Instructor: Roded
Sharan
Time: Tuesday 13-16
The syllabus for the course is here
A recommended book on biological networks is: "Biological networks" by Kepes. 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 |
22 Feb |
Molecular biology of the cell, Alberts et al. |
|
2 |
1 Mar |
Tutorials; |
|
3 |
8
Mar |
Protein-protein
interaction networks : data processing & functional annotation |
PPI preprocessing papers; Functional annotation papers |
4 |
15 Mar |
Integer Linear Programming |
|
5 |
22 Mar |
(Shiur Halshlama
on 26.4) Network querying |
|
6 |
29 Mar |
||
7 |
5 Apr |
||
8 |
12 Apr |
||
9 |
3 May |
||
10 |
17 May |
Networks
in drug development |
|
11 |
24 May |
||
12 |
31 May |
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.
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.