Biological systems and pathway analysis

1. Biological systems and pathway analysis An introduction

2. Protein-Protein Interactions

3. Global approaches: Systems Biology Perturbation Living cell Dynamic response time!

4. Global approaches: Systems Biology Perturbation Living cell Dynamic response time! Biological organisation Information processing Genome expression “Virtual cell”

5. Global approaches: Systems Biology Perturbation Living cell Dynamic response Bioinformatics Mathematical modelling Simulation • Basic principles • Practical applications “Virtual cell”

6. Dynamic Pathway Models • Forefront of the field of systems biology • Main types Metabolic networks Gene networks Signal transduction networks • Two types of formalism appearing in the literature: • data mining  e.g. genome expression at gene or protein level  contribute to conceptualisations of pathways • simulations of established conceptualisations

7. Dynamic models of cell signalling …from pathway interaction and molecular data Erk1/Erk2 Mapk Signaling pathway …to dynamic models of pathway function Schoeberl et al., 2002

8. Simulations: Dynamic Pathway Models • These have recently come to the forefront due to emergence of high-throughput technologies. • Composed of theorised/validated pathways with kinetic data attached to every connection - this enables one to simulate the change in concentrations of the components of the pathway over time given initial parameters. Schoeberl et al., 2002, Nat. Biotech. 20: 370

9. Response Models × Signalling Pathways Models Charasunti et al. (2004) • model of the action of Gleevec on the Crk-1 pathway in Chronic Myeloid Leukaemia

10. Dynamic biochemistry • Biomolecular interactions • Protein-ligand interactions • Metabolism and signal transduction • Databases and analysis tools • Metabolic and signalling simulation • Metabolic databases and simulation • Dynamic models of cell signalling

11. Types of Modelling Methods • Stochastic approaches • Simple statistics • Bayesian Networks • Deterministic • Boolean networks • ODE approach • Iterations in a system • Classification/Clustering approaches • Support Vector Machines • Neural Networks • Hybrid Models – mixture of the above Ideker & Lauffenberger, 2003, TiB 21(6): 255-262

12. Pathway simulation and analysis software accessible from http://sbml.org/index.psp JigCell JSIM JWS Karyote* libSBML MathSBML MOMA Monod NetBuilder PathArt PathScout ProcessDB* SBW SCIpath SigPath Simpathica StochSim STOCKS BASIS BioCharon Bio Sketch Pad BioSpreadsheet BioUML BSTLab CADLIVE CellDesigner Cellerator Cellware Cytoscape DBsolve Dizzy E-CELL ESS Gepasi Jarnac JDesigner TeraSim Trelis Virtual Cell WinSCAMP

13. Biomedicine ‘after the human genome’ • Patient Molecular basis of disease Current disease models Molecular building blocks genes proteins

14. Biomedicine ‘after the human genome’ • Patient Physiology Clinical data Molecular basis of disease Current disease models Molecular building blocks genes proteins

15. Biomedicine ‘after the human genome’ • Patient Disease manifestation in organs, tissues, cells Molecular organisation Complex disease models Computational modelling Molecular building blocks genes proteins

16. Physiome project “Virtual human” Simulation of complex models of cells, tissues and organs • 40 years of mathematical modeling of electrophysiology and tissue mechanics • New models will integrate large-scale gene expression profiles http://www.physiome.org/

17. Physiome project patient organ Anatomy and integrative function, electrical dynamics Vessels, circulatory flow, exchanges, energy metabolism Cell models, ion fluxes, action potential, molecules, functional genomics cell