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8.591J Systems Biology (MIT) 8.591J Systems Biology (MIT)

Description

This course introduces the mathematical modeling techniques needed to address key questions in modern biology. An overview of modeling techniques in molecular biology and genetics, cell biology and developmental biology is covered. Key experiments that validate mathematical models are also discussed, as well as molecular, cellular, and developmental systems biology, bacterial chemotaxis, genetic oscillators, control theory and genetic networks, and gradient sensing systems. Additional specific topics include: constructing and modeling of genetic networks, lambda phage as a genetic switch, synthetic genetic switches, circadian rhythms, reaction diffusion equations, local activation and global inhibition models, center finding networks, general pattern formation models, modeling cell-cell co This course introduces the mathematical modeling techniques needed to address key questions in modern biology. An overview of modeling techniques in molecular biology and genetics, cell biology and developmental biology is covered. Key experiments that validate mathematical models are also discussed, as well as molecular, cellular, and developmental systems biology, bacterial chemotaxis, genetic oscillators, control theory and genetic networks, and gradient sensing systems. Additional specific topics include: constructing and modeling of genetic networks, lambda phage as a genetic switch, synthetic genetic switches, circadian rhythms, reaction diffusion equations, local activation and global inhibition models, center finding networks, general pattern formation models, modeling cell-cell co

Subjects

molecular systems biology | molecular systems biology | constructing and modeling of genetic networks | constructing and modeling of genetic networks | control theory and genetic networks | control theory and genetic networks | ambda phage as a genetic switch | ambda phage as a genetic switch | synthetic genetic switches | synthetic genetic switches | bacterial chemotaxis | bacterial chemotaxis | genetic oscillators | genetic oscillators | circadian rhythms | circadian rhythms | cellular systems biology | cellular systems biology | reaction diffusion equations | reaction diffusion equations | local activation and global inhibition models | local activation and global inhibition models | gradient sensing systems | gradient sensing systems | center finding networks | center finding networks | developmental systems biology | developmental systems biology | general pattern formation models | general pattern formation models | modeling cell-cell communication | modeling cell-cell communication | quorum sensing | quorum sensing | models for Drosophilia development | models for Drosophilia development | 8.591 | 8.591 | 7.81 | 7.81

License

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8.591J Systems Biology (MIT) 8.591J Systems Biology (MIT)

Description

Includes audio/video content: AV lectures. This course provides an introduction to cellular and population-level systems biology with an emphasis on synthetic biology, modeling of genetic networks, cell-cell interactions, and evolutionary dynamics. Cellular systems include genetic switches and oscillators, network motifs, genetic network evolution, and cellular decision-making. Population-level systems include models of pattern formation, cell-cell communication, and evolutionary systems biology. Includes audio/video content: AV lectures. This course provides an introduction to cellular and population-level systems biology with an emphasis on synthetic biology, modeling of genetic networks, cell-cell interactions, and evolutionary dynamics. Cellular systems include genetic switches and oscillators, network motifs, genetic network evolution, and cellular decision-making. Population-level systems include models of pattern formation, cell-cell communication, and evolutionary systems biology.

Subjects

molecular systems biology | molecular systems biology | genetic networks | genetic networks | control theory | control theory | synthetic genetic switches | synthetic genetic switches | bacterial chemotaxis | bacterial chemotaxis | genetic oscillators | genetic oscillators | circadian rhythms | circadian rhythms | cellular systems biology | cellular systems biology | reaction diffusion equations | reaction diffusion equations | local activation | local activation | global inhibition models | global inhibition models | gradient sensing systems | gradient sensing systems | center finding networks | center finding networks | general pattern formation models | general pattern formation models | cell-cell communication | cell-cell communication | quorum sensing | quorum sensing

License

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8.591J Systems Biology (MIT)

Description

This course introduces the mathematical modeling techniques needed to address key questions in modern biology. An overview of modeling techniques in molecular biology and genetics, cell biology and developmental biology is covered. Key experiments that validate mathematical models are also discussed, as well as molecular, cellular, and developmental systems biology, bacterial chemotaxis, genetic oscillators, control theory and genetic networks, and gradient sensing systems. Additional specific topics include: constructing and modeling of genetic networks, lambda phage as a genetic switch, synthetic genetic switches, circadian rhythms, reaction diffusion equations, local activation and global inhibition models, center finding networks, general pattern formation models, modeling cell-cell co

Subjects

molecular systems biology | constructing and modeling of genetic networks | control theory and genetic networks | ambda phage as a genetic switch | synthetic genetic switches | bacterial chemotaxis | genetic oscillators | circadian rhythms | cellular systems biology | reaction diffusion equations | local activation and global inhibition models | gradient sensing systems | center finding networks | developmental systems biology | general pattern formation models | modeling cell-cell communication | quorum sensing | models for Drosophilia development | 8.591 | 7.81

License

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20.020 Introduction to Biological Engineering Design (MIT) 20.020 Introduction to Biological Engineering Design (MIT)

Description

Includes audio/video content: AV special element video. This class is a project-based introduction to the engineering of synthetic biological systems. Throughout the term, students develop projects that are responsive to real-world problems of their choosing, and whose solutions depend on biological technologies. Lectures, discussions, and studio exercises will introduce (1) components and control of prokaryotic and eukaryotic behavior, (2) DNA synthesis, standards, and abstraction in biological engineering, and (3) issues of human practice, including biological safety; security; ownership, sharing, and innovation; and ethics. Enrollment preference is given to freshmen. This subject was originally developed and first taught in Spring 2008 by Drew Endy and Natalie Kuldell. Many of Drew's Includes audio/video content: AV special element video. This class is a project-based introduction to the engineering of synthetic biological systems. Throughout the term, students develop projects that are responsive to real-world problems of their choosing, and whose solutions depend on biological technologies. Lectures, discussions, and studio exercises will introduce (1) components and control of prokaryotic and eukaryotic behavior, (2) DNA synthesis, standards, and abstraction in biological engineering, and (3) issues of human practice, including biological safety; security; ownership, sharing, and innovation; and ethics. Enrollment preference is given to freshmen. This subject was originally developed and first taught in Spring 2008 by Drew Endy and Natalie Kuldell. Many of Drew's

Subjects

biology | biology | chemistry | chemistry | synthetic biology | synthetic biology | project | project | biotech | biotech | genetic engineering | genetic engineering | GMO | GMO | ethics | ethics | biomedical ethics | biomedical ethics | genetics | genetics | recombinant DNA | recombinant DNA | DNA | DNA | gene sequencing | gene sequencing | gene synthesis | gene synthesis | biohacking | biohacking | computational biology | computational biology | iGEM | iGEM | BioBrick | BioBrick | systems biology | systems biology

License

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2.72 Elements of Mechanical Design (MIT) 2.72 Elements of Mechanical Design (MIT)

Description

This is an advanced course on modeling, design, integration and best practices for use of machine elements such as bearings, springs, gears, cams and mechanisms. Modeling and analysis of these elements is based upon extensive application of physics, mathematics and core mechanical engineering principles (solid mechanics, fluid mechanics, manufacturing, estimation, computer simulation, etc.). These principles are reinforced via (1) hands-on laboratory experiences wherein students conduct experiments and disassemble machines and (2) a substantial design project wherein students model, design, fabricate and characterize a mechanical system that is relevant to a real world application. Students master the materials via problems sets that are directly related to, and coordinated with, the deliv This is an advanced course on modeling, design, integration and best practices for use of machine elements such as bearings, springs, gears, cams and mechanisms. Modeling and analysis of these elements is based upon extensive application of physics, mathematics and core mechanical engineering principles (solid mechanics, fluid mechanics, manufacturing, estimation, computer simulation, etc.). These principles are reinforced via (1) hands-on laboratory experiences wherein students conduct experiments and disassemble machines and (2) a substantial design project wherein students model, design, fabricate and characterize a mechanical system that is relevant to a real world application. Students master the materials via problems sets that are directly related to, and coordinated with, the deliv

Subjects

biology | biology | chemistry | chemistry | synthetic biology | synthetic biology | project | project | biotech | biotech | genetic engineering | genetic engineering | GMO | GMO | ethics | ethics | biomedical ethics | biomedical ethics | genetics | genetics | recombinant DNA | recombinant DNA | DNA | DNA | gene sequencing | gene sequencing | gene synthesis | gene synthesis | biohacking | biohacking | computational biology | computational biology | iGEM | iGEM | BioBrick | BioBrick | systems biology | systems biology | machine design | machine design | hardware | hardware | machine element | machine element | design process | design process | design layout | design layout | prototype | prototype | mechanism | mechanism | engineering | engineering | fabrication | fabrication | lathe | lathe | precision engineering | precision engineering | group project | group project | project management | project management | CAD | CAD | fatigue | fatigue | Gantt chart | Gantt chart

License

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7.343 Network Medicine: Using Systems Biology and Signaling Networks to Create Novel Cancer Therapeutics (MIT) 7.343 Network Medicine: Using Systems Biology and Signaling Networks to Create Novel Cancer Therapeutics (MIT)

Description

In this course, we will survey the primary systems biology literature, particularly as it pertains to understanding and treating various forms of cancer. We will consider various computational and experimental techniques being used in the field of systems biology, focusing on how systems principles have helped advance biological understanding. We will also discuss the application of the principles of systems biology and network biology to drug development, an emerging discipline called "network medicine." This course is one of many Advanced Undergraduate Seminars offered by the Biology Department at MIT. These seminars are tailored for students with an interest in using primary research literature to discuss and learn about current biological research in a highly interactive sett In this course, we will survey the primary systems biology literature, particularly as it pertains to understanding and treating various forms of cancer. We will consider various computational and experimental techniques being used in the field of systems biology, focusing on how systems principles have helped advance biological understanding. We will also discuss the application of the principles of systems biology and network biology to drug development, an emerging discipline called "network medicine." This course is one of many Advanced Undergraduate Seminars offered by the Biology Department at MIT. These seminars are tailored for students with an interest in using primary research literature to discuss and learn about current biological research in a highly interactive sett

Subjects

systems biology | systems biology | network medicine | network medicine | cancer | cancer | cancer therapeutics | cancer therapeutics | quantitative high-throughput data acquisition | quantitative high-throughput data acquisition | genomic analysis | genomic analysis | signaling network biology | signaling network biology | statistical/computational modeling | statistical/computational modeling | network biology | network biology | drug development | drug development

License

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7.342 Systems and Synthetic Biology: How the Cell Solves Problems (MIT) 7.342 Systems and Synthetic Biology: How the Cell Solves Problems (MIT)

Description

A millennial challenge in biology is to decipher how vast arrays of molecular interactions inside the cell work in concert to produce a cellular function. Systems biology, a new interdisciplinary field of science, brings together biologists and physicists to tackle this grand challenge through quantitative experiments and models. In this course, we will discuss the unifying principles that all organisms use to perform cellular functions. We will also discuss key challenges faced by a cell in both single and multi-cellular organisms. Finally, we will discuss how researchers in the field of synthetic biology are using the new knowledge gained from studying naturally-occurring biological systems to create artificial gene networks capable of performing new functions. This course is one of many A millennial challenge in biology is to decipher how vast arrays of molecular interactions inside the cell work in concert to produce a cellular function. Systems biology, a new interdisciplinary field of science, brings together biologists and physicists to tackle this grand challenge through quantitative experiments and models. In this course, we will discuss the unifying principles that all organisms use to perform cellular functions. We will also discuss key challenges faced by a cell in both single and multi-cellular organisms. Finally, we will discuss how researchers in the field of synthetic biology are using the new knowledge gained from studying naturally-occurring biological systems to create artificial gene networks capable of performing new functions. This course is one of many

Subjects

systems biology | systems biology | synthetic biology | synthetic biology | cell | cell | cellular functions | cellular functions | biological systems | biological systems | artificial gene networks | artificial gene networks | molecular interactions | molecular interactions | molecular biology | molecular biology | genes | genes | RNA | RNA | proteins | proteins | macromolecules | macromolecules | intracellular biochemical interactions | intracellular biochemical interactions | extracellular molecules | extracellular molecules | gene expression | gene expression | stochastic gene expression | stochastic gene expression

License

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7.342 Systems Biology: Stochastic Processes and Biological Robustness (MIT) 7.342 Systems Biology: Stochastic Processes and Biological Robustness (MIT)

Description

In this seminar, we will discuss some of the main themes that have arisen in the field of systems biology, including the concepts of robustness, stochastic cell-to-cell variability, and the evolution of molecular interactions within complex networks. This course is one of many Advanced Undergraduate Seminars offered by the Biology Department at MIT. These seminars are tailored for students with an interest in using primary research literature to discuss and learn about current biological research in a highly interactive setting. Many instructors of the Advanced Undergraduate Seminars are postdoctoral scientists with a strong interest in teaching. In this seminar, we will discuss some of the main themes that have arisen in the field of systems biology, including the concepts of robustness, stochastic cell-to-cell variability, and the evolution of molecular interactions within complex networks. This course is one of many Advanced Undergraduate Seminars offered by the Biology Department at MIT. These seminars are tailored for students with an interest in using primary research literature to discuss and learn about current biological research in a highly interactive setting. Many instructors of the Advanced Undergraduate Seminars are postdoctoral scientists with a strong interest in teaching.

Subjects

systems biology | systems biology | synthetic networks | synthetic networks | noise | noise | gene expression | gene expression | oscillators | oscillators | PCR | PCR | stochastic | stochastic | robustness | robustness | biological networks | biological networks | chemotaxis | chemotaxis | circadian | circadian

License

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7.90J Computational Functional Genomics (MIT) 7.90J Computational Functional Genomics (MIT)

Description

The course focuses on casting contemporary problems in systems biology and functional genomics in computational terms and providing appropriate tools and methods to solve them. Topics include genome structure and function, transcriptional regulation, and stem cell biology in particular; measurement technologies such as microarrays (expression, protein-DNA interactions, chromatin structure); statistical data analysis, predictive and causal inference, and experiment design. The emphasis is on coupling problem structures (biological questions) with appropriate computational approaches. The course focuses on casting contemporary problems in systems biology and functional genomics in computational terms and providing appropriate tools and methods to solve them. Topics include genome structure and function, transcriptional regulation, and stem cell biology in particular; measurement technologies such as microarrays (expression, protein-DNA interactions, chromatin structure); statistical data analysis, predictive and causal inference, and experiment design. The emphasis is on coupling problem structures (biological questions) with appropriate computational approaches.

Subjects

systems biology | systems biology | genome structure | genome structure | DNA | DNA | RNA | RNA | transcription | transcription | stem cell | stem cell | biology | biology | microarray | microarray | gene expression | gene expression | statistical data analysis | statistical data analysis | chromatin | chromatin | gene sequence | gene sequence | genomic sequence | genomic sequence | motif | motif | protein | protein | error model | error model | diagnostic | diagnostic | gene clustering | gene clustering | phenotype | phenotype | clustering | clustering | proteome | proteome | 7.90 | 7.90 | 6.874 | 6.874

License

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20.482J Foundations of Algorithms and Computational Techniques in Systems Biology (MIT) 20.482J Foundations of Algorithms and Computational Techniques in Systems Biology (MIT)

Description

This subject describes and illustrates computational approaches to solving problems in systems biology. A series of case-studies will be explored that demonstrate how an effective match between the statement of a biological problem and the selection of an appropriate algorithm or computational technique can lead to fundamental advances. The subject will cover several discrete and numerical algorithms used in simulation, feature extraction, and optimization for molecular, network, and systems models in biology. This subject describes and illustrates computational approaches to solving problems in systems biology. A series of case-studies will be explored that demonstrate how an effective match between the statement of a biological problem and the selection of an appropriate algorithm or computational technique can lead to fundamental advances. The subject will cover several discrete and numerical algorithms used in simulation, feature extraction, and optimization for molecular, network, and systems models in biology.

Subjects

systems biology | systems biology | algorithms | algorithms | computational techniques | computational techniques | protein modeling | protein modeling | discrete conformational search | discrete conformational search | molecular dynamics | molecular dynamics | electrostatics | electrostatics | network models | network models | deconvolution | deconvolution | nonlinear dynamics | nonlinear dynamics | 20.482 | 20.482 | 6.581 | 6.581

License

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HST.508 Quantitative Genomics (MIT) HST.508 Quantitative Genomics (MIT)

Description

This course provides a foundation in the following four areas: evolutionary and population genetics; comparative genomics; structural genomics and proteomics; and functional genomics and regulation. This course provides a foundation in the following four areas: evolutionary and population genetics; comparative genomics; structural genomics and proteomics; and functional genomics and regulation.

Subjects

genomics | genomics | quantitative genomics | quantitative genomics | comparative genomics | comparative genomics | genes | genes | genome | genome | SNPs | SNPs | haplotypes | haplotypes | sequence alignment | sequence alignment | protein structure | protein structure | protein folding | protein folding | proteomics | proteomics | structural genomics | structural genomics | functional genomics | functional genomics | networks | networks | systems biology | systems biology | biological networks | biological networks | RNA | RNA | DNA | DNA | gene expression | gene expression | evolutionary genetics | evolutionary genetics | population genetics | population genetics

License

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HST.510 Genomics, Computing, Economics, and Society (MIT) HST.510 Genomics, Computing, Economics, and Society (MIT)

Description

This course will focus on understanding aspects of modern technology displaying exponential growth curves and the impact on global quality of life through a weekly updated class project integrating knowledge and providing practical tools for political and business decision-making concerning new aspects of bioengineering, personalized medicine, genetically modified organisms, and stem cells. Interplays of economic, ethical, ecological, and biophysical modeling will be explored through multi-disciplinary teams of students, and individual brief reports. This course will focus on understanding aspects of modern technology displaying exponential growth curves and the impact on global quality of life through a weekly updated class project integrating knowledge and providing practical tools for political and business decision-making concerning new aspects of bioengineering, personalized medicine, genetically modified organisms, and stem cells. Interplays of economic, ethical, ecological, and biophysical modeling will be explored through multi-disciplinary teams of students, and individual brief reports.

Subjects

genomics | genomics | bioengineering | bioengineering | biological engineering | biological engineering | personalized medicine | personalized medicine | informatics | informatics | bioinformatics | bioinformatics | human genome | human genome | stem cells | stem cells | genetically modified organisms | genetically modified organisms | biophysics | biophysics | bioethics | bioethics | society | society | bioeconomics | bioeconomics | statistics | statistics | modeling | modeling | datamining | datamining | systems biology | systems biology | technology development | technology development | biotechnology | biotechnology | public policy | public policy | health policy | health policy | business | business | economics | economics

License

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7.91J Foundations of Computational and Systems Biology (MIT) 7.91J Foundations of Computational and Systems Biology (MIT)

Description

This course is an introduction to computational biology emphasizing the fundamentals of nucleic acid and protein sequence and structural analysis; it also includes an introduction to the analysis of complex biological systems. Topics covered in the course include principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction and network modeling, as well as currently emerging research areas. This course is an introduction to computational biology emphasizing the fundamentals of nucleic acid and protein sequence and structural analysis; it also includes an introduction to the analysis of complex biological systems. Topics covered in the course include principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction and network modeling, as well as currently emerging research areas.

Subjects

7.91 | 7.91 | 20.490 | 20.490 | 20.390 | 20.390 | 7.36 | 7.36 | 6.802 | 6.802 | 6.874 | 6.874 | HST.506 | HST.506 | computational biology | computational biology | systems biology | systems biology | bioinformatics | bioinformatics | artificial intelligence | artificial intelligence | sequence analysis | sequence analysis | proteomics | proteomics | sequence alignment | sequence alignment | protein folding | protein folding | structure prediction | structure prediction | network modeling | network modeling | phylogenetics | phylogenetics | pairwise sequence comparisons | pairwise sequence comparisons | ncbi | ncbi | blast | blast | protein structure | protein structure | dynamic programming | dynamic programming | genome sequencing | genome sequencing | DNA | DNA | RNA | RNA | x-ray crystallography | x-ray crystallography | NMR | NMR | homologs | homologs | ab initio structure prediction | ab initio structure prediction | DNA microarrays | DNA microarrays | clustering | clustering | proteome | proteome | computational annotation | computational annotation

License

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7.91J Foundations of Computational and Systems Biology (MIT) 7.91J Foundations of Computational and Systems Biology (MIT)

Description

Serving as an introduction to computational biology, this course emphasizes the fundamentals of nucleic acid and protein sequence analysis, structural analysis, and the analysis of complex biological systems. The principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction, and network modeling are covered. Students are also exposed to currently emerging research areas in the fields of computational and systems biology. Serving as an introduction to computational biology, this course emphasizes the fundamentals of nucleic acid and protein sequence analysis, structural analysis, and the analysis of complex biological systems. The principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction, and network modeling are covered. Students are also exposed to currently emerging research areas in the fields of computational and systems biology.

Subjects

computational biology | computational biology | systems biology | systems biology | bioinformatics | bioinformatics | sequence analysis | sequence analysis | proteomics | proteomics | sequence alignment | sequence alignment | protein folding | protein folding | structure prediction | structure prediction | network modeling | network modeling | phylogenetics | phylogenetics | pairwise sequence comparisons | pairwise sequence comparisons | ncbi | ncbi | blast | blast | protein structure | protein structure | dynamic programming | dynamic programming | genome sequencing | genome sequencing | DNA | DNA | RNA | RNA | x-ray crystallography | x-ray crystallography | NMR | NMR | homologs | homologs | ab initio structure prediction | ab initio structure prediction | DNA microarrays | DNA microarrays | clustering | clustering | proteome | proteome | computational annotation | computational annotation | BE.490J | BE.490J | 7.91 | 7.91 | 7.36 | 7.36 | BE.490 | BE.490 | 20.490 | 20.490

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2.18 Biomolecular Feedback Systems (MIT) 2.18 Biomolecular Feedback Systems (MIT)

Description

This course focuses on feedback control mechanisms that living organisms implement at the molecular level to execute their functions, with emphasis on techniques to design novel systems with prescribed behaviors. Students will learn how biological functions can be understood and designed using notions from feedback control. This course focuses on feedback control mechanisms that living organisms implement at the molecular level to execute their functions, with emphasis on techniques to design novel systems with prescribed behaviors. Students will learn how biological functions can be understood and designed using notions from feedback control.

Subjects

biomolecular feedback systems | biomolecular feedback systems | systems biology | systems biology | modeling | modeling | feedback | feedback | cell | cell | system | system | control | control | dynamical | dynamical | input/output | input/output | synthetic biology | synthetic biology | techniques | techniques | transcription | transcription | translation | translation | transcriptional regulation | transcriptional regulation | post-transcriptional regulation | post-transcriptional regulation | cellular subsystems | cellular subsystems | dynamic behavior | dynamic behavior | analysis | analysis | equilibrium | equilibrium | robustness | robustness | oscillatory behavior | oscillatory behavior | bifurcations | bifurcations | model reduction | model reduction | stochastic | stochastic | biochemical | biochemical | simulation | simulation | linear | linear | circuit | circuit | design | design | biological circuit design | biological circuit design | negative autoregulation | negative autoregulation | toggle switch | toggle switch | repressilator | repressilator | activator-repressor clock | activator-repressor clock | IFFL | IFFL | incoherent feedforward loop | incoherent feedforward loop | bacterial chemotaxis | bacterial chemotaxis | interconnecting components | interconnecting components | modularity | modularity | retroactivity | retroactivity | gene circuit | gene circuit

License

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8.591J Systems Biology (MIT)

Description

This course provides an introduction to cellular and population-level systems biology with an emphasis on synthetic biology, modeling of genetic networks, cell-cell interactions, and evolutionary dynamics. Cellular systems include genetic switches and oscillators, network motifs, genetic network evolution, and cellular decision-making. Population-level systems include models of pattern formation, cell-cell communication, and evolutionary systems biology.

Subjects

molecular systems biology | genetic networks | control theory | synthetic genetic switches | bacterial chemotaxis | genetic oscillators | circadian rhythms | cellular systems biology | reaction diffusion equations | local activation | global inhibition models | gradient sensing systems | center finding networks | general pattern formation models | cell-cell communication | quorum sensing

License

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7.91J Foundations of Computational and Systems Biology (MIT)

Description

Serving as an introduction to computational biology, this course emphasizes the fundamentals of nucleic acid and protein sequence analysis, structural analysis, and the analysis of complex biological systems. The principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction, and network modeling are covered. Students are also exposed to currently emerging research areas in the fields of computational and systems biology.

Subjects

computational biology | systems biology | bioinformatics | sequence analysis | proteomics | sequence alignment | protein folding | structure prediction | network modeling | phylogenetics | pairwise sequence comparisons | ncbi | blast | protein structure | dynamic programming | genome sequencing | DNA | RNA | x-ray crystallography | NMR | homologs | ab initio structure prediction | DNA microarrays | clustering | proteome | computational annotation | BE.490J | 7.91 | 7.36 | BE.490 | 20.490

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20.482J Foundations of Algorithms and Computational Techniques in Systems Biology (MIT)

Description

This subject describes and illustrates computational approaches to solving problems in systems biology. A series of case-studies will be explored that demonstrate how an effective match between the statement of a biological problem and the selection of an appropriate algorithm or computational technique can lead to fundamental advances. The subject will cover several discrete and numerical algorithms used in simulation, feature extraction, and optimization for molecular, network, and systems models in biology.

Subjects

systems biology | algorithms | computational techniques | protein modeling | discrete conformational search | molecular dynamics | electrostatics | network models | deconvolution | nonlinear dynamics | 20.482 | 6.581

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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7.91J Foundations of Computational and Systems Biology (MIT)

Description

Serving as an introduction to computational biology, this course emphasizes the fundamentals of nucleic acid and protein sequence analysis, structural analysis, and the analysis of complex biological systems. The principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction, and network modeling are covered. Students are also exposed to currently emerging research areas in the fields of computational and systems biology.

Subjects

computational biology | systems biology | bioinformatics | sequence analysis | proteomics | sequence alignment | protein folding | structure prediction | network modeling | phylogenetics | pairwise sequence comparisons | ncbi | blast | protein structure | dynamic programming | genome sequencing | DNA | RNA | x-ray crystallography | NMR | homologs | ab initio structure prediction | DNA microarrays | clustering | proteome | computational annotation | BE.490J | 7.91 | 7.36 | BE.490 | 20.490

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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Curated collection of Bioinformatics resources

Description

This is an evaluated collection of links to resources for learning and teaching subjects relating to Bioinformatics. This forms part of the UK Centre for Bioscience OeRBITAL project.

Subjects

ukoer | bioinformatics | oerbital | protein structure analysis | computational and systems biology | Biological sciences | C000

License

Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales Attribution-Noncommercial-Share Alike 2.0 UK: England & Wales http://creativecommons.org/licenses/by-nc-sa/2.0/uk/ http://creativecommons.org/licenses/by-nc-sa/2.0/uk/

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7.91J Foundations of Computational and Systems Biology (MIT)

Description

Serving as an introduction to computational biology, this course emphasizes the fundamentals of nucleic acid and protein sequence analysis, structural analysis, and the analysis of complex biological systems. The principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction, and network modeling are covered. Students are also exposed to currently emerging research areas in the fields of computational and systems biology.

Subjects

computational biology | systems biology | bioinformatics | sequence analysis | proteomics | sequence alignment | protein folding | structure prediction | network modeling | phylogenetics | pairwise sequence comparisons | ncbi | blast | protein structure | dynamic programming | genome sequencing | DNA | RNA | x-ray crystallography | NMR | homologs | ab initio structure prediction | DNA microarrays | clustering | proteome | computational annotation | BE.490J | 7.91 | 7.36 | BE.490 | 20.490

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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2.18 Biomolecular Feedback Systems (MIT)

Description

This course focuses on feedback control mechanisms that living organisms implement at the molecular level to execute their functions, with emphasis on techniques to design novel systems with prescribed behaviors. Students will learn how biological functions can be understood and designed using notions from feedback control.

Subjects

biomolecular feedback systems | systems biology | modeling | feedback | cell | system | control | dynamical | input/output | synthetic biology | techniques | transcription | translation | transcriptional regulation | post-transcriptional regulation | cellular subsystems | dynamic behavior | analysis | equilibrium | robustness | oscillatory behavior | bifurcations | model reduction | stochastic | biochemical | simulation | linear | circuit | design | biological circuit design | negative autoregulation | toggle switch | repressilator | activator-repressor clock | IFFL | incoherent feedforward loop | bacterial chemotaxis | interconnecting components | modularity | retroactivity | gene circuit

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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2.72 Elements of Mechanical Design (MIT)

Description

This is an advanced course on modeling, design, integration and best practices for use of machine elements such as bearings, springs, gears, cams and mechanisms. Modeling and analysis of these elements is based upon extensive application of physics, mathematics and core mechanical engineering principles (solid mechanics, fluid mechanics, manufacturing, estimation, computer simulation, etc.). These principles are reinforced via (1) hands-on laboratory experiences wherein students conduct experiments and disassemble machines and (2) a substantial design project wherein students model, design, fabricate and characterize a mechanical system that is relevant to a real world application. Students master the materials via problems sets that are directly related to, and coordinated with, the deliv

Subjects

biology | chemistry | synthetic biology | project | biotech | genetic engineering | GMO | ethics | biomedical ethics | genetics | recombinant DNA | DNA | gene sequencing | gene synthesis | biohacking | computational biology | iGEM | BioBrick | systems biology | machine design | hardware | machine element | design process | design layout | prototype | mechanism | engineering | fabrication | lathe | precision engineering | group project | project management | CAD | fatigue | Gantt chart

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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7.91J Foundations of Computational and Systems Biology (MIT)

Description

This course is an introduction to computational biology emphasizing the fundamentals of nucleic acid and protein sequence and structural analysis; it also includes an introduction to the analysis of complex biological systems. Topics covered in the course include principles and methods used for sequence alignment, motif finding, structural modeling, structure prediction and network modeling, as well as currently emerging research areas.

Subjects

7.91 | 20.490 | 20.390 | 7.36 | 6.802 | 6.874 | HST.506 | computational biology | systems biology | bioinformatics | artificial intelligence | sequence analysis | proteomics | sequence alignment | protein folding | structure prediction | network modeling | phylogenetics | pairwise sequence comparisons | ncbi | blast | protein structure | dynamic programming | genome sequencing | DNA | RNA | x-ray crystallography | NMR | homologs | ab initio structure prediction | DNA microarrays | clustering | proteome | computational annotation

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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7.343 Network Medicine: Using Systems Biology and Signaling Networks to Create Novel Cancer Therapeutics (MIT)

Description

In this course, we will survey the primary systems biology literature, particularly as it pertains to understanding and treating various forms of cancer. We will consider various computational and experimental techniques being used in the field of systems biology, focusing on how systems principles have helped advance biological understanding. We will also discuss the application of the principles of systems biology and network biology to drug development, an emerging discipline called "network medicine." This course is one of many Advanced Undergraduate Seminars offered by the Biology Department at MIT. These seminars are tailored for students with an interest in using primary research literature to discuss and learn about current biological research in a highly interactive sett

Subjects

systems biology | network medicine | cancer | cancer therapeutics | quantitative high-throughput data acquisition | genomic analysis | signaling network biology | statistical/computational modeling | network biology | drug development

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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