MIT 6 047 - Computational Biology - Genomes, Networks, Evolution (56 pages)

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Computational Biology - Genomes, Networks, Evolution



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Computational Biology - Genomes, Networks, Evolution

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Lecture Notes


Pages:
56
School:
Massachusetts Institute of Technology
Course:
6 047 - Computational Biology

Unformatted text preview:

MIT OpenCourseWare http ocw mit edu 6 047 6 878 Computational Biology Genomes Networks Evolution Fall 2008 For information about citing these materials or our Terms of Use visit http ocw mit edu terms 6 047 6 878 Computational Biology Genomes Networks Evolution Manolis Kellis James Galagan Goals for the term Introduction to computational biology Fundamental problems in computational biology Algorithmic machine learning techniques for data analysis Research directions for active participation in the field Ability to tackle research Problem set questions algorithmic rigorous thinking Programming assignments hands on experience w real datasets Final project Research initiative to propose an innovative project Ability to carry out project s goals produce deliverables Write up goals approach and findings in conference format Present your project to your peers in conference setting Course outline Organization Duality Computation and Biology Important biological problems Fundamental computational techniques Foundations and Frontiers First half well defined problems and general methodologies Second half in depth look at complex problems combine techniques learned opens to projects research directions Topics covered First half the foundations String matching genome analysis expression clustering classification regulatory motifs biological networks evolutionary theory populations Second half the frontiers Comparative genomics Bayesian networks systems biology genome assembly metabolic modeling miRNA genome evolution Why Computational Biology Why Computational Biology Last year s answers Lots of data lots of data There are rules Pattern finding It s all about data Ability to visualize Simulations Guess verify generate hypotheses for testing Propose mechanisms theory to explain observations Networks combinations of variables Efficiency reduce experimental space to cover Informatics infrastructure ability to combine datasets Correlations Life itself is digital Understand



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