BCB Symposium - a big success

March 28, 2016

The Bioinformatics and Computational Biology Graduate Student Organization (BCBGSO) hosted their Second Annual BCB Symposium, “Current Topics in Bioinformatics and Computational Biology” on March 25 at Reiman Gardens. 

About 80 faculty and students attended the day-long symposium which featured several speakers, a poster session and 3-minute speeches highlighting graduate student research projects, and a presentation on professional networking.  At the luncheon, students had time to interact with the speakers. 

All speakers shared about the importance of collaborations during a graduate career as central to a career after graduation.  Relationships matter in advancing your career.  And, ethics matter even more.  Your career depends on building a network of colleagues who respect your integrity in science whether you pursue a position in academia or industry.

Speakers included the chair of the BCB Graduate Program, Dennis Lavrov, a 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 was 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 were so pleased to have these speakers for our symposium and were 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."

The title of Preeti's talk at the symposium was: Case Studies from Mass Spectrometry Based Metabolomics and her abstract follows:

Metabolomics has emerged as new systems biology tool for quantitative identification of the state of a complex biological system. I will highlight some case studies where I have used mass spectrometry based metabolomics in plant functional genomics, patient derived xenograft (PDX) mouse models for cancer studies and personalized medicine. I will also talk about the need for bioinformatics infrastructure development for multi OMICS studies and the current opportunities in a non-profit organization for BCB graduates.

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. "

The title of Dr. Zimmermann's presentation at the symposium was: Associations between Immune Cell Populations, Integrated High-Throughput Data, and Humoral Immunity to Influenza Vaccination.  His abstract follows:

"Many systems biology studies of human immune responses have used Peripheral Blood Mononuclear Cell (PBMC) samples to generate transcriptomic datasets in order to discern the molecular determinants of robust response and how these features are modified in cases of vaccine failure. A key advantage to this approach is that blood is easily obtained from individuals and PBMCs can be isolated without intensive purification processes that may alter cellular gene expression patterns.  A disadvantage is that PBMCs are a diverse mixture of cell types each with a potentially unique gene expression response to a given stimulus. A common theme observed throughout these studies has been that few individual genes contribute consistently to immune responses, and those that do have relatively small effect sizes. 

In this study, we integrate mRNA-Seq, CpG methylation, and flow-cytometry-derived PBMC composition in order to identify each data type’s association with humoral immune response outcomes to influenza vaccination. First, we hypothesize that many of the differentially expressed genes observed upon influenza vaccination are indicative of changes in the composition of participants’ PBMCs, rather than direct induction (or suppression) of the genes themselves. Second, we hypothesize that more robust models of vaccine response can be generated by accounting for the interplay between PBMC composition, gene expression, and gene regulation. These hypotheses were tested within a cohort (n = 159) of healthy participants who received seasonal trivalent influenza vaccine. Blood was taken pre-vaccination and at 3 and 28 days post-vaccination. All three data types (mRNA-Seq, CpG methylation, PBMC composition) were generated at each time point. 

We find that 1) the variability of participants’ PBMC composition is highly correlated with variability in gene expression; 2) many of the genes with statistically significant gene expression changes are associated with changes in specific cell subsets; 3) these genes are well-expressed by those cell subsets in independent transcriptomic studies; and 4) the three datasets provide complementary information in the prediction of humoral immune response outcomes. We believe that these findings are important for the interpretation of current omics-based studies and, when applicable, should be accounted for within ongoing studies of immune responses to viral vaccines."

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.

The title of his presentation at the symposium was:  Bringing "big data" analyses into routine use in life sciences labs.  Here is the abstract:

Science has been described in terms of a drunk searching for keys under a lamppost. Sometimes we look where the light is because it's easiest. Consequently, the research questions that we're asking are driven not just by what is true but by where we started and the path that we have taken. I'll discuss data-driven techniques that allow us to aggregate, summarize, and interpret publicly available data. These methods facilitate a "data search" that complements a literature search and help us as we design new projects and experiments. This allows us to take advantage of the results that the authors didn't write about in addition to those that they did, which we expect will broaden the hypotheses that we are generating and testing. Ultimately our lab's goal is to make this type of analysis a routine part of every life sciences lab's workflow, so I'll discuss our progress towards webservers that put these methods into the hands of biologists.

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