Biological pathway and systems analysis An introduction

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

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

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

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

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

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

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

…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 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

Pathway simulation and analysis software The Systems Biology Markup Language (SBML) open interchange format for computer models of biological processes useful for models of metabolism, cell signaling, and more Over 200 software packages today support SBML. The SBML Software Guide lists different software systems and their features.

Hunter and Borg (2003)

Computer models integrate experimental findings into a comprehensive, quantitative and mechanistic description of biological behaviour When experimental techniques cannot provide data

Hunter and Borg (2003)

Virtual Physiological Human Project

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

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

Computer modelling of the heart Pacemaker rhythm in the heart is integrated at the level of the cell No oscillator at the biochemical level of subcellular protein networks (Noble 2006) No ‘gene for’ pacemaker rhythm A set of proteins together with the cellular architecture Computer programs can mimic pacemaker rhythm Computer programs are representations of the processes involved at all the relevant biological levels Long-term aim: Investigate cardiac function of a specific person

Computer modelling of the heart Mathematical reconstruction of the nerve impulse (Alan Hodgkin & Andrew Huxley 1952) Denis Noble applied their ideas to reconstructing the electrical functioning of the heart at UCL (Noble 1960,1962) –long-lasting action potential and pacemaker potential of cardiac muscle Computer models for studying bioelectrical activity at the tissue level (Spach 1983; Barr & Plonsey 1984; Spach &Kootsey 1985)

Linking a genetic defect to its cellular phenotype in a cardiac arrhythmia Genetic defects in ion-channels are typically studied in isolated expression systems –away from the cellular environment where they function physiologically A connection between molecular findings and the physiology of the cell is difficult to establish Clancy and Rudy (1999)

Linking a genetic defect to its cellular phenotype in a cardiac arrhythmia Genetic mutations in the SCN5A gene give rise to a congenital form of the ‘long-QTsyndrome’ –substantial prolongation of the QT interval on the electrocardiogram, which may precede sudden cardiac death Mapped to the alpha-subunit of the cardiac sodium channel (LQT3) DKPQ mutation most severe: deletion of Lys 1505, Pro 1506 and Gln 1507 in the highly conserved portion responsible for fast inactivation

Markovian model represents discrete structural states and their interactions A mutation affecting the structural integrity of a particular state will affect adjacent states Integration of single channels (wild-type or mutant) into the whole cell where effects on cell behaviour can be studied Clancy and Rudy (1999) Nature 400: 566–569

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.

The hallmarks of systems biology a careful series of observations and calculations an iterative process of modelling and experimental validation from pathways to cells to organs…

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