BIOE598 GRN: Gene Regulatory Networks

Course objective:
Using transcription networks (TN) as representative gene regulatory networks (GRN) to study the physical principles of GRNs. Learn software tools to abstract, quantitatively analyze and visualize GRNs.

Text book: An introduction to systems biology: design principles of biological circuits. Uri Alon, Chapman & Hall/CRC.

Reading materials: Required, optional and presentation paper.

Software: dChip, WGCNA, EXPANDER, Galaxy, Cytoscape

Logistics:
Meeting Time: Spring 2009, 2:00pm-3:20pm, Tue Thur
Meeting place: 2320 Digital Computer Lab, 1304 W. Springfield Ave. Exceptions, the lab sessions (Feb 11, 18, 25, March 4, 18) will take place in IGB 607.
Credits: 3 hours.
Course Reference number: CRN 53713  
Instructor: Sheng Zhong (szhong AT uiuc DOT edu)
Prerequisites: MCB250, MATH285

Evaluation:
Course grade is based on class participation (50%), a short final paper (50%).

Class participation requirements: 1) hand in two questions and your answers to these questions before every Tuesday class. These questions should be related to the course materials of that time. 2) One in-class presentation. The presentation can present a paper or a software tool (software demo).

Final paper: Make one argument, comment or opinion with at most 500 words, one figure (or table) and three references. Tips to the paper: start now and revise 100 times!

Contents:

1. Transcription Networks: basic concepts. Slides

    1.1 The cognitive problem of the cell. Optional reading: Reconstruction of cellular signalling networks and analysis of their properties, Nat Rev Mol Cell Biol. 2005 Feb;6(2):99-111.

    1.2 Logic input functions. Required reading: On schemes of combinatorial transcription logic. Proc Natl Acad Sci U S A 2003, 100(9):5136-41. Slides

    Required reading: A biophysical model for analysis of transcription factor interaction and binding site arrangement from genome-wide binding data. PLoS ONE, 4(12): e8155. Presentation by Piyush Labhsetwar, Feb 23. slides

Tips: the two required reading papers above clarify the link between the strengths of TFBSs and the regulatory inputs.

    1.3 Dynamics and response time.

2. Reconstruction of GRNs (not in Alon book).  Software demo: dChip. Software demo: R, online manual

    2.1 Revisiting the back-of-envelop calculations. Required now. Reconstruction of cellular signalling networks and analysis of their properties, Nat Rev Mol Cell Biol. 2005 Feb;6(2):99-111.

    2.2 Handling gene expression data with dChIP software. Required reading: dChIP manual sections of Data Processing and Normalize arrays. Software demo by the instructor.

    2.3 Reconstruction with expression data. Required reading: Identification of functional modules using network topology and high-throughput data. BMC Systems Biology, Vol. 1, No. 8 (2007)

Required reading, An introduction to R: Preface, Introduction and preliminaries, Simple manipulations numbers and vectors. Software demo by the instructor.

Required reading, WGCNA: an R package for weighted correlation network analysis, BMC Bioinformatics 2008. Software demo: Chieh-Chun Chen. Software demo by Chieh-Chun Chen, Feb 18.

    2.4. Considering sequence and TF-DNA binding data. Required reading: From DNA sequence to transcriptional behaviour: a quantitative approach. NRG 2009.

Required reading: Cytoscape. Software demo by Xiaoyi Cao, Feb 25.

    Optional reading: Bayesian error analysis model for reconstructing transcriptional regulatory networks. PNAS 2006. Presentation paper.

    Required reading: Deciphering a transcriptional regulatory code: modeling short-range repression in the Drosophila embryo. MSB 2010. Presentation paper. Presentation by Shuyi Ma, Feb 16. slides

    Required reading, Galaxy: A platform for interactive large-scale genome analysis. Genome Research 2005. Software demo: Dan Xie, March 4. 

    2.5. Using other information for network reconstruction.

    2.5.1 TF information. Required reading: Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data. Segal et al., Nature Genetics 2003. Lecture.

    2.5.2 Time-course data. Required reading: Reconstructing dynamic regulatory maps. Ziv Bar-Joseph. MSB 2007. Charu Kumar. March 18.

    2.6. Network comparison from disease and evolutionary perspectives.
    Required reading: The human disease network. Barabasi, PNAS 2007. Presentation paper. May 4. Pengfei Yu.
    Required reading: Modeling co-expression across species for complex traits: insights to the difference of human and mouse embryonic stem cells. PLoS Comp Biol. 2010. Jin Li.  April 8.
    Optional reading: Cross-species analysis of biological networks by Bayesian alignment. PNAS 2006

Software demo: EXPANDER, Seth Kinast, Apr 1.

3. Auto regulation: A network motif.

    3.1.  Randomized networks and Network motifs.

    3.2.  Autoregulation: a network motif.

    3.3.  Negative autoregulation.

    3.4.  Positive autoregulation and bi-stability. Required reading: Transcriptional Dynamics of the Embryonic Stem Cell Switch. PloS Comp Biol. 2006. Presentation paper. Mark Russell. April 22.

4. Feed forward loops (FFL).

    4.1. Network motifs - definition and characteristics.

    4.2. The structure of FFL gene circuit.

    4.3. Dynamics of the coherent Type-1 FFL with AND logic.

    4.4. Detailed dynamics analysis of coherent FFL.

    4.5. An incoherent FFL.

    Optional reading: Noise Can Induce Bimodality in Positive Transcriptional Feedback Loops Without Bistability. Science 2010. Yuliang Wang. April 15.

    Required reading: Defining Network Topologies that Can Achieve Biochemical Adaptation. C Tang. Cell, 2009. Presentation by Giray Enkavi & Jing Li.  April 29.

5. Network motifs in developmental setting.

    5.1. Developmental TNs. Required reading: Systems biology of stem cell fate and cellular reprogramming. Nature Reviews Molecular Cell Biology 10, 672-681.    

    Optional reading: Direct cell reprogramming is a stochastic process amenable to acceleration. Nature 462, 595-601.

    5.2. Interlocked FFLs.