Great Lakes Bioinformatics Conference 2013

Submissions

FOR QUESTIONS, PLEASE CONTACT

Meeting Coordinator:
Joyce Perhac
Ph: (412) 372-1899
Email:  This email address is being protected from spambots. You need JavaScript enabled to view it.

• Call for Original Research Papers (Proceedings)

• Call for Highlight Presentations

• Call for Flash Presentations

• Call for Poster Abstracts

Great Lakes Bioinformatics Conference 2013

General Information

• Registration

• Accommodations

• Location

• Travel Fellowships

Great Lakes Bioinformatics Conference 2013

Program

• Full Agenda
  

• Keynote Speakers
  

• Poster Session
  

• Poster Presentations
  

• Oral Presentations
  

Great Lakes Bioinformatics Conference 2013

Conference Location

The GLBIO 2013 conference is being held at Carnegie Mellon University Campus at the University Center in Pittsburgh, Pennsylvania, southwestern Pennsylvania approximately 22 miles from the Pittsburgh International Airport.

UNIVERSITY CENTER

5032 Forbes Ave.           
Pittsburgh, PA 15213       
(412) 268-2107   

CAMPUS MAP

Campus Map - www.cmu.edu/about/visit/campus-map.shtml (University Center #30 on map)

Directions/Parking:  www.cmu.edu/about/visit/directions-parking.shtml 

TRANSPORTATION

By Air
The Pittsburgh International Airport is 22 miles (approximately 45 minutes driving time) from the Carnegie Mellon campus. For more information, please visit FlyPittsburgh (www.flypittsburgh.com/) or call 412-472-3525.

Ground Transportation
Once you arrive at the Pittsburgh International Airport there are several transportation options to bring you to campus or to your hotel:

• Taxi service.The average taxi fare from the airport to campus is about $40-$45, plus gratuity. There are typically taxi cabs outside of the airport, but if you need to call a service, call Yellow Cab (412-321-8100) or Classy Cab (412-322-5080).
  
  
• Rental Car. You can rent a car on-site at the Pittsburgh International Airport. For more information, including a list of car rental agencies and phone numbers, please refer to the transportation services (www.flypittsburgh.com/car_rental) section of the Pittsburgh International Airport website.
  
  
• Public Bus Transportation with Port Authority Transit. The 28X Airport Flyer (www.portauthority.org/paac/) bus stops in the heart of the Carnegie Mellon campus and typically takes 45 minutes en route to the airport. The trip is free for Carnegie Mellon students and costs $2.75 for other passengers. For more information about schedules and rates, please visit the official PAT website (www.portauthority.org/paac/)  or call 412-442-2000.
  
  
• Super Shuttle. Super Shuttle (www.supershuttle.com/Locations/PITAirportShuttlePittsburgh.aspx) provides shuttle service between the airport and downtown hotels. For reservations, contact Super Shuttle at 1-800-258-3826.
  
  
• Hotel Shuttle Service. Because many hotels offer a complimentary shuttle from the airport to the hotel and then on to Carnegie Mellon, please inquire about this option when making your lodging reservations. Please refer to the transportation services  (www.flypittsburgh.com/limousines_taxis_buses) section of the Pittsburgh International Airport website for further contact information.
  

By Bus 
The Greyhound Lines (www.greyhound.com/en/locations/terminal.aspx?city=171150) bus terminal is located less than eight miles from campus in downtown Pittsburgh. For a detailed transportation schedule, call Greyhound at 1-800-231-2222. The Pittsburgh station is located at 55 11th Street and can be reached at 412-392-6513.

By Train
The Amtrak Train Station-Pittsburgh (www.amtrak.com/servlet/ContentServer?pagename=am/am2Station/Station_Page&code=PGH) is located at 1100 Liberty Avenue, less than five miles from campus. For information on rates and schedules, call Amtrak at 1-800-872-7245.

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Great Lakes Bioinformatics Conference 2013

Sponsors

• Sponsorship Opportunities
  

• Conference Sponsors
  
• Past Sponsors

GLBIO CONFERENCE PAST SPONSORS

• Agilent Technologies
• Appistry
• BioOhio
• Bioteam
• Bowling Green State University
• Cambridge Computer
• Carnegie Mellon University
• Case Comprehensive Cancer Center
• Case Western - Case Center for Proteomics & Bioinformatics
• Cincinnati Children’s Hospital Medical Center
• CLC Bio
• Compendia Bioscience
• Diagnostic Hybrids
• GeneCode
• Genomatix Software
• Great Lakes Bioinformatics Consortium
• IBM
• International Society for Computational Biology
• Kent State University
• Mathworks
• Miami University - Dept. of Botany
• Michigan Economic Development Corporation
• Michigan Technology University
• Ohio State University - Center for Applied Plant Sciences (CAPS)
• Ohio Supercomputer Center
• Ohio University (Russ College; Vice President Research; College of Arts and Science)
• Ohio University Edison Biotechnology Institute
• Procter & Gamble
• Springer
• The Ohio State University
• University of Akron
• University of Cincinnati Children's Hospital
• University of Michigan - DCM&B and OVPR
• University of Michigan - Biomedical Research Core Facilities
• University of Michigan - Metabolomics Core
• University of Pittsburgh
• Wittenberg University
• Wright State University

Great Lakes Bioinformatics Conference 2013

Tutorial Sessions - Tuesday May 14, 2013

Updated May 09, 2013

TUTORIAL 1
9:00 am - 11:00 am
Location: McConomy

Spatial Rule-based Modeling of Cellular Biochemistry with MCell/BioNetGen/CellBlender

James Faeder
University of Pittsburgh School of Medicine

Markus Dittrich
Pittsburgh Supercomputing Center

 Dr. Faeder is Associate Professor of Computational and Systems Biology at the University of Pittsburgh School of Medicine. His research focuses on the development of novel computational methods and tools for the modeling and simulation of cell regulatory networks, as well as their application to specific systems of basic and biomedical relevance including immune regulation, cancer, and chemo-sensing in bacteria. Dr. Faeder's group also leads the development of the BioNetGen software for rule-based modeling of biochemical systems and is currently working on the integration of rule-based and spatial modeling.

Dr. Dittrich is Director of the National Resource for Biomedical Supercomputing at the Pittsburgh Supercomputing Center. His group uses computer models and simulation to study the structure and function of synapses. He is also one of the main developers of the MCell simulation package.

Dr. Faeder and Dr. Dittrich are both members of the Cell Modeling component of the NIH P41 Center for Multiscale Modeling of Biological Systems (MMBioS) at the University of Pittsburgh, which supports development of the MCell simulation platform for simulation of spatially realistic 3-D cellular models
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TUTORIAL 2
12:00 pm - 2:00 pm
Location: McConomy

Exploring and Enabling Biomedical Data Analysis with Galaxy

Chair:    Anton Nekrutenko, Penn State University

Are you a biomedical researcher who needs to do complex analysis on high throughput NGS datasets?  Galaxy (http://galaxyproject.org/) is an open, web-based platform for data intensive biological research that enables non-bioinformaticians to create, run, tune, and share their own bioinformatic analyses. This workshop will show participants how to integrate data and perform simple and complex analysis within Galaxy. It will also demonstrate how Galaxy enables reproducibility in bioinformatics, and how to use visualization to refine and drive analysis, all within the Galaxy Framework. Finally, the workshop will highlight how researchers can install Galaxy on local computational resources, or using computational cloud providers such as Amazon Web Services.
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TUTORIAL 3
2:30 pm - 4:30 pm
Location: McConomy

Machine Learning in High Dimension for Genomic Data Analysis

Chair:   Seyoung Kim, Machine Learning in Biology

Abstract:  The recent advances in the high-throughput technology such as next-generation sequencing technology have allowed the researchers to collect a large amount of data for genomes and various other aspects of a cell system.  Such datasets hold the key to understanding the detailed mechanisms of the genetic control of a biological system and further deepening our knowledge of cell biology with a potential application to medicine. Genomic data often lie in a very high-dimensional space in which many entities interact with each other in a complex manner, as in expression measurements for tens of thousands of genes and genotypes of millions of genetic loci. This tutorial will introduce statistical machine learning techniques for learning statistical models of various kinds in very high-dimensional space with fast and highly scalable learning methods. In particular, the tutorial will discuss sparse learning methods for regression and graphical models, structured-sparse learning, multi-task learning, and optimization methods. Then, it will show how such techniques can be applied to various types of genomic datasets to extract complex epistatic and pleiotropic interactions among various entities in a biological system.

Bio:   Dr. Seyoung Kim is an assistant professor in the Lane Center for Computational Biology in the School of Computer Science at Carnegie Mellon University. Her research focuses on developing and applying statistical machine learning methods for understanding how the information encoded in genomes shapes the biological system in terms of gene regulations, phenotypes, diseases, and developments. Dr. Kim received her B.S. in computer engineering from Seoul National University, Korea, and Ph.D. in computer science from the University of California, Irvine. She was a postdoctoral fellow in Machine Learning Department at Carnegie Mellon University. She is a recipient of the NSF Career Award and the Alfred P. Sloan Fellowship.

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Great Lakes Bioinformatics Conference 2013

Education Session - Wednesday, May 15, 2013

3:45pm – 5:30pm
Location:  Connan

Educating Biologists and Bioinformatics Professionals for the Future - Are We Getting It Right?

Panel Co-Chairs: Guenter Tusch, Grand Valley State U, and Sarah Elgin,Washington U in St. Louis

Panelists:

• Lonnie Welch, Ohio University, Vice Chair ISCB Education Committee
• Sarah Elgin, Washington University in St. Louis, and Genomics Education Partnershipnformatics Core, CCMB
• Panayiotis (Takis) Benos, University of Pittsburgh, Joint Carnegie Mellon – U Pittsburgh Computational Biology Graduate Program
• Regina Lamendella, Juniata College
• Shawn Stricklin, Monsanto

Panel description:  The panel will explore the state of the art of bioinformatics curriculum efforts in the Great Lakes Region and on a national and international level by looking at different content areas from an undergraduate and graduate perspective, as well as current skill requirements in research and industry.

While bioinformatics education has not received much attention at the GLBIO and OCCBIO conferences during the last few years, the RECOMB Satellite Conference on Bioinformatics Education has intensified the discussion about the importance of bioinformatics education for undergraduate biology students. Russ Altman asked in his 1998 editorial an obvious question: ‘What is the proper curriculum for bioinformatics professionals?’ He warns that we must be careful not to define ‘curriculum’ narrowly as a list of required courses, but also consider career counseling, exposure to the culture of the discipline, and access to quality research projects to build enthusiasm and skills. The Curriculum Task Force of the ISCB Education Committee organized a ‘Birds of a Feather ‘(BoF) session at ISMB 2012 to discuss bioinformatics curriculum guidelines. In this open forum the participants considered curricular implications of the task force’s surveys of career opportunities, hiring practices of bioinformatics core facility directors, and existing curricula. We need to continue this dialogue.

The purpose of this panel is to extend the ongoing discussion started by the Curriculum Task Force of the ISCB Education Committee and to explore the state of the art of bioinformatics curriculum efforts by considering different approaches at the undergraduate and graduate level. In particular, we will discuss the   skills and understandings needed for a student to progress from an undergraduate to a graduate program, and to prepare for employment in research and/or industry. At the undergraduate level the need to develop specific bioinformatics courses, and to encourage biology majors to take course work in statistics and computer science, will be addressed. Other questions to consider are the use software in classroom and lab, the balance of methodological and applied elements in a graduate/undergraduate curriculum, how to incorporate soft skills (such as working in teams) and the culture of bioinformatics, and the need for continuing education.

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