CAP 6938: Graphs and Networks in Computational Biology
 

Lecture: MW 4:30PM - 5:45PM

Location: BA2 221

Instructor: Dr. Haiyan Nancy Hu

Email: haihu@cs.ucf.edu

Office: HEC- 233

Phone Number: 407-882-0134

Office Hours: MW 10:00AM - 11:00AM

 
 

Description:

This course will summarize computational and mathematical methods in current biological network analysis. We will focus on methods that are able to discover simplifying principles that can help us to understand how biological circuits work, and why they are designed the way they are. We will also introduce/review computational environments and software tools that are prominent in Bioengineering/Bioinformatics applications and especially in analyzing biological networks constructed from large scale genomics data. This course will train graduate students so that they are aware of current research problems in computational systems biology and they are prepared to develop more advanced methods and tools for systems biology and bioengineering applications..

Bioinformatics is an active and interdisciplinary research area. This course is open to all students with background such as computer science, biology, mathematics or statistics who are interested in bioinformatics research.

 
 

Prerequisite:

No formal prerequisite and open to all graduate students.

 
 

Book Reference:

"An Introduction to Systems Biology: Design Principles of Biological Circuits" by Uri Alon. Chapman & Hall/CRC, ISBN 978-1584886426. 320 pages.
"Biomolecular Networks: Methods and Applications in Systems Biology" by Chen, L, Wang, R, zhang, XR. ISBN: 978-0-470-24373-2. 391 pages.
 

Grading:

Assignments and Paper presentation (50%). Each student will give a presentation of a course-related paper. Students are encouraged to discuss with the instructor to decide the topic he/she would like to present.

Final project (50%). We may have students from very diverse background such as biology and computer science. Final problem-solving projects can be either biology-oriented or programming-oriented depending on a student's own background. Students are required to discuss with the instructor to design the final projects during the early weeks of the class. A student is encouraged to discuss with the instructor on collaborating with another student with different background on the final project.

 

Tentative Schedule & Reading

Date Topic Notes and References*
W1: 08/22 Introduction/Administrivia Notes
08/24 The central dogma Notes
W2: 08/29 Biological networks and graph Notes
08/31 Gene expression data and co-expression networks Notes
W3: 09/05 Labor day, no class Notes
09/07 network module and graph clustering Notes
W4: 09/12 network module and graph clustering Notes
09/14 randomized networks and network motifs Notes
W5: 09/19 Graphical models: bayesian network Notes
09/21 Graphical models: bayesian network Notes
W6: 09/26 Graphical models: bayesian network Notes
09/28 Graphical models: Hidden Markov Model Notes
W7: 10/03 Graphical models: Hidden Markov Model Notes
10/05 Graphical models: Hidden Markov Model Notes
W8: 10/10 Mathematical model of GRN Paper 1, 3
10/12 TF information and gene expression data Paper 4, 8
W9: 10/17 TF information and gene expression data Paper 4, 8
10/19 using sequence and TF-DNA binding data Paper 5, 6, 7
W10: 10/24 Time-course data Paper 9
10/26 network modeling and simulation Notes
W11: 10/31 network modeling and simulation Notes
11/02 network modeling and simulation Paper 10,11
W12: 11/07 Other networks Paper 12, 13
11/9 novel graph alogorithms in computational biology
W13: 11/14 novel graph alogorithms in computational biology
11/16 novel graph alogorithms in computational biology
W14: 11/21 novel graph alogorithms in computational biology
11/23 novel graph alogorithms in computational biology
W15: 11/28 novel graph alogorithms in computational biology
11/30 novel graph alogorithms in computational biology
Reference Papers:
1. Reconstruction of cellular signalling networks and analysis of their properties, Nat Rev Mol Cell Biol. 2005 Feb;6(2):99-111.
2. Lee, T.I., et al., Transcriptional regulatory networks in Saccharomyces cerevisiae. Science, 2002. 298(5594): p. 799-804.

3. A biophysical model for analysis of transcription factor interaction and binding site arrangement from genome-wide binding data. PLoS ONE, 4(12): e8155
4. Identification of functional modules using network topology and high-throughput data. BMC Systems Biology, Vol. 1, No. 8 (2007)
5. From DNA sequence to transcriptional behaviour: a quantitative approach. NRG 2009.
6. Bayesian error analysis model for reconstructing transcriptional regulatory networks. PNAS 2006.
7. Deciphering a transcriptional regulatory code: modeling short-range repression in the Drosophila embryo. MSB 2010.
8. Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Segal et al., Nature Genetics 2003.
9. Reconstructing dynamic regulatory maps. Ziv Bar-Joseph. MSB 2007.
10. Transcriptional regulation in constraints-based metabolic models of Escherichia coli. J Biol Chem, 2002. 277(31): p. 28058-64.
11. Flux balance analysis of primary metabolism in Chlamydomonas reinhardtii. BMC Syst Biol, 2009. 3: p. 4.
12. The human disease network. Barabasi, PNAS 2007.
13. Cross-species analysis of biological networks by Bayesian alignment. PNAS 2006

14. Merchant, S.S., et al., The Chlamydomonas genome reveals the evolution of key animal and plant functions. Science, 2007. 318(5848): p. 245-50.

15. Pe'er, A. Regev and A. Tanay. Minreg: Inferring an Active Regulator Set. Bioinformatics 18(Supplement 1):S258-S267, 2002.
16. Hartemink, D. Gifford, T. Jaakkola and R. Young. Using Graphical Models and Genomic Expression Data to Statistically Validate Models of Genetic Regulatory Networks. In Proceedings of the Sixth Pacific Symposium on Biocomputing, 2001.
17. Hartemink, D. Gifford, T. Jaakkola and R. Young. Combining Location and Expression Data for Principled Discovery of Genetic Regulatory Network Models. In Proceedings of the Seventh Pacific Symposium on Biocomputing, 2002.
18. Chrisman, P. Langley, S. Bay and A. Pohorille. Incorporating Biological Knowledge into Evaluation of Causal Regulatory Hypotheses. In Proceedings of the Eighth Pacific Symposium on Biocomputing, 2003.
19. Bay, J. Shrager, A. Pohorille and P. Langley. Revising Regulatory Networks: From Expression Data to Linear Causal Models. Submitted, 2002.
20. Pe'er, A. Regev, G. Elidan and N. Friedman. Inferring Subnetworks from Perturbed Expression Profiles. In Proceedings of the Ninth International Conference on Intelligent Systems for Molecular Biology, 2001.
21. Segal, Y. Barash, I. Simon, N. Friedman and D. Koller. From Promoter Sequence to Expression: A Probabilistic Framework. In Proceedings of the Sixth Annual International Conference on Computational Molecular Biology (RECOMB), 2002.
22. Ideker, V. Thorsson and R. Karp. Discovery of Regulatory Interactions through Perturbation: Inference and Experimental Design. In Proceedings of the Fifth Pacific Symposium on Biocomputing, 2000.
23. Tanay and R. Shamir. Computational Expansion of Genetic Networks Proceedings of the 9th International Conference on Intelligent Systems for Molecular Biology, 2001.
24. Yoo, V. Thorsson and G. Cooper. Discovery of Causal Relationships in a Gene Regulation Pathway from a Mixture of Experimental and Observational DNA Microarray Data In Proceedings of the Seventh Pacific Symposium on Biocomputing, 2002.
25. Sharp and J. Reinitz Prediction of Mutant Expression Patterns using Gene Circuits. BioSystems 47:79-90, 1998.

26. Pham, L., et al., Network-based prediction for sources of transcriptional dysregulation using latent pathway identification analysis. Proc Natl Acad Sci U S A. 108(32): p. 13347-52.

27. Zhao, Y., E. Levina, and J. Zhu, Community extraction for social networks. Proc Natl Acad Sci U S A. 108(18): p. 7321-6.

28. Davidson, E.H., et al., A genomic regulatory network for development. Science, 2002. 295(5560): p. 1669-78.

29. Suderman, M. and M. Hallett, Tools for visually exploring biological networks. Bioinformatics, 2007. 23(20): p. 2651-9.

30. Shannon, P., et al., Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res, 2003. 13(11): p. 2498-504.

31. Saul, Z.M. and V. Filkov, Exploring biological network structure using exponential random graph models. Bioinformatics, 2007. 23(19): p. 2604-11.

32. Przulj, N., Biological network comparison using graphlet degree distribution. Bioinformatics, 2007. 23(2): p. e177-83.

33. Sneppen, K., M.A. Micheelsen, and I.B. Dodd, Ultrasensitive gene regulation by positive feedback loops in nucleosome modification. Mol Syst Biol, 2008. 4: p. 182.

34. Trusina, A., et al., Functional alignment of regulatory networks: a study of temperate phages. PLoS Comput Biol, 2005. 1(7): p. e74.

35. Maslov, S., et al., Toolbox model of evolution of prokaryotic metabolic networks and their regulation. Proc Natl Acad Sci U S A, 2009. 106(24): p. 9743-8.

36. Maslov, S. and K. Sneppen, Computational architecture of the yeast regulatory network. Phys Biol, 2005. 2(4): p. S94-100.

37. Axelsen, J.B., S. Bernhardsson, and K. Sneppen, One hub-one process: a tool based view on regulatory network topology. BMC Syst Biol, 2008. 2: p. 25.

38. Krishna, S., S. Semsey, and K. Sneppen, Combinatorics of feedback in cellular uptake and metabolism of small molecules. Proc Natl Acad Sci U S A, 2007. 104(52): p. 20815-9.

39. Werner, M., et al., Dynamics of uptake and metabolism of small molecules in cellular response systems. PLoS One, 2009. 4(3): p. e4923.
Software demo: 1. software demo: WGCNA: an R package for weighted correlation network analysis, BMC Bioinformatics 2008
2. software demo:Galaxy: A platform for interactive large-scale genome analysis. Genome Research 2005.
3. software demo: network motif finding
4. software demo: nucleR: a package for non-parametric nucleosome positioning
6. software demo:cytoscape
7. software demo:rBioNet
8. softare demo: linkcomm, LinkinPath, KEGGtranslator
9. software demo:COPASI, Bowtie, Tophat