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.

Microsoft® Excel software is recommended for viewing the .xls files found on this course site. Free Microsoft® Excel viewer software can also be used to view the .xls files.
Mathematica® software is required to run the .nb files found on this course site.
Media player software, such as Quicktime® Player, RealOne™ Player, or Windows Media® Player, is required to run the .mp3 files found on this course site.



RealOne™ is a trademark or a registered trademark of RealNetworks, Inc.
Microsoft^® is a registered trademark or trademark of Microsoft Corporation in the U.S. and/or other countries.
Mathematica^® is a registered trademark of Wolfram Research, Inc.
QuickTime^® is a trademark of Apple Computer, Inc., registered in the U.S. and other countries.
Windows Media^® is a registered trademark or trademark of Microsoft Corporation in the U.S. and/or other countries.

Syllabus

Prerequisites

Introductory courses in biology, computer science, and statistics. If you have any doubt about whether you have the equivalent experience, you should attend the appropriate sections which will focus on catching up with extra sections supplementing catch-up topics in greatest demand.

Course Organization

In addition to the lectures there will be section discussion meetings at the days and times above. Students will participate in at least one of those sections and form problem-solving and project teams consisting of at least one biology expert and one math/computer/engineering expert (or two to four people knowledgeable in both disciplines). Separate section on the basics of programming and molecular biology will be available in the first few weeks to even out the expected wide variation in backgrounds. Grades will be based on six problem sets, one final project, and participation in discussion sections covering the problems and about one scientific article per week. Your time commitment will be about 4 hours for classes and 6 to 12 additional hours per week, depending on background.

Calendar

 

LEC #       TOPICS                 1       Intro 1: Course overview and introduction to the computational side of computational biology. Why use Perl & Mathematica? Write and run simple scripts. We will also assign sections addressing Biology, computing, and advanced topics. Questionaires due.                 2       Intro 2: Biological Side of Computational Biology; Central Dogma; Comparative Genomics; Models & Real World Applications. Note: Please take your initial observations about Problem Set#1 to you first section meetings (i.e. check that you actually have access to Perl & Mathematica).                 3       DNA 1: Genome Sequencing, Polymorphisms, Populations, Statistics, Pharmacogenomics; Databases. Note: Problem Set #1 is due at the start of class. (Answers will be posted 48 hrs later.)                 4       DNA 2: Dynamic Programming, Blast, Multi-alignment, HiddenMarkovModels.                 5       RNA 1: Microarrays, Library Sequencing and Quantitation Concepts. Note: Problem Set #2 is due.                 6       RNA 2: Clustering by Gene or Condition and Other Regulon Data Sources Nucleic Acid Motifs; The Nature of Biological "proofs."                 7       Proteins 1: 3D Structural Genomics, Homology, Catalytic and Regulatory Dynamics, Function & Drug Design.                 8       Proteins 2: Mass Spectrometry, Post-synthetic Modifications, Quantitation of Proteins, Metabolites, & Interactions.                 9       Networks 1: Systems Biology, Metabolic Kinetic & Flux Balance Optimization Methods. Note: Problem Set #4 is due. (#5 will be available but not due until lecture 14.)                 10       Networks 2: Molecular Computing, Self-assembly, Genetic Algorithms, Neural Networks.                 11       Networks 3: The Future of Computational Biology: Cellular, Developmental, Social, Ecological & Commercial Models.                 12       Project Presentations; All written project reports and overhead slides (for presentations) due.                 13       Project Presentations.                 14       Project Presentations; Problem Set #5 due.