Instructor: Roded Sharan
Time/place: Thursday 15-18, Kaplun 118
The syllabus for the course is here
A recommended book on networks is: ‘Evolution of Networks’ by Dorogovstev and Mendes. A recommended book on introduction to biology is 'Molecular Biology of the Cell' by Alberts et al.
Scribe instructions can be found here
Following is a tentative outline of the course, which will be updated according to what is actually studied in class:
|
Week |
Date |
Lecture scribes |
Read more (refs. are below) |
|
1 |
26 Oct |
Molecular biology of the cell, Alberts et al. |
|
|
2 |
2 Nov |
Chapter 3 & tutorials; Newman'01 |
|
|
3 |
9 Nov |
||
|
4 |
16 Nov |
Network motif papers |
|
|
5 |
23
Nov |
Clustering & biclustering papers |
|
|
6 |
30
Nov |
Network
modules (II): color coding Guest lecture by Eitan Hirsh |
|
|
7 |
7
Dec |
Protein-protein
interaction networks (I): data preprocessing |
PPI preprocessing papers |
|
8 |
14 Dec |
Protein-protein
interaction networks (II): functional annotation |
Functional annotation papers |
|
9 |
21 Dec |
Protein-protein
interaction networks (III): network alignment |
Kelley'03; Sharan'05a; Sharan'05b; Koyuturk'05; Bandyopadhyay'05 |
|
10 |
28 Dec |
Project assignments |
|
|
11 |
4 Jan |
||
|
12 |
11 Jan |
||
|
13 |
18 Jan |
Yeger-Lotem'04; Zhang'05; Tan'07; Gunsalus'05; Yeang'04; Yeang'05; Luscombe'04 |
|
|
14 |
25 Jan |
Project presentations |
|
Reviews/tutorials on Biological Networks
1. N. Pruzlj et al. Graph theory analysis of protein-protein interactions. In "Knowledge discovery in proteomics", edited by Igor Jurisica and Dennis Wigle, CRC Press, 2005.
2. A.-L. Barabsi and Z.N. Oltvai. Network biology: understanding the cell's functional organization. Nat. Rev. Genet. 5:101-113, 2004.
3. M.E.J. Newman. The
structure and function of complex networks.
4. R. Albert and A.-L. Barabasi. Statistical mechanics of complex networks. Rev. Modern Phys. 74:47-97, 2002.
Network motifs
1. S. Shen-Orr et al. Network motifs in the transcriptional regulation of E. Coli. Nat. Genet. 31:64-8, 2002.
2. R.
3. R. Milo et al. Superfamilies of evolved and designed networks. Science 303:1538-42, 2003.
4. S. Itzkovitz et al. Subgraphs in random networks. Phys. Rev. E 68:026127, 2003.
5. N. Kashtan et al. Efficient sampling algorithms for estimating subgraph concentrations and detecting network motifs. Bioinformatics 20:1746-58, 2004.
6. S. Wuchty et al. Evolutionary conservation of motif constituents in the yeast protein interaction network. Nat. Genet. 35:176-9, 2003.
7. G.C. Conant and A. Wagner. Convergent evolution of gene circuits. Nat. Genet. 34:264-6, 2003.
Clustering and Biclustering
1. R. Shamir and R. Sharan. Algorithmic approaches to clustering gene expression data. Current Topics in Computational Biology, T. Jiang, T. Smith, Y. Xu, M.Q. Zhang eds., MIT Press, pp. 269-299, 2002.
2. A. Tanay, R. Sharan and R. Shamir. Biclustering algorithms: a
survey. In Handbook of Computational Molecular Biology, Edited by Srinivas
Aluru, Chapman & Hall/CRC, Computer and Information Science Series,
3. R. Sharan, A. Maron-Katz and R. Shamir. CLICK and EXPANDER: A System for Clustering and Visualizing Gene Expression Data. Bioinformatics 19:1787-99, 2003.
4. A. Tanay, R. Sharan and R. Shamir. Discovering Statistically Significant Patterns in Gene Expression Data. Bioinformatics 18 (Suppl. 1):136-144, 2002.
5. A. Tanay, R. Sharan, M. Kupiec and R. Shamir. Revealing Modularity and Organization in the Yeast Molecular Network by Integrated Analysis of Highly Heterogeneous Genome-wide Data. PNAS 101:2981-6, 2004.
6. Y. Cheng and
PPI Preprocessing
1. C. von Mering et al. Comparative assessment of large-scale data sets of protein-protein interactions. Nature 417: 399-403, 2002.
2. G.D. Bader & C.W.V. Hogue. Analyzing protein-protein interaction data obtained from different sources. Nature Biotech. 20:991-7, 2002.
3. C.M. Deane et al. PPIs: Two methods for assessment of the reliability of high throughput observations. Molecular and Cellular Proteomics 1:349-56, 2002.
4. L.R. Matthews et al. Identification of potential interaction networks using sequence-based searches for conserved PPIs or "Interologs". Genome Res. 11:2120-6, 2001.
5. A. Grigoriev. On the number of PPIs in the yeast proteome. NAR 31:4157-61, 2003.
6. D.S. Goldberg & F.P. Roth. Assessing experimentally derived interactions in a small world. PNAS 100:4372-6, 2003.
7. M. Deng et al. Assessment of the reliability of PPIs and protein function prediction. PSB 140-51, 2003.
8. J.S. Bader et al. Gaining confidence in high-throughput PPI networks. Nature Biotech. 22:78-85, 2004.
9. S. Suthram et al. Comparison of PPI confidence schemes. Proc. First Annual RECOMB Systems Biology Workshop, 2005.
Functional Annotation
1. B. Schwikowski et al. A network of PPIs in yeast. Nature Biotech. 18:1257-61, 2000.
2. H. Hishigaki et al. Assessment of prediction accuracy of protein function from PPI data. Yeast 18:523-31, 2001.
3. A. Vazquez et al. Global protein function prediction from PPI networks. Nature Biotech. 21:697-700, 2003.
4. M. Deng et al. Prediction of protein function using PPI data. JCB 10:947-60, 2003.
5. M. Deng et al. PPI Preprocessing papers, article 7.
6. S. Letovsky and S. Kasif. Predicting protein function from PPI data: a probabilistic approach. Bioinformatics 19(Suppl. 1):197-204, 2003.
7. U. Karaoz et al. Whole genome annotation by using evidence integration in functional linkage networks. PNAS 101:2888-93, 2004.
8. E. Nabieva et al. Whole proteome prediction of protein function via graph theoretic analysis of interaction maps. Bioinformatics 21(Suppl. 1):302-10, 2005.
9. A. Tanay et al. Clustering and Biclustering papers, article 5.