About the course

Syllabus

Schedule

Aug 26 Introduction
Sep 2 No class
Sep 9 Topics, projects and schedule
Sep 16 Diagnostic Genes
Sep 23 Disease Classification
Sep 30 Outcome Prediction Part I
Oct 7 Comparative Systems Biology
Oct 14 Chemical Genomics and Pharmacogenomics
Oct 21 Whole-genome functional annotation
Oct 28 Mid-term project reviews
Nov 4 Stem Cells
Outcome Prediction Part II
Nov 11 Malaria
RNA interference Part I
Nov 18 Genome variation and disease
RNA interference Part II
Nov 25 No class
Dec 2 Gene and literature data sets
Dec 9 Project presentations
Dec 16 Project presentations

Useful links

CS 6104: Advanced Topics Course on Systems Biology and Drug Discovery

T. M. Murali

Fall 2004, Thursdays, 5-7:45pm, McBryde 316

One page description of the course

Papers to be covered

Introduction to Systems Biology and Drug Discovery

• Systems biology--the new R&D buzzword? Bonetta, L. 2002. Nat Med, 8(4):315-6.
• Drug discovery: the leading edge. Chapman, T. 2004. Nature, 430(6995):109-15.
• Advancing drug discovery through systems biology. Davidov, E. and Holland, J. and Marple, E. and Naylor, S. 2003. Drug Discov Today, 8(4):175-83.
• Drug discovery: a historical perspective. Drews, J. 2000. Science, 287(5460):1960-4.
• Strategic trends in the drug industry. Drews, J. 2003. Drug Discov Today, 8(9):411-20.
• Biosequence exegesis. Boguski, M.S. 1999. Science, 286(5439):453-5.
• At the interfaces of epidemiology, genetics and genomics. Potter, J.D. 2001. Nat Rev Genet, 2(2):142-7.

Finding Diagnostic Genes Using Gene Expression Data

• Analysing differential gene expression in cancer. Liang, P. and Pardee, A.B. 2003. Nat Rev Cancer, 3(11):869-76.
• Gene selection in microarray data: the elephant, the blind men and our algorithms. Stolovitzky, G. 2003. Curr Opin Struct Biol, 13(3):370-6.
• RankGene: identification of diagnostic genes based on expression data. Su, Y. and Murali, T.M. and Pavlovic, V. and Schaffer, M. and Kasif, S. 2003. Bioinformatics, 19(12):1578-9.
• PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Mootha, V.K. and Lindgren, C.M. and Eriksson, K.F. and Subramanian, A. and Sihag, S. and Lehar, J. and Puigserver, P. and Carlsson, E. and Ridderstrale, M. and Laurila, E. and Houstis, N. and Daly, M.J. and Patterson, N. and Mesirov, J.P. and Golub, T.R. and Tamayo, P. and Spiegelman, B. and Lander, E.S. and Hirschhorn, J.N. and Altshuler, D. and Groop, L.C. 2003. Nat Genet, 34(3):267-73.
• A molecular signature of metastasis in primary solid tumors. Ramaswamy, S. and Ross, K.N. and Lander, E.S. and Golub, T.R. 2003. Nat Genet, 33(1):49-54.
• Metagenes and molecular pattern discovery using matrix factorization. Brunet, J.P. and Tamayo, P. and Golub, T.R. and Mesirov, J.P. 2004. Proc Natl Acad Sci U S A, 101(12):4164-9.
• Rules of evidence for cancer molecular-marker discovery and validation. Ransohoff, D.F. 2004. Nat Rev Cancer, 4(4):309-14.
• Genetics and medicine. Putting gene arrays to the test. Branca, M. 2003. Science, 300(5617):238.
• Medicine: profile of a tumour. Kallioniemi, O. 2004. Nature, 428(6981):379-82.

Stem Cells and Stemness

• ``Stemness'': transcriptional profiling of embryonic and adult stem cells. Ramalho-Santos, M. and Yoon, S. and Matsuzaki, Y. and Mulligan, R.C. and Melton, D.A. 2002. Science, 298(5593):597-600.
• A stem cell molecular signature. Ivanova, N.B. and Dimos, J.T. and Schaniel, C. and Hackney, J.A. and Moore, K.A. and Lemischka, I.R. 2002. Science, 298(5593):601-4.
• Portrait of a stem cell. Burns, C.E. and Zon, L.I. 2002. Dev Cell, 3(5):612-3.
• Comment on ``'Stemness': transcriptional profiling of embryonic and adult stem cells'' and ``A stem cell molecular signature''. Fortunel, N.O. and Otu, H.H. and Ng, H.H. and Chen, J. and Mu, X. and Chevassut, T. and Li, X. and Joseph, M. and Bailey, C. and Hatzfeld, J.A. and Hatzfeld, A. and Usta, F. and Vega, V.B. and Long, P.M. and Libermann, T.A. and Lim, B. 2003. Science, 302(5644):393; author reply 393.
• Comment on `` 'Stemness': transcriptional profiling of embryonic and adult stem cells'' and ``A stem cell molecular signature''. Evsikov, A.V. and Solter, D. 2003. Science, 302(5644):393; author reply 393.
• Response to Comments on `` 'Stemness': Transcriptional Profiling of Embryonic and Adult Stem Cells'' and ``A Stem Cell Molecular Signature'' Ivanova, Natalia B. and Dimos, John T. and Schaniel, Christoph and Hackney, Jason A. and Moore, Kateri A. and Ramalho-Santos, Miguel and Yoon, Soonsang and Matsuzaki, Yumi and Mulligan, Richard C. and Melton, Douglas A. and Lemischka, Ihor R. 2003. Science, 302(5644):393d-.
• Stem cells. 'Stemness' genes still elusive. Vogel, G. 2003. Science, 302(5644):371.
• Chipping away at 'stemness'. Pyle, A.D. and Donovan, P.J. and Lock, L.F. 2004. Genome Biol, 5(8):235.
• In search of ``stemness''. Cai, J. and Weiss, M.L. and Rao, M.S. 2004. Exp Hematol, 32(7):585-98.
• The transcriptome profile of human embryonic stem cells as defined by SAGE. Richards, M. and Tan, S.P. and Tan, J.H. and Chan, W.K. and Bongso, A. 2004. Stem Cells, 22(1):51-64.
• Gene expression in human embryonic stem cell lines: unique molecular signature. Bhattacharya, B. and Miura, T. and Brandenberger, R. and Mejido, J. and Luo, Y. and Yang, A.X. and Joshi, B.H. and Ginis, I. and Thies, R.S. and Amit, M. and Lyons, I. and Condie, B.G. and Itskovitz-Eldor, J. and Rao, M.S. and Puri, R.K. 2004. Blood, 103(8):2956-64.

Disease Classification Using Gene Expression Data

• Class struggle: expression profiling and categorizing cancer. Covitz, P.A. 2003. Pharmacogenomics J, 3(5):257-60.
• Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Golub, T.R. and Slonim, D.K. and Tamayo, P. and Huard, C. and Gaasenbeek, M. and Mesirov, J.P. and Coller, H. and Loh, M.L. and Downing, J.R. and Caligiuri, M.A. and Bloomfield, C.D. and Lander, E.S. 1999. Science, 286(5439):531-7.
• Knowledge-based analysis of microarray gene expression data by using support vector machines. Brown, M.P. and Grundy, W.N. and Lin, D. and Cristianini, N. and Sugnet, C.W. and Furey, T.S. and Ares, Jr, M. and Haussler, D. 2000. Proc Natl Acad Sci U S A, 97(1):262-7.
• Multiclass cancer diagnosis using tumor gene expression signatures. Ramaswamy, S. and Tamayo, P. and Rifkin, R. and Mukherjee, S. and Yeang, C.H. and Angelo, M. and Ladd, C. and Reich, M. and Latulippe, E. and Mesirov, J.P. and Poggio, T. and Gerald, W. and Loda, M. and Lander, E.S. and Golub, T.R. 2001. Proc Natl Acad Sci U S A, 98(26):15149-54.
• Molecular classification of multiple tumor types. Yeang, C.H. and Ramaswamy, S. and Tamayo, P. and Mukherjee, S. and Rifkin, R.M. and Angelo, M. and Reich, M. and Lander, E. and Mesirov, J. and Golub, T. 2001. Bioinformatics, 17 Suppl 1:S316-22.
• Analysis of gene expression microarrays for phenotype classification. Califano, A. and Stolovitzky, G. and Tu, Y. 2000. Proc Int Conf Intell Syst Mol Biol, 8:75-85. This paper discusses applying a pattern-discovery algorithm called SPLASH initially developed for finding patterns in strings to gene expression data. You will need to read the SPLASH paper to understand this paper.
• Genes@Work: an efficient algorithm for pattern discovery and multivariate feature selection in gene expression data. Lepre, J. and Rice, J.J. and Tu, Y. and Stolovitzky, G. 2004. Bioinformatics, 20(7):1033-44.
• DNA microarrays in clinical oncology. Ramaswamy, S. and Golub, T.R. 2002. J Clin Oncol, 20(7):1932-41.
• Estimating dataset size requirements for classifying DNA microarray data. Mukherjee, S. and Tamayo, P. and Rogers, S. and Rifkin, R. and Engle, A. and Campbell, C. and Golub, T.R. and Mesirov, J.P. 2003. J Comput Biol, 10(2):119-42.

Outcome Prediction and Personalised Medicine

• Special Issues of Nature Reviews Genetics and Nature Reviews Drug Discovery on pharacogenomics.
• Moving towards individualized medicine with pharmacogenomics. Evans, W.E. and Relling, M.V. 2004. Nature, 429(6990):464-8.
• Medicine. Why a new cancer drug works well, in some patients. Marx, J. 2004. Science, 304(5671):658-9.
• Semi-supervised methods to predict patient survival from gene expression data. Blair, E. and Tibshirani, R. 2004. PLoS Biol, 2(4):E108.
• Diffuse large B-cell lymphoma outcome prediction by gene-expression profiling and supervised machine learning. Shipp, M.A. and Ross, K.N. and Tamayo, P. and Weng, A.P. and Kutok, J.L. and Aguiar, R.C. and Gaasenbeek, M. and Angelo, M. and Reich, M. and Pinkus, G.S. and Ray, T.S. and Koval, M.A. and Last, K.W. and Norton, A. and Lister, T.A. and Mesirov, J. and Neuberg, D.S. and Lander, E.S. and Aster, J.C. and Golub, T.R. 2002. Nat Med, 8(1):68-74.
• Prediction of central nervous system embryonal tumour outcome based on gene expression. Pomeroy, S.L. and Tamayo, P. and Gaasenbeek, M. and Sturla, L.M. and Angelo, M. and McLaughlin, M.E. and Kim, J.Y. and Goumnerova, L.C. and Black, P.M. and Lau, C. and Allen, J.C. and Zagzag, D. and Olson, J.M. and Curran, T. and Wetmore, C. and Biegel, J.A. and Poggio, T. and Mukherjee, S. and Rifkin, R. and Califano, A. and Stolovitzky, G. and Louis, D.N. and Mesirov, J.P. and Lander, E.S. and Golub, T.R. 2002. Nature, 415(6870):436-42.
• Gene expression-based, individualized outcome prediction for surgically treated lung cancer patients. Tomida, S. and Koshikawa, K. and Yatabe, Y. and Harano, T. and Ogura, N. and Mitsudomi, T. and Some, M. and Yanagisawa, K. and Takahashi, T. and Osada, H. and Takahashi, T. 2004. Oncogene, 23(31):5360-70.
• Tests for finding complex patterns of differential expression in cancers: towards individualized medicine. Lyons-Weiler, J. and Patel, S. and Becich, M.J. and Godfrey, T.E. 2004. BMC Bioinformatics, 5(1):110.
• The microarray way to tailored cancer treatment. Van 't Veer, L.J. and De Jong, D. 2002. Nat Med, 8(1):13-4.
• Integrated modeling of clinical and gene expression information for personalized prediction of disease outcomes. Pittman, J. and Huang, E. and Dressman, H. and Horng, C.F. and Cheng, S.H. and Tsou, M.H. and Chen, C.M. and Bild, A. and Iversen, E.S. and Huang, A.T. and Nevins, J.R. and West, M. 2004. Proc Natl Acad Sci U S A, 101(22):8431-6.
• Papers on Active Model Selection by Russ Alberts.

Comparative Systems Biology

• A gene-coexpression network for global discovery of conserved genetic modules. Stuart, J.M. and Segal, E. and Koller, D. and Kim, S.K. 2003. Science, 302(5643):249-55.
• Similarities and differences in genome-wide expression data of six organisms. Bergmann, S. and Ihmels, J. and Barkai, N. 2004. PLoS Biol, 2(1):E9.
• Coexpression analysis of human genes across many microarray data sets. Lee, H.K. and Hsu, A.K. and Sajdak, J. and Qin, J. and Pavlidis, P. 2004. Genome Res, 14(6):1085-94.
• Large-scale meta-analysis of cancer microarray data identifies common transcriptional profiles of neoplastic transformation and progression. Rhodes, D.R. and Yu, J. and Shanker, K. and Deshpande, N. and Varambally, R. and Ghosh, D. and Barrette, T. and Pandey, A. and Chinnaiyan, A.M. 2004. Proc Natl Acad Sci U S A, 101(25):9309-14.
• ONCOMINE: a cancer microarray database and integrated data-mining platform. Rhodes, D.R. and Yu, J. and Shanker, K. and Deshpande, N. and Varambally, R. and Ghosh, D. and Barrette, T. and Pandey, A. and Chinnaiyan, A.M. 2004. Neoplasia, 6(1):1-6.
• A neutral model of transcriptome evolution. Khaitovich, P. and Weiss, G. and Lachmann, M. and Hellmann, I. and Enard, W. and Muetzel, B. and Wirkner, U. and Ansorge, W. and Paabo, S. 2004. PLoS Biol, 2(5):E132.

Chemical Genomics and Pharmacogenomics

• Pharmacogenomics--drug disposition, drug targets, and side effects. Evans, W.E. and McLeod, H.L. 2003. N Engl J Med, 348(6):538-49.
• Chemogenomics: an emerging strategy for rapid target and drug discovery. Bredel, M. and Jacoby, E. 2004. Nat Rev Genet, 5(4):262-75.
• Treatment-specific changes in gene expression discriminate in vivo drug response in human leukemia cells. Cheok, M.H. and Yang, W. and Pui, C.H. and Downing, J.R. and Cheng, C. and Naeve, C.W. and Relling, M.V. and Evans, W.E. 2003. Nat Genet, 34(1):85-90.
• Mining the genome for combination therapies. Golub, T.R. 2003. Nat Med, 9(5):510-1.
• Gene expression-based high-throughput screening (GE-HTS) and application to leukemia differentiation. Stegmaier, K. and Ross, K.N. and Colavito, S.A. and O'Malley, S. and Stockwell, B.R. and Golub, T.R. 2004. Nat Genet, 36(3):257-63.
• Gene expression as a drug discovery tool. Evans, W.E. and Guy, R.K. 2004. Nat Genet, 36(3):214-5.
• Chemical genomics: a systematic approach in biological research and drug discovery. Zheng, X.F. and Chan, T.F. 2002. Curr Issues Mol Biol, 4(2):33-43.
• Chemical genomics in the global study of protein functions. Zheng, X.F. and Chan, T.F. 2002. Drug Discov Today, 7(3):197-205.
• Chemosensitivity prediction by transcriptional profiling. Staunton, J.E. and Slonim, D.K. and Coller, H.A. and Tamayo, P. and Angelo, M.J. and Park, J. and Scherf, U. and Lee, J.K. and Reinhold, W.O. and Weinstein, J.N. and Mesirov, J.P. and Lander, E.S. and Golub, T.R. 2001. Proc Natl Acad Sci U S A, 98(19):10787-92.
• Prediction of clinical drug efficacy by classification of drug-induced genomic expression profiles in vitro. Gunther, E.C. and Stone, D.J. and Gerwien, R.W. and Bento, P. and Heyes, M.P. 2003. Proc Natl Acad Sci U S A, 100(16):9608-13.
• An integrative biology approach for analysis of drug action in models of human vascular inflammation. Kunkel, E.J. and Dea, M. and Ebens, A. and Hytopoulos, E. and Melrose, J. and Nguyen, D. and Ota, K.S. and Plavec, I. and Wang, Y. and Watson, S.R. and Butcher, E.C. and Berg, E.L. 2004. FASEB J, 18(11):1279-81.

Whole-Genome Functional Annotation

• Applying Support Vector Machines for Gene ontology based gene function prediction. Vinayagam, A. and Koenig, R. and Moormann, J. and Schubert, F. and Eils, R. and Glatting, K.H. and Suhai, S. 2004. BMC Bioinformatics, 5(1):116.
• Kernel-based data fusion and its application to protein function prediction in yeast. Lanckriet, G.R. and Deng, M. and Cristianini, N. and Jordan, M.I. and Noble, W.S. 2004. Pac Symp Biocomput, pages 300-11.
• Mapping Gene Ontology to proteins based on protein-protein interaction data. Deng, M. and Tu, Z. and Sun, F. and Chen, T. 2004. Bioinformatics, 20(6):895-902.
• Learning gene functional classifications from multiple data types. Pavlidis, P. and Weston, J. and Cai, J. and Noble, W.S. 2002. J Comput Biol, 9(2):401-11.
• An integrated probabilistic model for functional prediction of proteins. Deng, M. and Chen, T. and Sun, F. 2004. J Comput Biol, 11(2-3):463-75.
• Whole-genome annotation by using evidence integration in functional-linkage networks. Karaoz, U. and Murali, T.M. and Letovsky, S. and Zheng, Y. and Ding, C. and Cantor, C.R. and Kasif, S. 2004. Proc Natl Acad Sci U S A, 101(9):2888-93.
• Predicting protein function from protein/protein interaction data: a probabilistic approach. Letovsky, S. and Kasif, S. 2003. Bioinformatics, 19 Suppl 1:i197-204.
• Prediction of protein function using protein-protein interaction data. Deng, M. and Zhang, K. and Mehta, S. and Chen, T. and Sun, F. 2003. J Comput Biol, 10(6):947-60.

RNA Interference

• Surveys on RNAi
  • The RNAi revolution. Novina, C.D. and Sharp, P.A. 2004. Nature, 430(6996):161-4.
  • siRNAs: applications in functional genomics and potential as therapeutics. Dorsett, Y. and Tuschl, T. 2004. Nat Rev Drug Discov, 3(4):318-29.
  • Dissecting HIV-1 through RNA interference. Stevenson, M. 2003. Nat Rev Immunol, 3(11):851-8.
  • New tools for functional mammalian cancer genetics. Brummelkamp, T.R. and Bernards, R. 2003. Nat Rev Cancer, 3(10):781-9.
  • RNA regulation: a new genetics? Mattick, J.S. 2004. Nat Rev Genet, 5(4):316-23.
  • Killing the messenger: short RNAs that silence gene expression. Dykxhoorn, D.M. and Novina, C.D. and Sharp, P.A. 2003. Nat Rev Mol Cell Biol, 4(6):457-67.
  • RNA interference. Hannon, G.J. 2002. Nature, 418(6894):244-51.
  • The brave new world of RNA. Dennis, C. 2002. Nature, 418(6894):122-4.
  • Exploring plant genomes by RNA-induced gene silencing. Waterhouse, P.M. and Helliwell, C.A. 2003. Nat Rev Genet, 4(1):29-38.
  • Stealth regulation: biological circuits with small RNA switches. Gottesman, S. 2002. Genes Dev, 16(22):2829-42.
• MicroRNAs
  • A combined computational-experimental approach predicts human microRNA targets. Kiriakidou, M. and Nelson, P.T. and Kouranov, A. and Fitziev, P. and Bouyioukos, C. and Mourelatos, Z. and Hatzigeorgiou, A. 2004. Genes Dev, 18(10):1165-78.
  • MicroRNA targets in Drosophila. Enright, A.J. and John, B. and Gaul, U. and Tuschl, T. and Sander, C. and Marks, D.S. 2003. Genome Biol, 5(1):R1.
  • Human MicroRNA Targets Bino John, Anton J. Enright, Alexei Aravin, Thomas Tuschl, Chris Sander, Debora S. Marks. PLOS Biology. 2(11):
  • Vertebrate microRNA genes. Lim, L.P. and Glasner, M.E. and Yekta, S. and Burge, C.B. and Bartel, D.P. 2003. Science, 299(5612):1540.
  • Computational identification of microRNA targets. Rajewsky, N. and Socci, N.D. 2004. Dev Biol, 267(2):529-35.
  • Identification of Drosophila MicroRNA Targets. Stark, A. and Brennecke, J. and Russell, R.B. and Cohen, S.M. 2003. PLoS Biol, 1(3):E60.
  • Computational identification of Drosophila microRNA genes. Lai, E.C. and Tomancak, P. and Williams, R.W. and Rubin, G.M. 2003. Genome Biol, 4(7):R42.
  • Prediction of mammalian microRNA targets. Lewis, B.P. and Shih, I.H. and Jones-Rhoades, M.W. and Bartel, D.P. and Burge, C.B. 2003. Cell, 115(7):787-98.
  • Prediction of plant microRNA targets. Rhoades, M.W. and Reinhart, B.J. and Lim, L.P. and Burge, C.B. and Bartel, B. and Bartel, D.P. 2002. Cell, 110(4):513-20.
  • A uniform system for microRNA annotation. Ambros, V. and Bartel, B. and Bartel, D.P. and Burge, C.B. and Carrington, J.C. and Chen, X. and Dreyfuss, G. and Eddy, S.R. and Griffiths-Jones, S. and Marshall, M. and Matzke, M. and Ruvkun, G. and Tuschl, T. 2003. RNA, 9(3):277-9.
  • The microRNA Registry. Griffiths-Jones, S. 2004. Nucleic Acids Res, 32 Database issue:D109-11.
• Genome-wide RNAi assays
  • A resource for large-scale RNA-interference-based screens in mammals. Paddison, P.J. and Silva, J.M. and Conklin, D.S. and Schlabach, M. and Li, M. and Aruleba, S. and Balija, V. and O'Shaughnessy, A. and Gnoj, L. and Scobie, K. and Chang, K. and Westbrook, T. and Cleary, M. and Sachidanandam, R. and McCombie, W.R. and Elledge, S.J. and Hannon, G.J. 2004. Nature, 428(6981):427-31.
  • Systematic functional analysis of the Caenorhabditis elegans genome using RNAi. Kamath, R.S. and Fraser, A.G. and Dong, Y. and Poulin, G. and Durbin, R. and Gotta, M. and Kanapin, A. and Le Bot, N. and Moreno, S. and Sohrmann, M. and Welchman, D.P. and Zipperlen, P. and Ahringer, J. 2003. Nature, 421(6920):231-7.
  • Genome-wide RNAi screening in Caenorhabditis elegans. Kamath, R.S. and Ahringer, J. 2003. Methods, 30(4):313-21.
  • Genome-wide RNAi analysis of Caenorhabditis elegans fat regulatory genes. Ashrafi, K. and Chang, F.Y. and Watts, J.L. and Fraser, A.G. and Kamath, R.S. and Ahringer, J. and Ruvkun, G. 2003. Nature, 421(6920):268-72.
  • A large-scale RNAi screen in human cells identifies new components of the p53 pathway. Berns, K. and Hijmans, E.M. and Mullenders, J. and Brummelkamp, T.R. and Velds, A. and Heimerikx, M. and Kerkhoven, R.M. and Madiredjo, M. and Nijkamp, W. and Weigelt, B. and Agami, R. and Ge, W. and Cavet, G. and Linsley, P.S. and Beijersbergen, R.L. and Bernards, R. 2004. Nature, 428(6981):431-7.
  • RNA interference microarrays: high-throughput loss-of-function genetics in mammalian cells. Silva, J.M. and Mizuno, H. and Brady, A. and Lucito, R. and Hannon, G.J. 2004. Proc Natl Acad Sci U S A, 101(17):6548-52.
  • Genome-wide RNAi analysis of growth and viability in Drosophila cells. Boutros, M. and Kiger, A.A. and Armknecht, S. and Kerr, K. and Hild, M. and Koch, B. and Haas, S.A. and Consortium, H.F. and Paro, R. and Perrimon, N. 2004. Science, 303(5659):832-5.
  • Silence of the genes. Singer, O. and Yanai, A. and Verma, I.M. 2004. Proc Natl Acad Sci U S A, 101(15):5313-4.

Proteomics

• Proteomic diagnostics tested. 2004. Nature, 429(6991):487.
• Proteomics and cancer: running before we can walk? Check, E. 2004. Nature, 429(6991):496-7.

Integrated Analysis of Data

• To infinity, and beyond: uniting the galaxy of biological data. Covitz, P.A. 2003. OMICS, 7(1):21-2.
• Identification of a gene causing human cytochrome c oxidase deficiency by integrative genomics. Mootha, V.K. and Lepage, P. and Miller, K. and Bunkenborg, J. and Reich, M. and Hjerrild, M. and Delmonte, T. and Villeneuve, A. and Sladek, R. and Xu, F. and Mitchell, G.A. and Morin, C. and Mann, M. and Hudson, T.J. and Robinson, B. and Rioux, J.D. and Lander, E.S. 2003. Proc Natl Acad Sci U S A, 100(2):605-10.
• Samir Hanash article on intergated cancer profiling.

Literature-Based Datasets

• The genetic association database. Becker, K.G. and Barnes, K.C. and Bright, T.J. and Wang, S.A. 2004. Nat Genet, 36(5):431-2.
• Building global networks for human diseases: genes and populations. Hagen, H.E. and Carlstedt-Duke, J. 2004. Nat Med, 10(7):665-7.
• A gene network for navigating the literature. Hoffmann, R. and Valencia, A. 2004. Nat Genet, 36(7):664.
• MEDGENE-related papers.
• Bioinformatics: data's future shock. Buckingham, S. 2004. Nature, 428(6984):774-7.

Genome Variation and Disease

• Pharmacogenetics and genotyping: on the trail of SNPs. Melton, L. 2003. Nature, 422(6934):917, 919, 921, 923.
• Kaleigh Smith review of computational problem on SNPs.
• Chromosome aberrations in solid tumors. Albertson, D.G. and Collins, C. and McCormick, F. and Gray, J.W. 2003. Nat Genet, 34(4):369-76.
• Chromosome aberrations in solid tumors. Albertson, D.G. and Collins, C. and McCormick, F. and Gray, J.W. 2003. Nat Genet, 34(4):369-76.
• Fusion genes and rearranged genes as a linear function of chromosome aberrations in cancer. Mitelman, F. and Johansson, B. and Mertens, F. 2004. Nat Genet, 36(4):331-4.
• Mapping complex disease loci in whole-genome association studies. Carlson, C.S. and Eberle, M.A. and Kruglyak, L. and Nickerson, D.A. 2004. Nature, 429(6990):446-52.
• Structural location of disease-associated single-nucleotide polymorphisms. Stitziel, N.O. and Tseng, Y.Y. and Pervouchine, D. and Goddeau, D. and Kasif, S. and Liang, J. 2003. J Mol Biol, 327(5):1021-30.
• topoSNP: a topographic database of non-synonymous single nucleotide polymorphisms with and without known disease association. Stitziel, N.O. and Binkowski, T.A. and Tseng, Y.Y. and Kasif, S. and Liang, J. 2004. Nucleic Acids Res, 32 Database issue:D520-2.

Malaria

• Antimalarial drug discovery: efficacy models for compound screening. Fidock, D.A. and Rosenthal, P.J. and Croft, S.L. and Brun, R. and Nwaka, S. 2004. Nat Rev Drug Discov, 3(6):509-20.
• Discovery of gene function by expression profiling of the malaria parasite life cycle. Le Roch, K.G. and Zhou, Y. and Blair, P.L. and Grainger, M. and Moch, J.K. and Haynes, J.D. and De La Vega, P. and Holder, A.A. and Batalov, S. and Carucci, D.J. and Winzeler, E.A. 2003. Science, 301(5639):1503-8.
• Proteomics in malaria. Johnson, J.R. and Florens, L. and Carucci, D.J. and Yates, 3rd, J.R. 2004. J Proteome Res, 3(2):296-306.
• Analysis of the Plasmodium falciparum proteome by high-accuracy mass spectrometry. Lasonder, E. and Ishihama, Y. and Andersen, J.S. and Vermunt, A.M. and Pain, A. and Sauerwein, R.W. and Eling, W.M. and Hall, N. and Waters, A.P. and Stunnenberg, H.G. and Mann, M. 2002. Nature, 419(6906):537-42.
• Quantitative proteomics of the human malaria parasite Plasmodium falciparum and its application to studies of development and inhibition. Nirmalan, N. and Sims, P.F. and Hyde, J.E. 2004. Mol Microbiol, 52(4):1187-99.
• Progress and challenges for malaria vaccines. Richie, T.L. and Saul, A. 2002. Nature, 415(6872):694-701.
• Winning the drugs war. Ridley, R. and Toure, Y. 2004. Nature, 430(7002):942-3.
• Plasmodium, human and Anopheles genomics and malaria. Hoffman, S.L. and Subramanian, G.M. and Collins, F.H. and Venter, J.C. 2002. Nature, 415(6872):702-9.
• Medical need, scientific opportunity and the drive for antimalarial drugs. Ridley, R.G. 2002. Nature, 415(6872):686-93.
• Gene expression during the life cycle of Drosophila melanogaster. Arbeitman, M.N. and Furlong, E.E. and Imam, F. and Johnson, E. and Null, B.H. and Baker, B.S. and Krasnow, M.A. and Scott, M.P. and Davis, R.W. and White, K.P. 2002. Science, 297(5590):2270-5.
• An attack on all fronts. Klausner, R. and Alonso, P. 2004. Nature, 430(7002):930-1.
• Taking aim at mosquitoes. Hemingway, J. 2004. Nature, 430(7002):936.
• The immune response of Drosophila. Hoffmann, J.A. 2003. Nature, 426(6962):33-8.

The Biology of Cancer

• The hallmarks of cancer
• Untangling the roots of cancer, Scientific American, July 2003, 56-65.
• A census of human cancer genes. Futreal, P.A. and Coin, L. and Marshall, M. and Down, T. and Hubbard, T. and Wooster, R. and Rahman, N. and Stratton, M.R. 2004. Nat Rev Cancer, 4(3):177-83.
• Surfing the p53 network. Vogelstein, B. and Lane, D. and Levine, A.J. 2000. Nature, 408(6810):307-10.

Useful Links

Research Groups

• systems-biology.org
• Institute for Systems Biology
• Alliance for Cellular Signaling
• John Tyson in the Department of Biology at Virginia Tech.
• Virginia Bioinformatics Institute

Databases

• Kyoto Encyclopaedia of Genes and Genomes (KEGG)
• Signal Transduction Knowledge Environmen (STKE)
• EcoCyc

Software

• Mike Eisen (ScanAlyze, Cluster, and TreeView). Unfortunately, only for Windows.
• Open-source version of Mike Eisen's clustering software by M. J. L. de Hoon and others, with C/C++, Perl, and Python interfaces. Fortunately, for many platforms.
• xMotif, software that runs on GNU/Linux for biclustering DNA microarray data by T. M. Murali and Simon Kasif.
• GeneExpress, Java software for computing module networks from DNA microarray data.
• BioPerl/BioJava, general-purpose software for processing biological data.

Related Courses

• Problems and Perspectives in Computational Molecular Biology, Cornell (CS 726)
• Analysis of Gene Expression Data, DNA Chips and Gene Networks, Spring 2002, Ron Shamir, Tel Avi University.
• Learning and Modeling Biological Networks, Mark Craven, David Page, and Jude Shavlik, Dept. of Computer Science, University of Wisconsin, Madison.
• Seminar on Computational Systems Biology, Eric Mjolness, Dept. of Information and Computer Science, University of California, Irvine.

Journals, Conferences, and Workshops

• International Conference on Systems Biology (ICSB) 2000 2001 2002 2003
• Systems Biology seminar series at VBI.

Data Sets For Course Projects

• DNA microarray data sets
  • Human cancer data sets: a good source is the data sets section of the Cancer Genomics research group at MIT's Whitehead Institute. The following links point to local copies of some of these data sets:
    • ALL-AML: Microarray data, class labels/phenotypes
    • B-cell lymphoma: Microarray data
    • Central Nervous System: Microarray data, class labels/phenotypes
    • Global Cancer Map: Microarray data, class labels/phenotypes
  • Yeast data sets: an excellent source is the Expression Junction section of the Saccharomyces Genome Database. I could not find any direct link to a site containing all the data sets, so you have to click on each individual web supplement to download the data. I have linked to some local copies below.
    • Stress data of Gasch et al.: Microarray data, class labels/phenotypes
    • alpha-factor Rosetta data set: Microarray data, class labels/phenotypes
    • Gene knock-out Rosetta data set: Microarray data, class labels/phenotypes
• Functional annotations: Gene Ontology (GO), KEGG, MIPS
  • Gene Ontology annotations for Saccharomyces cerevisiae
  • Gene Ontology annotations for Homo sapiens
  • KEGG pathway lists
  • MIPS functional categories
• Protein-protein interactions
  • GRID data set
  • GRID data set restricted to interactions reported in at least two publications
• Transcriptional regulatory networks: the files below are not regulatory networks per se but are binary (0-1) matrices that state whether than binding site for a particular transcription factor appears in the promotor region of a gene.
Last modified: Tue Oct 26 17:29:05 EDT 2004