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Uedu Open / Genomics and Computational Biology
HST.508

Genomics and Computational Biology

Dr. George Church | Fall 2002
Science & Math Biology Engineering Biological Engineering Health & Medicine Biomedical Technologies Computation and Systems Biology Computational Biology
前往原始課程
CC BY-NC-SA 4.0
課程簡介
This course will assess the relationships among sequence, structure, and function in complex biological networks as well as progress in realistic modeling of quantitative, comprehensive, functional genomics analyses. Exercises will include algorithmic, statistical, database, and simulation approaches and practical applications to medicine, biotechnology, drug discovery, and genetic engineering. Future opportunities and current limitations will be critically addressed. In addition to the regular lecture sessions, supplementary sections are scheduled to address issues related to Perl, Mathematica and biology.
課程資訊
來源MIT 開放式課程
科系Health Sciences and Technology
語言English
影片數21
課程影片 (21)
1
1A. Intro 1: Computational Side of Computational Biology. Statistics; Perl, Mathematica
1A. Intro 1: Computational Side of Computational Biology. Statistics; Perl, Mathematica
2
1B. Intro 1: Computational Side of Computational Biology. Statistics; Perl, Mathematica
1B. Intro 1: Computational Side of Computational Biology. Statistics; Perl, Mathematica
3
2A. Intro 2: Biological Side of Computational Biology. Comparative Genomics, Models & A...
2A. Intro 2: Biological Side of Computational Biology. Comparative Genomics, Models & A...
4
2B. Intro 2: Biological Side of Computational Biology. Comparative Genomics, Models & A...
2B. Intro 2: Biological Side of Computational Biology. Comparative Genomics, Models & A...
5
3A. DNA 1: Genome Sequencing, Polymorphisms, Populations, Statistics, Pharmacogenomics...
3A. DNA 1: Genome Sequencing, Polymorphisms, Populations, Statistics, Pharmacogenomics...
6
3B. DNA 1 : Genome Sequencing, Polymorphisms, Populations, Statistics, Pharmacogenomics...
3B. DNA 1 : Genome Sequencing, Polymorphisms, Populations, Statistics, Pharmacogenomics...
7
4A. DNA 2: Dynamic Programming, Blast, Multi-alignment, Hidden Markov Models
4A. DNA 2: Dynamic Programming, Blast, Multi-alignment, Hidden Markov Models
8
4B. DNA 2: Dynamic Programming, Blast, Multi-alignment, Hidden Markov Models
4B. DNA 2: Dynamic Programming, Blast, Multi-alignment, Hidden Markov Models
9
5AB. RNA 1: Microarrays, Library Sequencing and Quantitation Concepts
5AB. RNA 1: Microarrays, Library Sequencing and Quantitation Concepts
10
5C. RNA 1: Microarrays, Library Sequencing and Quantitation Concepts
5C. RNA 1: Microarrays, Library Sequencing and Quantitation Concepts
11
6A. RNA 2: Clustering by Gene or Condition and Other Regulon Data Sources Nucleic Acid ...
6A. RNA 2: Clustering by Gene or Condition and Other Regulon Data Sources Nucleic Acid ...
12
6B. RNA 2: Clustering by Gene or Condition and Other Regulon Data Sources Nucleic Acid ...
6B. RNA 2: Clustering by Gene or Condition and Other Regulon Data Sources Nucleic Acid ...
13
7A. Protein 1: 3D Structural Genomics, Homology, Catalytic and Regulatory Dynamics, Fun...
7A. Protein 1: 3D Structural Genomics, Homology, Catalytic and Regulatory Dynamics, Fun...
14
7B. Protein 1: 3D Structural Genomics, Homology, Catalytic and Regulatory Dynamics, Fun...
7B. Protein 1: 3D Structural Genomics, Homology, Catalytic and Regulatory Dynamics, Fun...
15
8A. Protein 2: Mass Spectrometry, Post-synthetic Modifications, Quantitation of Protein...
8A. Protein 2: Mass Spectrometry, Post-synthetic Modifications, Quantitation of Protein...
16
8B. Protein 2: Mass Spectrometry, Post-synthetic Modifications, Quantitation of Protein...
8B. Protein 2: Mass Spectrometry, Post-synthetic Modifications, Quantitation of Protein...
17
9A. Networks 1: Systems Biology, Metabolic Kinetic & Flux Balance Optimization Methods
9A. Networks 1: Systems Biology, Metabolic Kinetic & Flux Balance Optimization Methods
18
9B. Networks 1: Systems Biology, Metabolic Kinetic & Flux Balance Optimization Methods
9B. Networks 1: Systems Biology, Metabolic Kinetic & Flux Balance Optimization Methods
19
10A. Networks 2: Molecular Computing, Self-assembly, Genetic Algorithms, Neural Networks
10A. Networks 2: Molecular Computing, Self-assembly, Genetic Algorithms, Neural Networks
20
11B. Networks 3: The Future of Computational Biology: Cellular, Developmental, Social,...
11B. Networks 3: The Future of Computational Biology: Cellular, Developmental, Social,...
21
11A. Networks 3: The Future of Computational Biology: Cellular, Developmental, Social, E...
11A. Networks 3: The Future of Computational Biology: Cellular, Developmental, Social, E...