Biological systems and pathway analysis An introduction

Protein-Protein Interactions

Post-genome informatics: Synthesis How does the organisation of living matter in space and time give rise to biological processes? Prediction of all interactions gagccagcgagccag cgcgcgcgggcgggc ggacagatcggagcc gagcggggccgggcg gggcgctccctgcag +

Systems Biology Emergence Self-organisation Agent interactions Agents System Biological systems are modular (sub-systems) hierarchical multiscale (space and time) molecules pathways cells transcription, catalysis, P-P interaction, cross-talk, cell- cell signalling molecular complexes, pathways, networks, tissues a behaviour or property resulting from interaction and self-organisation interacting components (modules), ongoing relationships, can evolve

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

Living cell “Virtual cell” Perturbation Dynamic response Biological organisation (e.g., gene families, structural genomics, pathways) Global approaches: Systems Biology time! -omics time series

Living cell “Virtual cell” Perturbation Dynamic response Biological organisation (e.g., gene families, structural genomics, pathways) Basic principles Practical applications Global approaches: Systems Biology Bioinformatics Mathematical modelling Simulation time! -omics time series

Dynamic Pathway Models Forefront of the field of systems biology Types –Inter/intra-cellular –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

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

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

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

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

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):

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

Microarrays, Protein Pathways and SCIpath mRNA Microarray Technology Pathway Information Measure mRNA levels as approximation of genome expression

SBML BioPAX Pathway builder MicroExpress maps microarray and proteomic data to pathway nodes External database links

Dynamic information processing in the cancer cell ‘Pathway signatures’ Molecular signatures for pathways are needed (not only for single molecules)

A p53 Pathway

A protein in the pathway

Upregulation of the protein’s gene e.g. this protein is upregulated in prostate cancer compared to normal cells

Greater upregulation…

Downregulation of the protein’s gene

Greater downregulation…

Leukaemia types

Molecular basis of disease Biomedicine ‘after the human genome’ Current disease models Patient Molecular building blocks proteinsgenes

Molecular basis of disease Biomedicine ‘after the human genome’ Patient Molecular building blocks proteinsgenes Current disease models Physiology Clinical data

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

Tumour Systems Modelling Four major areas of focus: –Growth –Treatment Response –Pathway –Angiogenesis Levels of granularity and scope –Phenomenological  mechanistic GROWTH RESPONSE ANGIOGENESIS PATHWAYS deliveryflow

Growth Models Stamatakos et al. (1998) –3D simulation –Satisfactory agreement with in vitro experiments t = 240h Irradiation starts at t = 168h Proliferating: Mitosis: Necrotic Products: G0 Phase: Necrosis or Apoptotic

Cellular Automata (CA) approaches Determination of local interaction rules Stamatakos et al. 1998, 2001 G1G1 SG2G2 MG0G0 N Cell death

Stamatakos et al… Flowchart for the response of a single tumour cell to irradiation. Stamatakos et al., 2001, IEEE Trans. Inf. Tech. Biomed. 5(4)

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

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