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Biological pathway and systems analysis An introduction.

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Presentation on theme: "Biological pathway and systems analysis An introduction."— Presentation transcript:

1 Biological pathway and systems analysis An introduction

2 Molecular basis of disease Biomedicine ‘after the human genome’ Current disease models Patient Molecular building blocks proteinsgenes very data-rich about genes, genome organisation, proteins, biochemical function of individual biomolecules

3 Molecular basis of disease Patient Molecular building blocks proteinsgenes Current disease models Physiology Clinical data Disease manifestation in organs, tissues, cells Molecular organisation ?

4 Computational modelling Complex disease models Patient Molecular building blocks proteinsgenes Disease manifestation in organs, tissues, cells Molecular organisation physiology, clinical data tissues organs

5 Living cell “ Virtual cell ” Perturbation Dynamic response Basic principles Applied uses, e.g. drug design Global approaches: Systems Biology Bioinformatics Mathematical modelling Simulation cell network modelling

6 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

7 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

8 …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

9 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 biochemical reaction - this enables one to simulate the change in concentrations of the components of the pathway over time given initial parameters. These concentrations underlie cell behaviour. Schoeberl et al (2002) Nat. Biotech 20: 370 Epidermal growth factor (EGF) pathway

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11 The epidermal growth factor receptor (EGFR) pathway

12 The effect of the number of active EGFR molecules on ERK activation Schoeberl et al., 2002, Nat. Biotech. 20: ,000 active receptors 50,000 active receptors = Inhibition by one order of magnitude EGFR PLCRasPI3K PKCMAPKPKB/Akt TFsFunctional targets CELL GROWTH AND PROLIFERATION ERK

13 The effect of active EGFR number on ERK activation 500,000 active receptors 50,000 active receptors Can this be achieved by receptor inactivation alone?

14 The effect of active EGFR number on ERK activation 50,000 active receptors with normal levels of ERK or ERK overexpression and cross-activation

15 Hunter and Borg (2003)

16 Virtual Physiological Human Simulation of complex models of cells, tissues and organs Heart modelling: 40+ years of mathematical modeling of electrophysiology and tissue mechanics New models integrate molecular mechanisms and large-scale gene expression profiles

17 Multi-level modelling cell organ patient Anatomy and integrative function, electrical dynamics Vessels, circulatory flow, exchanges, energy metabolism Cell models, ion fluxes, action potential, molecules, functional genomics integration across scales through computational modelling

18 Spatial distribution of key proteins Transmural expression differences of an ion channel protein leads to different action potential profiles at the epicardium, midwall and endocardium Arrhythmias Hunter et al (2005) Mechanisms of Ageing and Development 126:187–192.

19 Virtual Physiological Human Project The Virtual Physiological Human https://www.youtube.com/watch?v=CM76-mS84Xs

20 The hallmarks of systems biology formulate a general or specific question define the components of a biological system collect previous relevant datasets integrate them to formulate an initial model of the system generate testable predictions and hypotheses systematically perturb the components of the system experimentally or through simulation study the results compare the responses observed to those predicted by the model refine the model so that its predictions fit best to the experimental observations conceive and test new experimental perturbations to distinguish between the multiple competing hypotheses iterate the process until a suitable response to the initial question is obtained


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