1. Knowledgebase Creation & Systems Biology: A new prospect in discovery informatics S.Shriram, Siri Technologies (Cytogenomics), Bangalore S.Shriram, Siri Technologies (Cytogenomics), Bangalore

2. A knowledgebase provides an integrated approach to biological simulation that combines process, technology, tools and applications for solving complex problems and for data representation, validation and prediction What is a knowledgebase?

3. The use of simulation in pharmaceutical research enables investigators to anticipate potential issues in the drug discovery process and to select the best overall design principles in advance of real-life studies Knowledgebases forms the base for SYSTEM BIOLOGY - ie study of biological systems using a holistic approach

4. Access proprietary and public databases, data analysis tools, and computer algorithms within a single, expandable, web-based software environment Streamline data analysis and interpretation Share experimental data and models across organizational networks Advantages of Knowledgebase

5. Advantages…. Explore new experimental scenarios, test hypotheses, and generate predictive information Create, run, modify and routinely upgrade detailed models of cells, tissues and organs with little or no dependence on highly trained programmers

6. Path to Knowledgebase Knowledgebase Database Infobase

7. Identify the disease and tissue (eg. pancreatic cancer and pancreas and other related tissues) Data to be gathered form curation of literature, experiments(eg. gene expression studies), clinical studies and analysis/interpretation of these details to extract information (eg. identify targets/side effects of known drugs in the pathways) Creation of Knowledgebase

8. Creation… Represent complex experimental data in mathematical form and then uses these equations to build customized biological models Models to aid prediction of effects of new drugs, etc.

9. Drug Discovery –Emerging needs for Knowledgebase Chemo-informatics Curated Knowledgebase Target identification Target evaluation and selection Lead identification/o ptimization Process development Preclinical evaluation Clinical evaluation Bioinformatics Drug

10. Types of Knowledgebase Ligands/ Drugs Gene/Protein/Target Pathways

11. Integrated Platform for Bio and Chemo Knowledgebase Patent & Published Literature Proprietary Data BioinformaticsChemoinformatics Integrated Knowledgebase

12. Ligand centric knowledgebase Pharmacophore models Combinatorial chemistry Ligand Centric Structure 2D, 3D Drug Targets Target Sequence Target Structure (3D) Patents & literature Synthesis Assay / Bioactivity SAR Physico-chemical properties Ligand – target interaction Regulatory information ADME and Toxicity Analogs

14. Data Integration Integrated Database Homologene LocusLink SWISS-PROT Homologous GenBank BODYMAP Probeset UniGene OMIM

15. Features of pathway database Clickable maps that give data on the proteins of interest Multiple search modes, including protein, signaling molecule and ligand structure based searches and other filters like physiology, organism, disease state etc Provision for a comprehensive account of the diseases, to enable the user to build and visualize non-canonical pathways

16. Caspase Pathway

17. Features of Pathway Knowledgebase Tagging of gene expression data (from Microarray, SAGE, etc) onto the simple clickable pathway maps. In-silico manipulation of pathways – ie predict the alterations in expression levels in any given tissue or disease conditions Ease target prioritization

23. Building blocks of SYSTEM BIOLOGY Ligands/ Drugs Gene/Protein/Target Pathways Organism / disease Cell / tissue

24. Why we need biological systems?  To figure out  What is the effect of an intervention in one part of the system, and its associated problem?  What intervention one has to make in order to obtain some desired result? Key Players  Physiome Sciences  Entelos Inc.

25. Predictive biology GeneChips High-ThroughputSystems Knock-outs HealthcareAlliances Bioinformatics Predictive Biological ModelsFragmentedExpertise IntegratedKnowledge Computer Simulation Novel InsightsClinicalResponse MolecularTarget

27. ExampleExample Since these equations are completely defined by the knowledge of connectivity of a network, and knowledge of various transition rate constants, and since these quantities are all stored in a databases, the equations may be generated automatically on a computer

31. Allen resident cell activation inflammatory cell influx Bill resident cell activation inflammatory cell influx 8% improvement in FEV121% improvement in FEV1

32. Applications of Systems Biology Understanding disease dynamics Understanding disease dynamics Test hypotheses of a disease pathology Test hypotheses of a disease pathology – Ask better questions – Plan better experiments Identify and fill the knowledge gaps Identify and fill the knowledge gaps Bridging biochemistry to clinical outcomes Bridging biochemistry to clinical outcomes Target assessment & prioritization Target assessment & prioritization Drug candidate advancement Drug candidate advancement Combination therapy assessment Combination therapy assessment Designing and understanding clinical studies Designing and understanding clinical studies Patient selection and dosing Patient selection and dosing Surrogate marker prediction Surrogate marker prediction

33. Entelos demo

34. Thanks to Siri Technologies Cytogenomics Jubilant Biosys Entelos Physiome Sciences

35. Thank you