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MIT 6 047 - Computational Biology - Genomes, Networks, Evolution

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6.047 / 6.878 Computational Biology: Genomes, Networks, EvolutionGoals for the termCourse outlineWhy Computational Biology ?Why Computational Biology: Last year’s answersMolecular Biology Primer“Central dogma” of Molecular BiologyDNA: The double helixDNA: the molecule of heredityDNA: chemical detailsDNA: deoxyribose sugarDNA: the four basesDNA: base pairsDNA: sequencesDNA packagingChromosomes inside the cell“Central dogma” of Molecular BiologyGenes control the making of cell partsmRNA: The messengerFrom DNA to RNA: TranscriptionFrom pre-mRNA to mRNA: SplicingRNA can be functionalRNA structure: 2ndary and 3rdarySplicing machinery made of RNA“Central dogma” of Molecular BiologyProteins carry out the cell’s chemistryProtein structureProtein building blocksFrom RNA to protein: TranslationThe Genetic CodeThe Genetic CodeSummary: The Central DogmaCellular dynamics and regulation How cells move through this Central DogmaRegulation of Gene ExpressionRegulatory InteractionsComputational Motif PredictionRegulatory CircuitsRegulatory CircuitsComputational ApproachesMetabolismMetabolic PathwaysComputational Metabolic ModelingSynthetic BiologyRecitation tomorrow! Room/time TBAToday: Regulatory Motif DiscoveryRegulatory motif discoveryMotifs are preferentially conserved across evolutionFraming the problem computationallyComputational approaches for motif discoveryMIT OpenCourseWare http://ocw.mit.edu 6.047 / 6.878 Computational Biology: Genomes, Networks, EvolutionFall 2008 For information about citing these materials or our Terms of Use, visit: http://ocw.mit.edu/terms.6.047 / 6.878Computational Biology: Genomes, Networks, EvolutionManolis KellisJames GalaganGoals 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 settingCourse 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, genomeassembly, metabolic modeling, miRNA, genome evolutionWhy 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 cellular instruction


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