About the course

Course Number (undergrad) CS 446 (Section 001)      [view flyer]
Course Number (grad) CS 519 (Section 007)      [view flyer]
Course title Biological Networks (aka "Networks in Computational Biology")
Course offered at Oregon State University, Corvallis Campus
Course dates Fall Quarter, 2016 (Sept. 21 – Dec. 9)
Instructor Stephen Ramsey
Number of credits 3 (expected workload is nine hours per week, including the three hours in class)
Prerequisites CS 261 (which also includes the prerequisite MTH 231), or permission of the instructor. CS 325 and MTH 341 are recommended but not required. Also, some familiarity with the concepts of probability distributions and random variables is recommended (e.g., MTH 361, ECE 353, or ST 314). Non-CS students who are proficient in programming and have the above-described math background are encouraged to register; just email the instructor. For students wishing to assess their level of mathematical preparation, Chapter 7 of the book Networks: an Introduction by M.E.J. Newman (see below) gives a good sense of the level of math in this course.
Weekly schedule MWF 1:00–1:50 PM (Strand Agriculture Hall 112)
Course type Lecture, discussion, and computational work
Catalog description An introduction to biological networks and computational methods for their analysis, inference, and functional modeling. Various network centralities, topological measures, clustering algorithms, and probabilistic annotation models are introduced in the context of protein interaction, gene regulatory, and metabolic networks. The course also surveys bioinformatics methods for data-driven inference of network structure.
Textbook Assigned readings will be provided as PDFs on Canvas. Students note: this class is reading-intensive.

Some useful textbooks that I will put on reserve in the library are:
  • MEJ Newman, Networks: an introduction. Oxford University Press, 2010.
  • Dasgupta, Papadimitrou, and Vazirani. Algorithms. Free online PDF. 2006.
  • Alpan Raval and Animesh Ray. Introduction to biological networks. CRC Press, 2014.
  • Kennety A. Lambert. Fundamentals of Python: Data Structures. CENGAGE Learning, 2014.
  • Björn Junker and Falk Schreiber. Analysis of biological networks. Wiley 2008.
  • Dehmer, Emmert-Streib, Graber, and Salvador (ed): Applied Statistics for Network Biology. Wiley-Blackwell, 2011.
Of the above, Newaman's book is well worth owing as a reference textbook on computational and mathematical methods of network analysis. And PDF copies of the textbook by Dasgupta, Papadimitrou, and Vazirani can be easily found online because it was at one time published online by the authors as a free PDF.
Class intranet We will be using Canvas for class coordination and for distributing reading materials.
Course materials List of topics is available under the Topics tab; detailed course syllabus with reading assignments is available for enrolled students on the Canvas.
Grading (tentative plan)
  • 50% Homework (homework assignments will involve programming and data analysis)
  • 20% In-class quizzes
  • 10% In-class participation
  • 20% In-class poster presentation on your final project

Topics for CS 446/519

Here is the list of topics to be covered in the class. For specific reading assignments, please see the online syllabus on the Canvas system. Generally, for each class session, there will be assigned reading on algorithms and methods (generally from a book chapter), and then an article from the computational biology literature demonstrating an application of one of the methods. Please note that the syllabus is subject to change as the quarter progresses; updates will be posted on the Canvas system if there is a change in the syllabus.

  • Introduction to biological networks
  • Graph theory definitions and data structures
  • Scale-free networks; attack tolerance
  • Clustering coefficients; network density
  • Paths, geodesic paths, diameter, components
  • Single-vertex shortest paths
  • Betweenness centrality; distance-based centralities
  • Feedback-based centralities
  • Subnetwork motifs
  • Functional/transcriptional congruence
  • Assortative mixing, network modularity
  • Network community detection – global
  • Network community detection – seed- and-extend
  • Network conservation and alignment
  • Introduction to probability
  • Network-based probabilistic prediction of protein function
  • Correlation networks and weighted correlation network analysis
  • Partial correlation coefficients and covariance estimation
  • Information-theoretic inference of network structure
  • Inference of protein-protein interactions
  • Inference of probabilistic network structure from single-time, multivariate measurements with interventional data
  • Sampling from the posterior distribution of the network structure
  • Inference of Boolean networks from time-course, multivariate measurements
  • Integrative methods for inferring gene regulatory network structure

Organization of CS446/519

This course will combine lecture, in-class discussions of assigned readings, and computational exercises. Student performance in the course will be evaluated based on their completion of problem-based homework assignments, in-class quizzes, participation in in-class discussion and activities, and an in-class poster presentation on a research project. For more information, please consult the detailed syllabus posted on the Canvas system.

Statement Regarding Students with Disabilities

Accommodations for students with disabilities are determined and approved by Disability Access Services (DAS). If you, as a student, believe you are eligible for accommodations but have not obtained approval please contact DAS immediately at 541-737-4098 or at ds.oregonstate.edu. DAS notifies students and faculty members of approved academic accommodations and coordinates implementation of those accommodations. While not required, students and faculty members are encouraged to discuss details of the implementation of individual accommodations.

Statement of Expectations for Student Conduct, including the Cheating Policy


Undergraduate Policy Manual, College of Engineering


Contact the instructor

Stephen Ramsey stephen.ramsey@oregonstate.edu
Office (Biomedical Sciences) 208A Dryden Hall
Office Hours TBD (Dryden Hall)
© 2013–2016 Stephen Ramsey.    The orange OSU logo is a trademark of Oregon State University and is used with permission.