CS 6824: New Directions in Computational Systems Biology
Spring 2011, 2:30pm-3:45pm Mondays and Wednesdays, Randolph 208
|Date||Topic and papers||Presenter(s) (links point to the presentations)|
|Wed, Jan 19, 2011||Introduction to Computational Systems Biology||T. M. Murali|
|Mon, Jan 24, 2011||Course Topics and Projects||T. M. Murali|
|Wed, Jan 26, 2011||Course Projects||T. M. Murali, same lecture as the previous class|
|Mon, Jan 31, 2011||Gene function prediction||T. M. Murali|
|Wed, Feb 2, 2011||Gene function prediction||T. M. Murali, same lecture as the previous class|
|Mon, Feb 7, 2011||The state of gene function prediction in Arabidopsis thaliana||T. M. Murali, lecture available on Scholar|
|Wed, Feb 9, 2011||Class cancelled|
|Mon, Feb 14, 2011||Clustering gene expression data||T. M. Murali|
|Wed, Feb 16, 2011||Clustering gene expression data to find cancer gene modules||T. M. Murali|
|Mon, Feb 21, 2011||Systematic planning of genome-scale experiments in poorly studied species||Iman Tamassoly|
|Wed, Feb 23, 2011||Systematic planning of genome-scale experiments in poorly studied species||Iman Tamassoly
Continued from the previous class
|Mon, Feb 28, 2011||Link communities reveal multiscale complexity in networks||Tozammel Hossain|
|Wed, Mar 2, 2011||Dynamic interaction networks in a hierarchically organized tissue||Akshay Kakumanu|
|Mon, Mar 14, 2011||Dynamic interaction networks in a hierarchically organized tissue||Vinaya Vijayan
Continued from the previous class
|Wed, Mar 16, 2011||Midterm project reviews||All students|
|Mon, Mar 21, 2011||Genome-Wide Association Data Reveal a Global Map of Genetic Interactions among Protein Complexes||Steve Mason|
|Wed, Mar 23, 2011||Integrated functional networks of process, tissue, and developmental stage specific interactions in Arabidopsis thaliana||Elijah Myers|
|Mon, Mar 28, 2011||Estimating high dimensional intervention effects from observational data||Zhao Zhao|
|Wed, Mar 30, 2011||Class cancelled|
|Mon, Apr 4, 2011||Predicting
causal effects in large-scale systems from observational
Continued from the previous class
|Wed, Apr 6, 2011||Automated multidimensional phenotypic profiling using large public microarray repositories||Mingming Liu|
|Mon, Apr 11, 2011||SING: subgraph search in non-homogeneous graphs||Ahsanur Rahman|
|Wed, Apr 13, 2011||A
predictive model for transcriptional control of physiology
in a free living cell
Be sure to read the supplement for this class.
|T. M. Murali|
|Mon, Apr 18, 2011||A predictive model for transcriptional control of physiology in a free living cell||T. M. Murali, continued from the previous class|
|Wed, Apr 20, 2011||A predictive model for transcriptional control of physiology in a free living cell||T. M. Murali, continued from the previous class|
|Mon, Apr 25, 2011||Densely Interconnected Transcriptional Circuits Control Cell States in Human Hematopoiesis||Lecong Zhou|
|Wed, Apr 27, 2011||Class cancelled|
|Mon, May 2, 2011||A Biological Solution to a Fundamental Distributed Computing Problem||T. M. Murali|
|Wed, May 4, 2011||A Biological Solution to a Fundamental Distributed Computing Problem||T. M. Murali, same lecture as the previous class|
|Tue, May 10, 2011
|Final project presentations|
About the Course
- What is Computational Systems Biology?
- What is the focus of this 6000-level course?
- Who should take this course?
- Course structure
- Papers to be covered
Cells, tissues, organs and organisms are systems of components whose interactions have been defined, refined, and optimised over hundreds of millions of years of evolution. Computational systems biology is a field that aims at a system-level understanding of biological systems by analysing biological data using computational techniques. Systems biology aims to answer the following key questions by integrating experimental and computational approaches:
- What are the basic structures and properties of the biological networks in a living cell?
- How does a biological system behave over time under various conditions?
- How does a biological system maintain its robustness and stability?
- How can we modify or construct biological systems to achieve desired properties?
You should take this course if you are curious to find out how the latest research is shaping our understanding of how the living cell behaves as a system. The course will cover the latest research in computational systems biology, primarily in the context of molecular interaction networks. We will spend a significant part of the course on examining how the analysis of DNA microarray data and other high-throughput data is crucial to progress in this area. The course is geared towards graduate students whose main research interest is bioinformatics or who use bioinformatic tools and techniques in their research.
There are many exciting and profound issues that researchers in this area are actively investigating, such as the robustness of biological systems, network structures and dynamics, and applications to drug discovery. During this course, we will come across many interesting open research problems. Taking this course might be an excellent way to create research topics and projects for your Master's or Ph.D. thesis in the area of bioinformatics/computational biology. In this course, you will be able to communicate and work with students and researchers with varied backgrounds. In addition, Virginia Tech is humming with research activities in this area.
Computer Science graduate students: the Data and Algorithm Analysis (CS 4104) or similar course is a pre-requisite. It will help if you also have taken Algorithms in Bioinformatics (CS 5124) and a course on combinatorics and graph theory such as Applied Combinatorics (MATH 3134). An introductory molecular biology course such as Biological Paradigms for Bioinformatics will provide extremely useful biological background.
Life science graduate students: I expect that you have taken courses in biochemistry, cell biology, and molecular biology. A course like Computation for Life Sciences (CS 5045) provides very useful computational background.
The course will primarily be driven by lectures and by seminars where one or more students present a related group of papers from literature. I will try to arrange papers that cover both biological and computational aspects. Ideally, I would like a group to contain students with backgrounds in computer science, mathematics, and/or statistics and students with backgrounds in biology and chemistry.
Your grade will depend on your presentation (20%), on class participation (30%), and a final project (50%). The final project is a group software project. I will define software projects that are inspired by the papers you present in class. The project will involve creating some new software or using existing software innovatively combined with some intensive biological analysis of the results. You are welcome to suggest a project to me.CiteULike page collects a superset of the papers that we will discuss. The actual set of papers we will cover will depend on the interests of the students. I have also grouped some of these papers under more specific categories. This list will evolve over the course of the semester.