BCB Symposium

Friday, March 25, 2016 - 8:00am
Event Type: 

The Bioinformatics and Computational Biology Graduate Student Organization (BCBGSO) is planning to host their Second Annual BCB Symposium, “Current Topics in Bioinformatics and Computational Biology” on March 25, 2016 at Reiman Gardens.  Registration is now open at: https://docs.google.com/forms/d/1WNDFO3nz7Oin0bG69bZSe5NfZ5MFtoQEfFljCUMoSgo/viewform?c=0&w=1&usp=send_form .

The day-long symposium will feature several speakers, a poster session highlighting graduate student research, a presentation on professional networking, and will include time for graduate students in the BCB program to interact with the speakers.  Speakers are being asked to share about their current research, their journey to current positions, and offer advice to current students who would like to pursue a career in industry or academia.

Confirmed speakers include the chair of the BCB Graduate Program, Dennis Lavrov, BCB Faculty member in the Department of EEOB, two alumni from the BCB Graduate Program, Preeti Bais and Michael Zimmermann.  And, the featured speaker for our symposium will be Casey Greene, an Assistant Professor in the Department of Systems Pharmacology and Translational Therapeutics in the Perelman School of Medicine at the University of Pennsylvania.

We are so pleased to have these speakers for our symposium and are especially glad to welcome Preeti and Michael back as speakers for this event.

Preeti Bais

Preeti Bais, Jackson Laboratory (https://www.jax.org/) Associate Computational Scientist at the Connecticut campus.Preeti Bais

Dr. Bais' mentors while in the BCB program were Julie Dickerson, BCB Faculty member in ECPE, and Basil Nikolau, BCB Faculty member in BBMB. The title of her dissertation was: Bioinformatics methods for metabolomics based biomarker detection in functional genomics studies. 


From her abstract: "The biochemical and physiological function of a large proportion of the approximately 27,000 protein-encoding genes in the Arabidopsis genome is experimentally undetermined using sequence homology techniques alone. This thesis presents a set of bioinformatics resources including a software platform for data visualization and data analysis that address the key issues in incorporating the metabolomics data for functional genomics studies."

Michael Zimmermann

Michael ZimmermannMichael Zimmermann, Mayo Clinic ( mayoclinic.org ) Health Sciences Research, Division of Biomedical Statistics and Informatics, Rochester, MN. Dr. Zimmermann's mentors were Robert Jernigan, BCB Faculty member in the BBMB Department and Dr. Edward Yu, BCB Faculty member in the BBMB, Physics and Chemistry Departments.  The title of his dissertation was "Mechanistic insights on important biomolecules derived using simple dynamics models from extending the reach of elastic network modeling".

From his abstract: "The dynamics of biomolecules are important for carrying out their biologic functions, but these remain difficult to probe in detail experimentally, so that their accurate computational evaluation is an important field of ongoing study. Critical questions remain open such as what are the importance of individual interactions within a structure, the composition of denatured states and equilibrium native ensembles, as well as the role and conservation of flexibility in functional dynamics. The tools of Molecular Dynamics, Monte Carlo simulation, and Normal Mode Analysis coupled with knowledge-based approaches represent the mainstay of computational approaches used in this field.  The primary focus of this dissertation is to explore the functional dynamics of important biomolecules while extending the utility of Normal Mode Analysis using Elastic Network Models through the application of novel analysis methods. "

Casey Greene

Casey Greene is an Assistant Professor in the Department of Systems Pharmacology and Translational Therapeutics in the Perelman School of Medicine at the University of Pennsylvania. His Integrative Genomics Lab (http://www.greenelab.com/) develops deep learning methods that integrate distinct large-scale datasets to extract the rich and intrinsic information embedded in such integrated data. This approach reveals underlying principles of an organism’s genetics, its environment, and its response to that environment. Extracting this key contextual information reveals where the data’s context doesn’t fit existing models and raises the questions that a complete collection of publicly available data indicates researchers should be asking.

Photo of Casey GreeneIn addition to developing deep learning methods for extracting context, a core mission of his lab is bringing these capabilities into every molecular biology lab. Before starting the Integrative Genomics Lab in 2012, Casey earned his Ph.D. for his study of gene-gene interactions in the field of computational genetics from Dartmouth College in 2009 and moved to the Lewis-Sigler Institute for Integrative Genomics at Princeton University where he worked as a postdoctoral fellow from 2009-2012. The overarching theme of his work has been the development and evaluation of methods that acknowledge the emergent complexity of biological systems.