Great Lakes Bioinformatics Conference 2013

Keynote Speakers

updated April 04, 2013

Ivet Bahar
University of Pittsburgh

Title: Protein Dynamics: Relevance to Sequence Evolution and Drug Discovery

Abstract: Most proteins are molecular machines. They undergo collective motions under physiological conditions. These motions are uniquely defined by their 3-dimensional structure. Several studies in the last decade further showed that these structure-encoded motions, also called intrinsic dynamics, are relevant to biological function. For example, transporters alternate between outward- and inward-facing conformers to enable uptake and release of substrate from the extracellular and intracellular media, respectively; many allosteric machines undergo cooperative transitions between tense and relaxed conformers during their allosteric cycles; or many enzymes fluctuate between closed and open substates in order to accommodate ligand binding and stabilization. These observations brought up new questions with regard to the significance of intrinsic dynamics in the evolutionary selection of structure. The selected structures are not only those maintaining their stability in response to possible mutations, but also those possessing suitable intrinsic flexibilities to accomplish functional changes in conformation. Notably, these properties have important implications with regard to drug discovery. Recent years have indeed drawn attention to two challenging issues in computer-aided assessment of protein targets: First, proteins are not static entities, and drugs need to be designed to bind these moving targets. Second, not all proteins present druggable sites. Accurate evaluation of target druggability and flexibility is key to discovering potent inhibitors of allosteric interactions, and this goal is within reach with significant progresses made in recent years on modeling protein dynamics and assessing druggability.

Biography: Professor Ivet Bahar is the John K. Vries Chair of Computational & Systems Biology at the University of Pittsburgh (Pitt), School of Medicine. She joined the School of Medicine at Pitt in 2001 to start a new Center for Computational Biology and Bioinformatics, as a Professor in the Department of Molecular Genetics and Biochemistry. She founded the Computational Biology Department in 2004, and the Department of Computational & Systems Biology, in 2009. She also founded the Joint PhD Program in Computational Biology between Carnegie Mellon University (CMU) and the University of Pittsburgh (together with Prof RF Murphy, CMU co-director) in 2005. Prior to joining Pitt, Dr. Bahar worked as an Assistant Professor (1986-87), Associate Professor (1987-1993) and Professor (1993-2001) at the Chemical Engineering Department of Bogazici University, Istanbul Turkey. She served as the Director of the Polymer Research Center at the same university (1993-2000). She authored more than 200 scientific papers, which received more than 8,000 citations to date. She is an elected member of the European Molecular Biology Organization (EMBO) and is currently serving on several national and international scientific review and/or advisory committees.

Ziv Bar-Joseph
Carnegie Mellon University

Title: Reconstructing Dynamic Networks in Development and Disease

Abstract: Transcriptional gene regulation is a dynamic process and its proper functioning is essential for all living organisms. We have been developing methods to integrate time series data (including gene expression and protein-DNA interaction data) with static data (including sequence and protein interactions) for reconstructing such networks. The models take into account various aspects of post-transcriptional regulation including temporal regulation by microRNAs and provide a link between regulatory and signaling networks that explains the observed activation of genes. I will discuss the application and experimental validation of predictions made by our methods for studying lung development in mice and immune response in humans. Results from this analysis indicate that some disease progression pathways may represent partial reversal developmental processes.

Biography: Ziv Bar-Joseph is an Associate Professor in the Lane Center for Computational Biology and the Machine Learning Department at the School of Computer Science at Carnegie Mellon University. His work focuses on the analysis and integration of static and temporal high throughput biological data for systems biology and on improving algorithms for distributed computational networks by relying on our increased understanding of how biological systems operate and what makes them robust and adaptable. Dr. Bar-Joseph has been the co-chair of the RECOMB meeting on Regulatory Networks and Systems Biology and is currently an associate editor of Bioinformatics. He received his Ph.D. from the MIT in 2003. He was the recipient of the DIMACS-Celera Genomics Graduate Student Award in Computational Molecular Biology and the NSF CAREER award. Most recently he was named the 2012 recipient of the Overton prize in computational biology, awarded annually by the International Society for Computational Biology (ISCB).

Jing Li
Case Western Reserve University

Title: Haplotype Reconstruction in Large Pedigrees: Recent Development and Applications

Abstract: Haplotypes can provide important information for understanding genetics of human traits. However haplotypes cannot be obtained directly using current genotyping/sequencing platforms, which stimulates extensive investigations of computational methods to recover such information. In this talk, I will mainly discuss some recent developments in addressing computational challenges for haplotype inference from large families with many ungenotyped members.

I will also discuss some applications that can take advantage of the inferred haplotypes.

Biography: Dr. Jing Li is an Associate Professor in the department of Electrical Engineering and Computer Science at Case Western Reserve University. He received B.S. in Statistics from Peking University in 1995 and Ph.D. in Computer Science from the University of California - Riverside in 2004. Dr. Li's research mainly focuses on the development of efficient computational algorithms in computational molecular biology and bioinformatics. More information about his work can be found at

  Isidore Rigoutsos
Thomas Jefferson University

Title:   Unraveling the Rules of MicroRNA Targeting: Towards an "Expanded" Model

Abstract:  MicroRNAs (miRNAs) are short non-coding RNAs that regulate their target's expression in a sequence-dependent manner. Ever since the first reported animal heteroduplex in 1993 it has been clear that a portion of the 5´ region of a miRNA plays a central role in the recognition of the miRNA’s target. This portion typically spans positions 2-7 from the miRNA’s 5´ end and is known as the ‘seed.’ For the past decade, miRNA targeting has been studied largely under the paradigm of the standard model that calls for Watson-Crick base-pairing in the seed region with targets that are located primarily in the 3´ untranslated regions of protein-coding genes and are typically conserved across organisms.  

In recent years, we and others have been reporting specific examples of miRNA:mRNA heteroduplexes that are functionally important and transcend the standard model. These examples revealed that base-pairing in the seed region is not contingent solely upon Watson-Crick coupling and that partial sequence complementarity, in the form of G:U wobbles and bulges, within the seed region is in fact permissible and leads to functioning heteroduplexes.  Also, using molecular dynamics simulation of the Argonaute silencing complex, we demonstrated that non-standard heteroduplexes whose ‘seed’ region includes several G:U wobbles and/or bulges are admissible and do not affect the stability of the complex.  These findings support what we have been referring to as the "expanded" model of miRNA:target interactions. 

In this presentation, I will describe our latest results on miRNA targeting and miRNA promiscuity, and discuss sequence (Watson-Crick vs. wobble) and architecture (bulge vs. no bulge) preferences in the seed region that emerge from our analyses.

Biography:  Dr. Rigoutsos is the Director of the Computational Medicine Center at Thomas Jefferson University (TJU). He joined TJU in early 2010 as a Professor in the Department of Pathology, Anatomy & Cell Biology. He also has joint appointments in the Department of Cancer Biology, and the Department of Biochemistry & Molecular Biology, and is a member of the Kimmel Cancer Center at TJU.  For nearly 18 years prior to joining TJU, Dr. Rigoutsos was with IBM's Thomas J. Watson Research Center where he founded and managed the Bioinformatics and Pattern Discovery group. In parallel to his IBM tenure, Dr. Rigoutsos was for a decade a Visiting Lecturer at MIT's Dept. of Chemical Engineering where he taught graduate-level classes and summer professional courses in Bioinformatics.

Dr. Rigoutsos’ involvement in the field of Computational Biology spans more than twenty years. During this time, he has been developing computer algorithms for studying genomic architecture and biological sequences and analyzing very large biological datasets. Since 1996, his efforts have revolved around the theoretical and practical aspects of pattern discovery and the design of pattern-based solutions to a variety of problems from genomics, genetics, molecular biology and medicine.  Starting in 2002, Dr. Rigoutsos’ work placed particular emphasis on the study of non-coding RNAs (microRNAs, pyknons, piRNAs, etc.). Of particular interest are the questions surrounding the biogenesis of such non-coding RNAs, the mechanisms of action and the discovery of their targets, the discovery of novel organism-specific regulatory sequences, and the elucidation of the roles of non-coding RNAs in the onset and progression of disease.