Objective

To learn statistical and computational system modeling methodologies and apply to high throughput data generated from modern biology to build hypothetical abstract system models to explain underlying biological mechanisms.

Overview

This course introduces algorithms in state-of-the-art statistical and mathematical models in systems biology. Cellular and molecular processes, such as metabolism and signal transduction, are studied as networks systematically and quantitatively. The central theme along which this course develops is how to construct various networks from high throughput measurements of above life processes. Examples of data include genomes, transcriptomes, proteomes, interactomes. Principles of assessing statistical significance of reconstructed networks will also be discussed. Topics include differential gene expression analysis, Bayesian networks, Boolean networks, and linear differential equations models. Molecular biology concepts in systems biology will be reviewed at the beginning of the course. Homework and team projects will be assigned. Each group will make a final presentation of their projects.

Prerequisites: graduate standing in biology, computer science, mathematics, electrical & computer engineering, physics, or permission of instructor. A previous exposure in two of the following four subjects at undergraduate level is highly recommended: programming in C/C++, Java, R/Splus, or MATLAB; molecular biology; probability and statistics; and differential equations.

Course Information

• Meet during 2:35-3:50pm (Mountain Time) Tuesdays & Thursdays
• Meet through video conference at:
  SH124 at New Mexico State University, Las Cruces, New Mexico
  Brenton Center at Iowa State University, Ames, Iowa
• Instructors:
  • New Mexico State University:

    Prof. Joe Song, joemsong@cs.nmsu.edu, 505-646-4299, Office: SH 141

  • Iowa State University:

    Prof. Dan Voytas, voytas@iastate.edu

    Prof. Vasant Honavar, honavar@cs.iastate.edu

    Prof. Robert Jernigan, jernigan@iastate.edu

• Course web page: www.cs.nmsu.edu/~joemsong/579

Office Hours

I will be holding office hours during 3:50-4:30pm (Mountain Time) Tuesdays & Thursdays or by appointment.

The office hour will be both in person and online. For ISU students, you can add my id joemsong@cs.nmsu.edu to your Windows Live Messenger. I have a webcam in my office and we can talk either with the microphone only or with the webcam on, which way you prefer. Please note that I will be online only during the office hours.

Text

• [recommended] Klipp et al., Systems Biology in Practice, Wiley, 2005. (Amazon.com: new $120, used $88.99, www.addall.com: from $89.70)
• [recommended] Dhammika Amaratunga and Javier Cabrera, Exploration and Analysis of DNA Microarray and Protein Array Data, Wiley, 2003.
• [recommended] Bernard Palsson, Systems Biology : Properties of Reconstructed Networks, Cambridge. 2006.
• [recommended] Alberts et al., Essential Cell Biology - An Introduction to the Molecular Biology of the Cell, Graland Publishing, 1998.
• [recommended] T.A. Brown, Genomes, 2nd Ed., Wiley-Liss, 2002.
• [recommended] Hiroaki Kitano (eds) Foundations of Systems Biology, 2001, MIT Press.
• Journals articles from Bioinformatics, J. of Computational Biology, J. of Bioinformatics and Computational Biology, Nucleic Acid Research, Genome Research, J. of Molecular Biology, and etc.
• Conference papers from ISMB, RECOMB, CSB, ECCB, PSB and etc.

Important Dates

First class meets on .......................................................................... August 24 (TH)

Last day to withdraw with “W” at NMSU ......................................................October 17 (TU)

There will be no class due to Thanksgiving holiday on ..............................November 21 (TU) & 23 (TH)

Last class meets on ......................................................................... December 7 (TH)

Final presentation during 3:30 am to 5:30 pm (Mountain Time) on ............................. December 14 (TH)

Grading Policies

• Homework (45%).
• One team project (55%).
• Grading policies:
  • Homework is due two weeks after posted. Late homework will not be accepted.
  • No incomplete grades.

Topics

• Review of molecular biology and biochemistry

  (Reference: Edda Klipp 2005)

• Statistical analysis of microarray data
  • Multiple t-test
  • The SAM test

  (Reference: Amaratunga and Cabrera 2003)

• Boolean networks

  (Reference: recent papers by Edward Dougherty et al.)

• Bayesian networks

  (Reference: recent papers by Nir Friedman et al.)

• Metabolic networks

  (Reference: Bernard Palsson 2006. Edda Klipp 2005.)

• Linear differential equations models

  (Reference: Bernard Palsson 2006. Edda Klipp 2005.)

Useful Internet Resources

• www.systems-biology.org

Plagiarism and Code of Conduct

Please see the “Student Code of Conduct” in the current “Student Handbook.” (URL http://www.nmsu.edu/%7Evpsa/SCOC/misconduct.html) regarding academic misconduct and plagiarism. The penalty for plagiarism or other forms of academic misconduct (as defined in the Student Code of Conduct) leads to failure of the course.

Non-Discrimination Policy

Feel free to call Jerry Nevarez, Director of Institutional Equity, at 505-646-3635 with any questions you may have about NMSU’s Non-Discrimination Policy and complaints of discrimination, including sexual harassment.

Students with Disabilities

Feel free to call Michael Armendariz, Coordinator of Services for Students with Disabilities, at 505-646-6840 with any questions you may have on student issues related to the Americans with Disabilities Act (ADA) and/or Section 504 of the Rehabilitation Act of 1973. All medical information will be treated confidentially.

Lecture Schedule

• Lecture 1 (Voytas, 8/24/06 TH). Basics of molecular biology 1.
• Lecture 2 (Voytas, 8/29/06 TU). Basics of molecular biology 2.
• Lecture 3 (Voytas, 8/31/06 TH). Biotechnology 3.
• Lecture 4 (Voytas, 9/5/06 TU). Biotechnology 4.
• Lecture 5 (9/7/06 TH). Systems biology overview.
• Lecture 6 (9/12/06 TU). Differential gene expression analysis 1.
• Lecture 7 (9/14/06 TH). Differential gene expression analysis 2.
• Lecture 8 (9/19/06 TU). Differential gene expression analysis 3.
• Lecture 9 (9/21/06 TH). Differential gene expression analysis 4.
• Lecture 10 (9/26/06 TU). Differential gene expression analysis 5.
• Lecture 11 (9/28/06 TH). Boolean networks – Properties.
• Lecture 12 (10/3/06 TU). Boolean networks – Simulation.
• Lecture 13 (10/5/06 TH). Boolean networks – Reconstruction.
• Lecture 14 (10/10/06 TU). Boolean networks – Reconstruction.
• Lecture 15 (10/12/06 TH). Boolean networks – Applications.
• Lecture 16 (Honavar, 10/17/06 TU). Bayesian networks 1.
• Lecture 17 (Honavar, 10/19/06 TH). Bayesian networks 2.
• Lecture 18 (Honavar, 10/24/06 TU). Bayesian networks 3.
• Lecture 19 (Honavar, 10/26/06 TH). Bayesian networks 4.
• Lecture 20 (Honavar, 10/31/06 TU). Bayesian networks 5.
• Lecture 21 (11/2/06 TH). Metabolism networks 1.
• Lecture 22 (11/7/06 TU). Metabolism networks 2.
• Lecture 23 (11/9/06 TH). Metabolism networks 3.
• Lecture 24 (11/14/06 TU). Metabolism networks 4.
• Lecture 25 (11/16/06 TH). Metabolism networks 5.
• No class (11/21/06 TU Thanksgiving Holiday). No Class.
• No class (11/23/06 TH Thanksgiving Holiday). No Class.
• Lecture 26 (11/28/06 TU). Differential equation models 1.
• Lecture 27 (11/30/06 TH). Differential equation models 2.
• Lecture 28 (12/5/06 TU). Differential equation models 3.
• Lecture 29 (12/7/06 TH). Differential equation models 4.
• Final project presentation (12/14/06 TH).