文档介绍：Tue Sep 18 Intro 1: Computing, statistics, Perl, Mathematica
Tue Sep 25 Intro 2: Biology, comparative genomics, models & evidence, applications
Tue Oct 02 DNA 1: Polymorphisms, populations, statistics, pharmacogenomics, databases
Tue Oct 09 DNA 2: Dynamic programming, Blast, multi-alignment, HiddenMarkovModels
Tue Oct 16 RNA 1: 3D-structure, microarrays, library sequencing & quantitation concepts
Tue Oct 23 RNA 2: Clustering by gene or condition, DNA/RNA motifs.
Tue Oct 30 Protein 1: 3D structural genomics, homology, dynamics, function & drug design
Tue Nov 06 Protein 2: Mass spectrometry, modifications, quantitation of interactions
Tue Nov work 1: Metabolic ic & flux balance optimization methods
Tue Nov work 2: puting, self-assembly, ic algorithms, s
Tue Nov work 3: Cellular, developmental, social, ecological & commercial models
Tue Dec 04 Project presentations
Tue Dec 11 Project Presentations
Tue Jan 08 Project Presentations
Tue Jan 15 Project Presentations
Bio 101: Genomics & Computational Biology
Protein2: Last week's take home lessons
Separation of proteins & peptides
Protein localization & complexes
Peptide identification (MS/MS)
Database searching & sequencing.
Protein quantitation
Absolute & relative
Protein modifications & crosslinking
Protein - metabolite quantitation
Net1: Today's story & goals
Macroscopic continuous concentration rates
Cooperativity & Hill coefficients
Bistability
Mesoscopic discrete molecular numbers
Approximate & exact stochastic
Chromosome Copy Number Control
Flux balance optimization
Universal stoichiometric matrix
Genomic parisons
Networks Why model?
Red blood cell metabolism Enzyme ics (Pro2)
Cell division cycle Checkpoints (RNA2)
Plasmid Copy No. Control Single molecules
Phage l switch Stochastic parative metabolism Genomic connections
Circadian rhythm Long time delays
E. coli chemotaxis Adaptive, spatial effects
also, all have large ic & ic datasets.
Types of interaction models
Quantum Electrodynamics subatomic
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