Open Topics in Bioinformatics (CSE 891)
Course objectives: study interesting computational network biology problems and
their algorithms, with a focus on the principles used to design those algorithms. (3
credits)
Instructor: Jin Chen, Office: 232 Plant Biology Bld. Email: jinchen@msu.edu. Office
hours: Thursday 2PM-3PM. If you cannot attend office hours, email me about
scheduling a different time.
Web page: http://www.msu.edu/~jinchen/cse891a
Class time: 2011 spring, Tue/Thu 12:40 - 2:00 PM in Egr 1300
Important Days:
Class Begins
Open adds end
Last day to drop with refund
Last day to drop with no grade reported
Class Ends
1/10/2011
1/14/2011
2/3/2011
3/2/2011
5/6/2011
Final exam: No final exam.
Course work: One 80 minutes lecture, and 80 minutes of discussion & student
presentations each week.
Prerequisites: Graduate students in science or engineering. Note: an override is
necessary for non-CSE graduate students; please send your PID & NetID to Dr. Jin
Chen.
Grading policies: The course will be graded on attendance (10%), participation
(20%), and presentation (70%).
Course Introduction: CSE381 will introduce biologists to computational
considerations, and computational scientists to biological considerations, in the
context of modern biological "grand challenges". The course will cover recent
research on graph mining algorithms for the computational analysis of biological
networks. No prior knowledge of biology is required. Necessary concepts from
biology will be reviewed as needed. Computationally inclined biology graduate
students are encouraged to take the class as well. Some basic computer science
knowledge is assumed but an effort is made for the class to be self-contained.
The course covers a wide range of algorithmic techniques and tools, with the goal
that when you are faced with some problem in the future, you will have seen many
techniques that might be applicable. The course is useful even for people who will
not focus on computational biology. Likely topics for 2011 will focus on
computational network biology topics including network construction and behavior
study on gene regulation networks, protein-protein interaction networks and
metabolic networks, and their next-generation sequencing related solutions.
Biological problems considered include predicting protein function from proteinprotein interaction networks, comparing interaction networks from multiple
organisms, finding common network patterns, inferring interactions between
proteins, modeling signaling pathways, and visualizing biological graphs.
A biological network is an integrated research environment. Biological networks
research environment enables integrative analysis of:
* Interaction networks, metabolic and signaling pathways together with
transcriptomic, metabolomic and proteomic data
* Genomic sequences including gene regulatory regions, binding sites and
respective transcription factors
* Comparative genomics of clusters of homologous/orthologous genes and
phylogenies
* 3D protein structures and ligand binding, small molecules and drugs
* Multiple ontologies including GeneOntology, Cell and Tissue types,
Anatomy and Diseases, and taxonomies
Biological networks system allows querying over a large number of features related
to transcriptional regulation, pathways and interaction maps, microarray
experiments, 3D structures and other types of data. Additional potential topics
include genome-scale alignments; protein structure; databases, data integration,
and data warehousing.