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Computational Biology
Trends and Careers
www.flickr.com
IntroductionOlena Marchenko, Senior, Colby College: Computer Science and Molecular Biology.
Manasi Vartak, Senior, Worcester Polytechnic Institute: Computer Science and Math.
Nancy Amato, Professor, Dept. of Computer Science and Engineering, Texas A&M University: Motion planning, computational biology, robotics, computational geometry, animation, EDI, VR. Parallel and distributed computing, parallel algorithms, performance modeling and optimization. [email protected]
Anne Condon, Professor, Dept. of Computer Science, University of British Columbia: Computational complexity theory and design of algorithms, prediction of the secondary, structure of nucleic acids, design of molecules using prediction tools. [email protected]
Stephanie Taylor, Clare Boothe Luce Assistant Professor, Dept. of Computer Science, Colby College: Systems Biology, phase sensitivity of oscillatory systems, computational models of circadian clocks. [email protected]
Special thanks to Prof. Raquell Holmes
Talk Outline
• What is Computational Biology?
• Current Trends and Challenges
• Careers
• Q/A
http://www.sciencedaily.com/images/2005/07/050730093601.jpg
What is Computational Biology?
Computational molecular biology is conceptualizing
biology in terms of molecules & applying
“informatics” techniques - derived from disciplines
such as mathematics, computer science, and statistics -
to organize and understand information associated
with these molecules, on a large scale
(Gerstein, 2007)
Olena Marchenko, Colby College
Computational Biology is...
http://compbio.cs.huji.ac.il/
Research Directions
• Dynamical Interactions
• Structures and Functions of BioComponents
• Design of Artificial Components
Dynamical phenomena in cell
Del Castillo & Moore, 1959
Networks and Relationships of Biological Components
W.Chen & B. Schoeberl, 2009
Data Types vs. Abstraction Level
•DNA/Amino acids
sequences
•Protein structure
•Intermolecular interactions:
•Dynamics of interactions
•Systems
http://www.bioteach.ubc.ca/what-is-bioinformatics/
Producing and Utilizing the Knowledge
www.biomedcentral.com/.../1471-2105-6-287-1.JPEG
Computational Approaches
Modeling regulatory networks
– Bayesian Networks
Inferring regulatory network models from
experimental data
– microarray data
– computation inference of module networks
Architectural properties of regulatory networks
– modular structure of regulatory networks
•Ask the right questions
•Select/Build the tools
•Learn more about the system and ask new questions
Ultimate goal : to understand all aspects of an
organism and its environment through the
combination of a variety of scientific fields.
Underlying Approach
Talk Outline
• What is Computational Biology?
• Current Trends and Challenges
• Careers
• Q/A
http://www.sciencedaily.com/images/2005/07/050730093601.jpg
Current Trends and Challenges
Overall trend: More people are recognizing that computer science can enable biological discovery
Prof. Stephanie Taylor, Colby College
Grand Challenge:Relating genotype to phenotype in complex environments (iPG2P)
GenotypeThe exact DNA sequence of an individual
PhenotypeThe collection of all observable and measurable traits of that individual
(Zhang et al. BMC Plant Biology 2008)(Wood et al. Science, 2001)
iPG2P:Multi-scale, Dynamic Problem
(ghr.nlm.nih.org; wikitextbook.co.uk; generalhorticulture.tamu.edu; scienceblogs.com)
iPG2P:Multi-scale, Dynamic Solutions
(ghr.nlm.nih.org; wikitextbook.co.uk; generalhorticulture.tamu.edu; scienceblogs.com)
Bioinformatics: learn from the data
Modeling: simulate the dynamical interactions among components
iPG2P:Cyber-infrastructure Challenges
Data management. Relating different data. Learning from the data.
1. Good middleware for virtual, integrated, distributed databases: Pipelining of NextGen sequence data into virtual genotype and molecular phenotype databases.
2. Data integration (adding depth and context to establish relationships and meaning) – relate information from such virtual databases to permit deeper insights, generation of hypotheses, evaluation of models, practical applications, etc
3. Statistically-based tools for use in inferring relationships. Many such tools exist, but the value-added aspect in the current context is to make them smoothly interoperable with the other features of the cyberinfrastructure.
iPG2P:Cyber-infrastructure Challenges
Data and model visualization.
4. Visual analysis tools. It is necessary to present integrated data to users in ways that are both concise and revealing. This includes multi-dimensional data displays.
iPG2P:Cyber-infrastructure Challenges
Modeling and model analysis.
5. Modeling framework tools to support the construction, parameter estimation, sensitivity analysis, and utilization of models. Again, the value added is in interoperablility.
Talk Outline
• What is Computational Biology?
• Current Trends and Challenges
• Careers
• Q/A
http://www.sciencedaily.com/images/2005/07/050730093601.jpg
Careers in CompBioManasi Vartak, Worcester Polytechnic Institute
• CompBio as an Undergrad• CompBio in Grad school• Jobs in CompBio
- Teaching- Research- Bio/Pharmaceutical Industry- Computer Industry
• What does it take? • Pros and Cons
CompBio as an Undergrad
• Not a mainstream major (Exceptions: U. of Nebraska, George Washington U. etc)
• Alternatives:– Concentration or focus– Bio + CS double major– Computational Science and Engineering major– Interdisciplinary courses: Bio, Math
department
• Summer Research Programs– CRA-W DREU, UConn Health Center
CompBio in Grad School I
• MS/Ph.D. programs at several universities– Harvard, MIT, CMU, Princeton, UW, ETH
Zurich, Cambridge University, EPFL, Max Planck Institute etc.
• Program administration:– CS programs with CompBio focus– Joint/Interdisciplinary programs – Programs administered by
Biochemistry/Bioengineering depts.
CompBio in Grad School II
• Specific funding opportunities:– Ph.D.: IGERT: Integrative Graduate Education
and Research Traineeship– Postdocs: NIH/universities/research labs– Traditional opportunities
• Part time option: – Grad courses at universities– Online courses
Jobs in CompBio I
• Teaching:– Faculty at a university: research + teaching– Hot area; universities looking for talent
• Research:– CompBio scientist:• Labs: National Research Council IIT
Bioinformatics Lab, Canada; IBM T. J. Watson Research Center• Medical centers: UConn Health Center
– Software engineer at research lab
Jobs in CompBio II
• Bio/Pharmaceutical companies:– Pharma CompBio divisions: Genentech,
Amgen etc.– CompBio companies: Zymeworks, Entelos
• Software Development Companies:– Write software for use by biologists e.g.
modeling, simulation, data mining– CS skills can land you a job anyway!
What does it take?
• Willingness to collaborate!
• OK with not knowing everything
• Understanding that not everything has a rigid algorithm
• Understanding the qualitative and quantitative aspects of the work
+/-Pros Cons
Very hot field, professionals in demand
Industries wary of investing too much
Universities hiring Not too many positions
Opportunities for making big contributions; few senior researchers
Success depends on collaborations; your part in the success may be small
Real life applications Skepticism about the field
Resources
• www.iscb.org *
• www.bioinformatics.org
• http://ocw.mit.edu/OcwWeb/Biology/7-91JSpring2004/CourseHome/
• http://stellar.mit.edu/S/course/6/fa08/6.047/
• http://www.embl-heidelberg.de/
• http://www.ncbi.nlm.nih.gov/pubmed/16650809
• http://iplantcollaborative.org/
• http://www.bioteach.ubc.ca/what-is-bioinformatics/
• http://www.cs.washington.edu/homes/tompa/papers/