1. Bioinformatics Needs for the post-genomic era Dr. Erik Bongcam-Rudloff The Linnaeus Centre for Bioinformatics

2. From Egg to Adult in 3x109 Bases A single cell, the fertilized egg, eventually differentiates into the ~300 different types of cells that make up an adult body. With a few exceptions all of these cells contain the complete human genome, but express only a subset of the genes. Gene expression patterns are determined largely by cell type, and vice versa.

3. The “body” has: The genome A comprehensive list of genes Gene expression data Protein localization in the cells Information about Protein/protein and protein/DNA interactions. Ways to store, display and query masses of data so activity can focus on relevant bits.

4. Primary Flows of Information and Substance in the Cell

5. Why a Grid? Growth of Molecular Biological problems is getting out of sync with Moore's Law Growing interest in Bioinformatics from other disciplines New experimental approaches (genomics, proteomics, etc.) require new and more demanding solutions

6. Comparative Genomics Comparative genomics: comparison of whole genomes (e.g. human and mouse) and new techniques for phylogenetic footprinting.

7. Rnomics Rnomics: tertiary structure prediction and novel RNA gene location in whole genomes We are conducting genome wide scans for RNA regulatory elements and RNA genes using state of the art comparative genomics tools. The analysis involves comparison of the human and mouse genomes using tools such as stochastic context-free grammars

8. Molecular Interactions Large scale in silico maps of the molecular interactions over entire proteomes and genomes. These maps provide quantitative functional models that bridge the biological with the chemical.

9. We are developing models of gene participation in biological processes. Such models are developed from microarray-based gene expressions and background knowledge, e.g. as provided by the so- called Gene Ontology. The GRID Test Bed will be an excellent computational environment for finding molecular classifiers associated with e.g. major diseases such as, for instance, cancer, artherosclerosis and other diseases that kill many people in Europe.

10. What is needed? Standard, stable interfaces to conceptual problem solvers / data / objects A distributed way to store and analyse information Security for user data Avoiding duplication of implementation and computation

11. Protein structure prediction an example There are over 1.3 million sequences in the non- redundant protein database managed by the NCBI and over 19 thousand structures in the protein data bank (PDB) Using this data we have built a library of common protein substructures linking structure and sequence on a local level Our library consists of over 4000 unique substructure associated with from seven to two thousand examples of sequence fragments

12. In order to extract properties that recognize proteins containing particular substructures, we iteratively test different (combinations of) properties on proteins containing and proteins not containing the substructure of interest. calculating properties for all groups takes one week on ten Athlon XP 1700+ (1.46 GHz, 1GB RAM) processors In a more realistic search space, without the drastic search space reductions, we estimate to need approximately 700 processor days with 2GB RAM. And depending on the available resources, we would like to run several such trails in order to test different parameter settings. Thus our upper estimates may be multiplied by a factor 5-10.