robust and automated workflows for data analysis that can effectively and rapidly convert raw data into 3D structures. Although many components of the data processing pipeline can already run in an unsupervised manner, there are still several steps where user involvement is required in order to produce meaningful structures. The identification of these bottlenecks is the first step towards achieving the ambitious goal of fully automating the structure determination process by cryo-EM. In this talk, I’ll identify some of the roadblocks that stand in the way of full automation in single particle image analysis and discuss strategies for streamlining and establishing robust workflows for high resolution structure determination by cryo-EM.
Associate Professor of Computer Science and Engineering
Associate Professor of Biochemistry and Molecular Biology
My research falls at the interface of biology and theoretical computer science, specifically in problems where rigorous algorithms and analysis can have a demonstrated impact in the biological sciences. My main focus has been on genome assembly and variation detection, though I am interested in a variety of areas such as phylogenetics, graph theory, computational complexity, on-line algorithms, and networking.