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8.592 Statistical Physics in Biology (MIT) 8.592 Statistical Physics in Biology (MIT)

Description

Statistical Physics in Biology is a survey of problems at the interface of statistical physics and modern biology. Topics include: bioinformatic methods for extracting information content of DNA; gene finding, sequence comparison, and phylogenetic trees; physical interactions responsible for structure of biopolymers; DNA double helix, secondary structure of RNA, and elements of protein folding; Considerations of force, motion, and packaging; protein motors, membranes. We also look at collective behavior of biological elements, cellular networks, neural networks, and evolution.Technical RequirementsAny number of biological sequence comparison software tools can be used to import the .fna files found on this course site. Statistical Physics in Biology is a survey of problems at the interface of statistical physics and modern biology. Topics include: bioinformatic methods for extracting information content of DNA; gene finding, sequence comparison, and phylogenetic trees; physical interactions responsible for structure of biopolymers; DNA double helix, secondary structure of RNA, and elements of protein folding; Considerations of force, motion, and packaging; protein motors, membranes. We also look at collective behavior of biological elements, cellular networks, neural networks, and evolution.Technical RequirementsAny number of biological sequence comparison software tools can be used to import the .fna files found on this course site.

Subjects

Bioinformatics | Bioinformatics | DNA | DNA | gene finding | gene finding | sequence comparison | sequence comparison | phylogenetic trees | phylogenetic trees | biopolymers | biopolymers | DNA double helix | DNA double helix | secondary structure of RNA | secondary structure of RNA | protein folding | protein folding | protein motors | membranes | protein motors | membranes | cellular networks | cellular networks | neural networks | neural networks | evolution | evolution | statistical physics | statistical physics | molecular biology | molecular biology | deoxyribonucleic acid | deoxyribonucleic acid | genes | genes | genetics | genetics | gene sequencing | gene sequencing | phylogenetics | phylogenetics | double helix | double helix | RNA | RNA | ribonucleic acid | ribonucleic acid | force | force | motion | motion | packaging | packaging | protein motors | protein motors | membranes | membranes | biochemistry | biochemistry | genome | genome | optimization | optimization | partitioning | partitioning | pattern recognition | pattern recognition | collective behavior | collective behavior

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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7.349 Biological Computing: At the Crossroads of Engineering and Science (MIT) 7.349 Biological Computing: At the Crossroads of Engineering and Science (MIT)

Description

Imagine you are a salesman needing to visit 100 cities connected by a set of roads. Can you do it while stopping in each city only once? Even a supercomputer working at 1 trillion operations per second would take longer than the age of the universe to find a solution when considering each possibility in turn. In 1994, Leonard Adleman published a paper in which he described a solution, using the tools of molecular biology, for a smaller 7-city example of this problem. His paper generated enormous scientific and public interest, and kick-started the field of Biological Computing, the main subject of this discussion based seminar course. Students will analyze the Adleman paper, and the papers that preceded and followed it, with an eye for identifying the engineering and scientific aspects of Imagine you are a salesman needing to visit 100 cities connected by a set of roads. Can you do it while stopping in each city only once? Even a supercomputer working at 1 trillion operations per second would take longer than the age of the universe to find a solution when considering each possibility in turn. In 1994, Leonard Adleman published a paper in which he described a solution, using the tools of molecular biology, for a smaller 7-city example of this problem. His paper generated enormous scientific and public interest, and kick-started the field of Biological Computing, the main subject of this discussion based seminar course. Students will analyze the Adleman paper, and the papers that preceded and followed it, with an eye for identifying the engineering and scientific aspects of

Subjects

biological computing | biological computing | Leonard Adleman | Leonard Adleman | exquisite detection | exquisite detection | whole-cell computing | whole-cell computing | computation | computation | molecular biology | molecular biology | biotin-avidin | biotin-avidin | magnetic beads | magnetic beads | cellular processes | cellular processes | combinatorial problems | combinatorial problems | self-assembly | self-assembly | nanodevices | nanodevices | molecular machines | molecular machines | quorum sensing | quorum sensing | molecular switches | molecular switches | ciliates | ciliates | molecular gates | molecular gates | molecular circuits | molecular circuits | genetic switch | genetic switch | cellular networks | cellular networks | genetic networks | genetic networks | genetic circuits | genetic circuits

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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8.592J Statistical Physics in Biology (MIT) 8.592J Statistical Physics in Biology (MIT)

Description

Statistical Physics in Biology is a survey of problems at the interface of statistical physics and modern biology. Topics include: bioinformatic methods for extracting information content of DNA; gene finding, sequence comparison, and phylogenetic trees; physical interactions responsible for structure of biopolymers; DNA double helix, secondary structure of RNA, and elements of protein folding; considerations of force, motion, and packaging; protein motors, membranes. We also look at collective behavior of biological elements, cellular networks, neural networks, and evolution. Statistical Physics in Biology is a survey of problems at the interface of statistical physics and modern biology. Topics include: bioinformatic methods for extracting information content of DNA; gene finding, sequence comparison, and phylogenetic trees; physical interactions responsible for structure of biopolymers; DNA double helix, secondary structure of RNA, and elements of protein folding; considerations of force, motion, and packaging; protein motors, membranes. We also look at collective behavior of biological elements, cellular networks, neural networks, and evolution.

Subjects

8.592 | 8.592 | HST.452 | HST.452 | Statistical physics | Statistical physics | Bioinformatics | Bioinformatics | DNA | DNA | gene finding | gene finding | sequence comparison | sequence comparison | phylogenetic trees | phylogenetic trees | biopolymers | biopolymers | DNA double helix | DNA double helix | secondary structure of RNA | secondary structure of RNA | protein folding | protein folding | protein motors | protein motors | membranes | membranes | cellular networks | cellular networks | neural networks | neural networks | evolution | 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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8.592J Statistical Physics in Biology (MIT) 8.592J Statistical Physics in Biology (MIT)

Description

Statistical Physics in Biology is a survey of problems at the interface of statistical physics and modern biology. Topics include: bioinformatic methods for extracting information content of DNA; gene finding, sequence comparison, and phylogenetic trees; physical interactions responsible for structure of biopolymers; DNA double helix, secondary structure of RNA, and elements of protein folding; considerations of force, motion, and packaging; protein motors, membranes. We also look at collective behavior of biological elements, cellular networks, neural networks, and evolution. Statistical Physics in Biology is a survey of problems at the interface of statistical physics and modern biology. Topics include: bioinformatic methods for extracting information content of DNA; gene finding, sequence comparison, and phylogenetic trees; physical interactions responsible for structure of biopolymers; DNA double helix, secondary structure of RNA, and elements of protein folding; considerations of force, motion, and packaging; protein motors, membranes. We also look at collective behavior of biological elements, cellular networks, neural networks, and evolution.

Subjects

Bioinformatics | Bioinformatics | DNA | DNA | gene finding | gene finding | sequence comparison | sequence comparison | phylogenetic trees | phylogenetic trees | biopolymers | biopolymers | DNA double helix | DNA double helix | secondary structure of RNA | secondary structure of RNA | protein folding | protein folding | protein motors | protein motors | membranes | membranes | cellular networks | cellular networks | neural networks | neural networks | evolution | 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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8.592J Statistical Physics in Biology (MIT)

Description

Statistical Physics in Biology is a survey of problems at the interface of statistical physics and modern biology. Topics include: bioinformatic methods for extracting information content of DNA; gene finding, sequence comparison, and phylogenetic trees; physical interactions responsible for structure of biopolymers; DNA double helix, secondary structure of RNA, and elements of protein folding; considerations of force, motion, and packaging; protein motors, membranes. We also look at collective behavior of biological elements, cellular networks, neural networks, and evolution.

Subjects

Bioinformatics | DNA | gene finding | sequence comparison | phylogenetic trees | biopolymers | DNA double helix | secondary structure of RNA | protein folding | protein motors | membranes | cellular networks | neural networks | 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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Attribution

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8.592 Statistical Physics in Biology (MIT)

Description

Statistical Physics in Biology is a survey of problems at the interface of statistical physics and modern biology. Topics include: bioinformatic methods for extracting information content of DNA; gene finding, sequence comparison, and phylogenetic trees; physical interactions responsible for structure of biopolymers; DNA double helix, secondary structure of RNA, and elements of protein folding; Considerations of force, motion, and packaging; protein motors, membranes. We also look at collective behavior of biological elements, cellular networks, neural networks, and evolution.Technical RequirementsAny number of biological sequence comparison software tools can be used to import the .fna files found on this course site.

Subjects

Bioinformatics | DNA | gene finding | sequence comparison | phylogenetic trees | biopolymers | DNA double helix | secondary structure of RNA | protein folding | protein motors | membranes | cellular networks | neural networks | evolution | statistical physics | molecular biology | deoxyribonucleic acid | genes | genetics | gene sequencing | phylogenetics | double helix | RNA | ribonucleic acid | force | motion | packaging | protein motors | membranes | biochemistry | genome | optimization | partitioning | pattern recognition | collective behavior

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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7.349 Biological Computing: At the Crossroads of Engineering and Science (MIT)

Description

Imagine you are a salesman needing to visit 100 cities connected by a set of roads. Can you do it while stopping in each city only once? Even a supercomputer working at 1 trillion operations per second would take longer than the age of the universe to find a solution when considering each possibility in turn. In 1994, Leonard Adleman published a paper in which he described a solution, using the tools of molecular biology, for a smaller 7-city example of this problem. His paper generated enormous scientific and public interest, and kick-started the field of Biological Computing, the main subject of this discussion based seminar course. Students will analyze the Adleman paper, and the papers that preceded and followed it, with an eye for identifying the engineering and scientific aspects of

Subjects

biological computing | Leonard Adleman | exquisite detection | whole-cell computing | computation | molecular biology | biotin-avidin | magnetic beads | cellular processes | combinatorial problems | self-assembly | nanodevices | molecular machines | quorum sensing | molecular switches | ciliates | molecular gates | molecular circuits | genetic switch | cellular networks | genetic networks | genetic circuits

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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8.592J Statistical Physics in Biology (MIT)

Description

Statistical Physics in Biology is a survey of problems at the interface of statistical physics and modern biology. Topics include: bioinformatic methods for extracting information content of DNA; gene finding, sequence comparison, and phylogenetic trees; physical interactions responsible for structure of biopolymers; DNA double helix, secondary structure of RNA, and elements of protein folding; considerations of force, motion, and packaging; protein motors, membranes. We also look at collective behavior of biological elements, cellular networks, neural networks, and evolution.

Subjects

8.592 | HST.452 | Statistical physics | Bioinformatics | DNA | gene finding | sequence comparison | phylogenetic trees | biopolymers | DNA double helix | secondary structure of RNA | protein folding | protein motors | membranes | cellular networks | neural networks | 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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