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Biological systems and pathway analysis
An introduction
Protein-Protein Interactions
Living cellPerturbation Dynamic response
Global approaches: Systems Biology
time!
Living cell
“Virtual cell”
Perturbation Dynamic response
Biological organisation
Information processing
Global approaches: Systems Biology
time!
Genome expression
Living cell
“Virtual cell”
Perturbation Dynamic response
•Basic principles
•Practical applications
Global approaches: Systems Biology
Bioinformatics
Mathematical modelling
Simulation
Dynamic Pathway Models
• Forefront of the field of systems biology• Main types
Metabolic networksGene networksSignal 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)– 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 and signalling 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): 255-262
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 softwareaccessible from http://sbml.org/index.psp
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 inorgans, tissues,
cells
Molecular organisation
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/
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
