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Computational Biology

Computational Biology. Trends and Careers. www.flickr.com. Introduction. Olena Marchenko , Senior, Colby College: Computer Science and Molecular Biology. Manasi Vartak , Senior, Worcester Polytechnic Institute: Computer Science and Math.

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Computational Biology

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  1. Computational Biology Trends and Careers www.flickr.com

  2. Introduction Olena 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. amato@cse.tamu.edu 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. condon@cs.ubc.ca 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. srtaylor@colby.edu Special thanks to Prof. Raquell Holmes

  3. Talk Outline • What is Computational Biology? • Current Trends and Challenges • Careers • Q/A http://www.sciencedaily.com/images/2005/07/050730093601.jpg

  4. What is Computational Biology? Olena Marchenko, Colby College 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)

  5. Computational Biology is... http://compbio.cs.huji.ac.il/

  6. Research Directions • Dynamical Interactions • Structures and Functions of BioComponents • Design of Artificial Components

  7. Dynamicalphenomena in cell Del Castillo & Moore, 1959

  8. Networks and Relationships of Biological Components W.Chen & B. Schoeberl, 2009

  9. 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/

  10. Producing and Utilizing the Knowledge www.biomedcentral.com/.../1471-2105-6-287-1.JPEG

  11. 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

  12. Underlying Approach • 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.

  13. Talk Outline • What is Computational Biology? • Current Trends and Challenges • Careers • Q/A http://www.sciencedaily.com/images/2005/07/050730093601.jpg

  14. Current Trends and Challenges Prof. Stephanie Taylor, Colby College Overall trend: More people are recognizing that computer science can enable biological discovery

  15. Grand Challenge:Relating genotype to phenotype in complex environments (iPG2P) Genotype The exact DNA sequence of an individual Phenotype The collection of all observable and measurable traits of that individual (Wood et al. Science, 2001) (Zhang et al. BMC Plant Biology 2008)

  16. iPG2P:Multi-scale, Dynamic Problem (ghr.nlm.nih.org; wikitextbook.co.uk; generalhorticulture.tamu.edu; scienceblogs.com)

  17. iPG2P:Multi-scale, Dynamic Solutions Bioinformatics: learn from the data Modeling: simulate the dynamical interactions among components (ghr.nlm.nih.org; wikitextbook.co.uk; generalhorticulture.tamu.edu; scienceblogs.com)

  18. iPG2P:Cyber-infrastructure Challenges Data management. Relating different data. Learning from the data. Good middleware for virtual, integrated, distributed databases: Pipelining of NextGen sequence data into virtual genotype and molecular phenotype databases. 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 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.

  19. iPG2P:Cyber-infrastructure Challenges Data and model visualization. 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.

  20. iPG2P:Cyber-infrastructure Challenges Modeling and model analysis. Modeling framework tools to support the construction, parameter estimation, sensitivity analysis, and utilization of models. Again, the value added is in interoperablility.

  21. Talk Outline • What is Computational Biology? • Current Trends and Challenges • Careers • Q/A http://www.sciencedaily.com/images/2005/07/050730093601.jpg

  22. Manasi 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 Careers in CompBio

  23. 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

  24. 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. • Programadministration: • CS programs with CompBio focus • Joint/Interdisciplinary programs • Programs administered by Biochemistry/Bioengineering depts.

  25. 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

  26. 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

  27. 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!

  28. 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

  29. +/-

  30. 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/

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