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6.895 Computational Biology: Genomes, Networks, Evolution (MIT) 6.895 Computational Biology: Genomes, Networks, Evolution (MIT)

Description

This course focuses on the algorithmic and machine learning foundations of computational biology, combining theory with practice. We study the principles of algorithm design for biological datasets, and analyze influential problems and techniques. We use these to analyze real datasets from large-scale studies in genomics and proteomics. The topics covered include:Genomes: Biological Sequence Analysis, Hidden Markov Models, Gene Finding, RNA Folding, Sequence Alignment, Genome Assembly.Networks: Gene Expression Analysis, Regulatory Motifs, Graph Algorithms, Scale-free Networks, Network Motifs, Network Evolution.Evolution: Comparative Genomics, Phylogenetics, Genome Duplication, Genome Rearrangements, Evolutionary Theory, Rapid Evolution. This course focuses on the algorithmic and machine learning foundations of computational biology, combining theory with practice. We study the principles of algorithm design for biological datasets, and analyze influential problems and techniques. We use these to analyze real datasets from large-scale studies in genomics and proteomics. The topics covered include:Genomes: Biological Sequence Analysis, Hidden Markov Models, Gene Finding, RNA Folding, Sequence Alignment, Genome Assembly.Networks: Gene Expression Analysis, Regulatory Motifs, Graph Algorithms, Scale-free Networks, Network Motifs, Network Evolution.Evolution: Comparative Genomics, Phylogenetics, Genome Duplication, Genome Rearrangements, Evolutionary Theory, Rapid Evolution.

Subjects

Genomes: Biological sequence analysis | Genomes: Biological sequence analysis | hidden Markov models | hidden Markov models | gene finding | gene finding | RNA folding | RNA folding | sequence alignment | sequence alignment | genome assembly | genome assembly | Networks: Gene expression analysis | Networks: Gene expression analysis | regulatory motifs | regulatory motifs | graph algorithms | graph algorithms | scale-free networks | scale-free networks | network motifs | network motifs | network evolution | network evolution | Evolution: Comparative genomics | Evolution: Comparative genomics | phylogenetics | phylogenetics | genome duplication | genome duplication | genome rearrangements | genome rearrangements | evolutionary theory | evolutionary theory | rapid evolution | rapid evolution

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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6.877J Computational Evolutionary Biology (MIT) 6.877J Computational Evolutionary Biology (MIT)

Description

Why has it been easier to develop a vaccine to eliminate polio than to control influenza or AIDS? Has there been natural selection for a 'language gene'? Why are there no animals with wheels? When does 'maximizing fitness' lead to evolutionary extinction? How are sex and parasites related? Why don't snakes eat grass? Why don't we have eyes in the back of our heads? How does modern genomics illustrate and challenge the field? This course analyzes evolution from a computational, modeling, and engineering perspective. The course has extensive hands-on laboratory exercises in model-building and analyzing evolutionary data. Why has it been easier to develop a vaccine to eliminate polio than to control influenza or AIDS? Has there been natural selection for a 'language gene'? Why are there no animals with wheels? When does 'maximizing fitness' lead to evolutionary extinction? How are sex and parasites related? Why don't snakes eat grass? Why don't we have eyes in the back of our heads? How does modern genomics illustrate and challenge the field? This course analyzes evolution from a computational, modeling, and engineering perspective. The course has extensive hands-on laboratory exercises in model-building and analyzing evolutionary data.

Subjects

6.877 | 6.877 | HST.949 | HST.949 | computational approaches | computational approaches | evolutionary biology | evolutionary biology | evolutionary theory and inferential logic of evolution by natural selection | evolutionary theory and inferential logic of evolution by natural selection | computational and algorithmic implications and requirements of evolutionary models | computational and algorithmic implications and requirements of evolutionary models | whole-genome species comparison | whole-genome species comparison | phylogenetic tree construction | phylogenetic tree construction | molecular evolution | molecular evolution | homology and development | homology and development | optimization and evolvability | optimization and evolvability | heritability | heritability | disease evolution | disease evolution | detecting selection in human populations | and evolution of language | detecting selection in human populations | and evolution of language | extensive laboratory exercises in model-building and analyzing evolutionary data | extensive laboratory exercises in model-building and analyzing evolutionary data

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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6.096 Algorithms for Computational Biology (MIT) 6.096 Algorithms for Computational Biology (MIT)

Description

This course is offered to undergraduates and addresses several algorithmic challenges in computational biology. The principles of algorithmic design for biological datasets are studied and existing algorithms analyzed for application to real datasets. Topics covered include: biological sequence analysis, gene identification, regulatory motif discovery, genome assembly, genome duplication and rearrangements, evolutionary theory, clustering algorithms, and scale-free networks. This course is offered to undergraduates and addresses several algorithmic challenges in computational biology. The principles of algorithmic design for biological datasets are studied and existing algorithms analyzed for application to real datasets. Topics covered include: biological sequence analysis, gene identification, regulatory motif discovery, genome assembly, genome duplication and rearrangements, evolutionary theory, clustering algorithms, and scale-free networks.

Subjects

biological sequence analysis | biological sequence analysis | gene finding | gene finding | motif discovery | motif discovery | RNA folding | RNA folding | global and local sequence alignment | global and local sequence alignment | genome assembly | genome assembly | comparative genomics | comparative genomics | genome duplication | genome duplication | genome rearrangements | genome rearrangements | evolutionary theory | evolutionary theory | gene expression | gene expression | clustering algorithms | clustering algorithms | scale-free networks | scale-free networks | machine learning applications | machine learning applications

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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21L.705 Major Authors: America's Literary Scientists (MIT) 21L.705 Major Authors: America's Literary Scientists (MIT)

Description

Global exploration in the eighteenth and nineteenth centuries radically changed Western science, orienting philosophies of natural history to more focused fields like comparative anatomy, botany, and geology. In the United States, European scientific advances and home-grown ventures like the Wilkes Exploring Expedition to Antarctica and the Pacific inspired new endeavors in cartography, ethnography, zoology, and evolutionary theory, replacing rigid models of thought and classification with more fluid and active systems. They inspired literary authors as well. This class will examine some of the most remarkable of these authors—Herman Melville (Moby-Dick and "The Encantadas"), Henry David Thoreau (Walden), Sarah Orne Jewett (Country of the Pointed Firs), Edith Wharton (House Global exploration in the eighteenth and nineteenth centuries radically changed Western science, orienting philosophies of natural history to more focused fields like comparative anatomy, botany, and geology. In the United States, European scientific advances and home-grown ventures like the Wilkes Exploring Expedition to Antarctica and the Pacific inspired new endeavors in cartography, ethnography, zoology, and evolutionary theory, replacing rigid models of thought and classification with more fluid and active systems. They inspired literary authors as well. This class will examine some of the most remarkable of these authors—Herman Melville (Moby-Dick and "The Encantadas"), Henry David Thoreau (Walden), Sarah Orne Jewett (Country of the Pointed Firs), Edith Wharton (House

Subjects

America's literary scientists | America's literary scientists | global exploration in the eighteenth and nineteenth centuries | global exploration in the eighteenth and nineteenth centuries | Wilkes exploring expedition to Antarctica and the Pacific | Wilkes exploring expedition to Antarctica and the Pacific | cartography | cartography | ethnography | ethnography | zoology | zoology | evolutionary theory | evolutionary theory | Herman Melville | Herman Melville | Henry David Thoreau | Henry David Thoreau | Sarah Orne Jewett | Sarah Orne Jewett | Toni Morrison | Toni Morrison

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see http://ocw.mit.edu/terms/index.htm

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6.895 Computational Biology: Genomes, Networks, Evolution (MIT)

Description

This course focuses on the algorithmic and machine learning foundations of computational biology, combining theory with practice. We study the principles of algorithm design for biological datasets, and analyze influential problems and techniques. We use these to analyze real datasets from large-scale studies in genomics and proteomics. The topics covered include:Genomes: Biological Sequence Analysis, Hidden Markov Models, Gene Finding, RNA Folding, Sequence Alignment, Genome Assembly.Networks: Gene Expression Analysis, Regulatory Motifs, Graph Algorithms, Scale-free Networks, Network Motifs, Network Evolution.Evolution: Comparative Genomics, Phylogenetics, Genome Duplication, Genome Rearrangements, Evolutionary Theory, Rapid Evolution.

Subjects

Genomes: Biological sequence analysis | hidden Markov models | gene finding | RNA folding | sequence alignment | genome assembly | Networks: Gene expression analysis | regulatory motifs | graph algorithms | scale-free networks | network motifs | network evolution | Evolution: Comparative genomics | phylogenetics | genome duplication | genome rearrangements | evolutionary theory | rapid evolution

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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21L.705 Major Authors: America's Literary Scientists (MIT)

Description

Global exploration in the eighteenth and nineteenth centuries radically changed Western science, orienting philosophies of natural history to more focused fields like comparative anatomy, botany, and geology. In the United States, European scientific advances and home-grown ventures like the Wilkes Exploring Expedition to Antarctica and the Pacific inspired new endeavors in cartography, ethnography, zoology, and evolutionary theory, replacing rigid models of thought and classification with more fluid and active systems. They inspired literary authors as well. This class will examine some of the most remarkable of these authors—Herman Melville (Moby-Dick and "The Encantadas"), Henry David Thoreau (Walden), Sarah Orne Jewett (Country of the Pointed Firs), Edith Wharton (House

Subjects

America's literary scientists | global exploration in the eighteenth and nineteenth centuries | Wilkes exploring expedition to Antarctica and the Pacific | cartography | ethnography | zoology | evolutionary theory | Herman Melville | Henry David Thoreau | Sarah Orne Jewett | Toni Morrison

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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6.877J Computational Evolutionary Biology (MIT)

Description

Why has it been easier to develop a vaccine to eliminate polio than to control influenza or AIDS? Has there been natural selection for a 'language gene'? Why are there no animals with wheels? When does 'maximizing fitness' lead to evolutionary extinction? How are sex and parasites related? Why don't snakes eat grass? Why don't we have eyes in the back of our heads? How does modern genomics illustrate and challenge the field? This course analyzes evolution from a computational, modeling, and engineering perspective. The course has extensive hands-on laboratory exercises in model-building and analyzing evolutionary data.

Subjects

6.877 | HST.949 | computational approaches | evolutionary biology | evolutionary theory and inferential logic of evolution by natural selection | computational and algorithmic implications and requirements of evolutionary models | whole-genome species comparison | phylogenetic tree construction | molecular evolution | homology and development | optimization and evolvability | heritability | disease evolution | detecting selection in human populations | and evolution of language | extensive laboratory exercises in model-building and analyzing evolutionary data

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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6.096 Algorithms for Computational Biology (MIT)

Description

This course is offered to undergraduates and addresses several algorithmic challenges in computational biology. The principles of algorithmic design for biological datasets are studied and existing algorithms analyzed for application to real datasets. Topics covered include: biological sequence analysis, gene identification, regulatory motif discovery, genome assembly, genome duplication and rearrangements, evolutionary theory, clustering algorithms, and scale-free networks.

Subjects

biological sequence analysis | gene finding | motif discovery | RNA folding | global and local sequence alignment | genome assembly | comparative genomics | genome duplication | genome rearrangements | evolutionary theory | gene expression | clustering algorithms | scale-free networks | machine learning applications

License

Content within individual OCW courses is (c) by the individual authors unless otherwise noted. MIT OpenCourseWare materials are licensed by the Massachusetts Institute of Technology under a Creative Commons License (Attribution-NonCommercial-ShareAlike). For further information see https://ocw.mit.edu/terms/index.htm

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