1. Biological Networks & Systems Anne R. Haake Rhys Price Jones

2. Gene Networks "The approach to biology for the past 30 years has been to study individual proteins and genes in isolation. The future will be the study of the genes and proteins of organisms in the context of their informational pathways or networks." Leroy Hood, Director of the Institute for Systems Biology, Nature, Oct. 19, 2000.

3. Gene Networks: Some Examples Genes and their products are related through their roles in: –metabolic pathways –cell signalling networks

4. Metabolic Pathway

5. Cell Signalling Networks www.mpi-dortmund.mpg.de/departments/dep1/signaltransduktion/image3.gif

6. Relating gene expression patterns to functional networks is a complex problem http://industry.ebi.ac.uk/~brazma/Genenets

7. How do we reconstruct networks from gene expression data? Cluster analysis? – similarity in expression pattern suggests possible co- regulation –may be co-expression by coincidence –doesn’t reveal cause-effect relationships Can we get more information out of clusters? –Look for additional evidence of co-regulation to infer relationships among genes

8. –Complete set of genes used to study diauxic shift time course http://home.stat.ubc.ca/~isabella/diauxic.htmlhttp://home.stat.ubc.ca/~isabella/diauxic.html –Cluster analysis of data identified group of genes with similar expression profiles –Upstream regulatory sites of these genes compared to identify transcription factor binding sites –Ref: Brazma A. and Vilo, J:Minireview. Gene expression data analysis. FEBS Letters, 480:17-24, 2000. –http://cgsigma.cshl.org/jian/http://cgsigma.cshl.org/jian/ Yeast genome

9. How else can we use gene expression data? Interpret expression data in context of known pathways/networks Gene Ontology –Categories of information about each gene: Cellular compartment Biological Process Molecular Function Visualization tools help the researcher to put expression results in context

10. Using information networks as an interpretive layer between phenotypes and the underlying genes, proteins and metabolites Highly connected genes are often critical in the onset of cancer and metabolic diseases. However, drug treatment targeting less connected genes will have fewer side effects. Database stores information about the connections among cellular building blocks and traits. DNA chip/microarray Red indicates regions implicated in disease Human Chromosomes 5 & 13 Use network to understand the relationship between genes associated with disease regions. J. Blanchard-CAAGED Workshop 2002

11. Lots of tools available! GenMapp –Gene Microarray Pathway Profiler –www.genmapp.orgwww.genmapp.org

12. Moreover… Biologists want to be able to answer questions about phenotype, about disease, about mechanisms of development, development new drugs….. Understanding systems requires integration of many bodies of “knowledge” For example: “Wet-lab” approaches –Relating expression patterns to networks and systems using in situ hybridization to localize time and place of expression, knock-out experiments to identify downstream network components More examples of software to support integrated approach: PathDB (http://www.ncgr.org/pathdb/demo/demo2.html)http://www.ncgr.org/pathdb/demo/demo2.html

13. Integration of databases and resources Important issue because of large number of distributed databases containing biological data of interest and the heterogeneity of the data. Approaches to integration of databases and resources –data warehouses –multi-database query systems –inter-linked web resources –component-based systems

14. A sampling of Integrated Resources ISYS at NCGR http://www.ncgr.orghttp://www.ncgr.org DAS (Distributed Annotation System) NCBI

15. ISYS NCGR Stanford Berkeley Wash. U Manchester Web Other third party software Your organization’s tools PathDB CMD Tool Table Viewer Sequence Viewer Similarity Search Viewer X-Cluster GO Browser ATV MaxD Entrez - NCBI BLAST - NCBI GeneScan - MIT Google TAIR - NCGR GeneX - NCGR Wash. U ISYS™ is a dynamic, flexible platform for the integration of bioinformatics software tools and databases. ISYS offers a component-based architecture that enables scientists to "plug and play" among tools of interest.

16. http://www.ncgr.org

17. http://www.ncgr.org/isys/ ISYS's DynamicDiscovery ™ technology creates an exploratory environment in which scientists can navigate freely among registered components. DynamicDiscovery helps to guide the user by suggesting appropriate registered components to process selected data objects.

18. Other Analytic Approaches for Inference of Networks Next class!