Systems biology US visit Oxford 6th July 2004

The questions What have been your most successful approaches and applications of systems biology? What are your most critical failures? What special technologies, challenges have you focused on? What are the interactions between academia, industry, and government? What are your plans for the future? What are your expectations for systems biology (i.e., what do you expect to gain)? What is the expected time-line for the payoffs (low-hanging fruit, longer range)? How does your work fit into the educational institutions?

Combinatorial explosion Assume each function depends on 2 genes (absurd, but still instructive) Total number of possible ‘functions’ would be 0.5 x 40,000 x 39,999 = 799,980,000 With more realistic assumptions about # of genes in each function, the figures are huge : at 100/function (~ 1.5 e 302 ); for all combinations (~ 2 e ) Feytmans, Noble & Peitsch, Transactions in Computational Systems Biology, 2004

Is the Genome the “Book of Life?” Problem 1 : the function of a gene is NOT specified in the DNA language Problem 2 : each gene plays roles in MULTIPLE functions Problem 3 : each function arises from co-operation of MANY genes Problem 4 : function also depends on important properties NOT specified by genes – properties of water, lipids, self-assembly etc etc Problem 5 : nature has built-in fail-safe ‘redundancy’ – this ONLY emerges at the functional level NOBLE, D (2002) Physiology News 46,

NOBLE, D (2002) Nature Reviews Molecular Cell Biology 3, Unravelling complexity Need to work in an integrative way at all levels: organism organ tissue cellular sub-cellular pathways protein gene There are feed-downs as well as upward between all these levels higher levels control gene expression higher levels control cell function & pathways

Unravelling complexity Top-Down or Bottom-Up? Middle-out!! Noble D (2002) The Rise of Computational Biology. Nature Reviews Molecular Cell Biology, 3, Sidney Brenner 2001

Example Modeling the heart Cell models

Model Construction 2000 I Na I Cl I K1 IKIK I to I Ca Channels I Na/K I NaCa Na/H Na/HCO 3 Cl/OH Cl/HCO3 Carriers Ca pH ATP Glucose Fatty Acids Amino Acids H/Lactate Substrates Ang II 1 2 NO ß M Receptors

Example of protein interaction in a cell model Reconstructing the heart’s pacemaker Sinus rhythm generated by ion channel interaction I Ca L I Kr EmEm I f is example of fail-safe ‘redundancy’ Rhythm abolished when interaction prevented Acceleration of sinus rhythm by adrenaline IfIf All 3 protein levels up-regulated

Disease insight Modelling arrhythmias Mutations in various ionic channels can predispose to repolarization failure This simulation is of a sodium channel mis-sense mutation responsible for idiopathic ventricular fibrillation

Expressed sodium channel kinetics (Chen et al, Nature, 19 March 1998)

Computer model prediction Sodium channel missense mutation 12 and 18 mV voltage shifts Using digital cell ventricular model 12 mV shift 18 mV shift

This approach has now been used for a substantial number of gene manipulations in heart cells and can account for genetic susceptibility to fatal cardiac arrhythmia Including interactions with drugs causing long QT and arrhythmia in clinical trials Genetic typing to screen out those susceptible to drugs causing QT problems is therefore a foreseeable possibility Noble D (2002) Unravelling the genetics and mechanisms of cardiac arrhythmia. Proc Natl Acad Sci USA 99, Unravelling genetics of arrhythmia

Connecting levels Incorporation of cellular models into organ models

Noble D (2002) Modelling the heart: from genes to cells to the whole organ. Science 295, Physiome Sciences

The questions What have been your most successful approaches and applications of systems biology? Unraveling complexity at cellular and other levels in the heart What are your most critical failures? Linking electrophysiology to metabolic and genetic pathways What special technologies, challenges have you focused on? Electrophysiology, computer modeling, cytochemical imaging What are the interactions between academia, industry, and government? Good involvement with pharmaceutical companies Government (DTI, EPSRC) involvement in GRID computing

The questions What are your plans for the future? Understanding arrhythmia at the whole organ level Enabling tools (COR, GRID, ML etc) to be freely available What are your expectations for systems biology (i.e., what do you expect to gain)? This is THE post-genomic challenge. Expectations very high. Difficulties only too clear : combinatorial explosion What is the expected time-line for the payoffs (low-hanging fruit, longer range)? The two items above are scheduled for roughly 5 year programmes How does your work fit into the educational institutions? Strong interdisciplinarity: maths, computing, engineering, physiology, clinical

SA node model – i bNa & i f Example of ‘gene knock-out’ EmEm IfIf I bNa 20% 40% 60%80%100%

Creating a pacemaker : gene transfer experiment Miake, Marban & Nuss Nature, 12 Sept 2002 Ventricular cell model

This example shows effect of 90% block of I K alone (pure class III) effect of additional 20% block of I ca,L Multiple site drugs

Normal action potential Block of I K alone Partial block of I CaL

Understanding complexity is frequently counterintuitive Example from cardiac ischaemia

Ischaemia Project

2D Model of Ischaemia Two dimensional simulations with 10,000 grid points sustained ectopic beating due to calcium oscillations

I NaCa Time (seconds) [Na + ] i (mM) Voltage [Na + ] i [Ca 2+ ] i (  M) I NaCa (nA) Voltage (mV) [Ca 2+ ] i Ca oscillator activated as [Na] i rises

Time (seconds) [Na + ] i (mM) Voltage I NaCa [Na + ] i [Ca 2+ ] i (  M) I NaCa (nA) Voltage (mV) [Ca 2+ ] i Down-regulated Na + -Ca 2+ Exchange

Time (seconds) [Na + ] i (mM) Voltage I NaCa [Na + ] i [Ca 2+ ] i (  M) I NaCa (nA) Voltage (mV) [Ca 2+ ] i Up-regulated Na + -Ca 2+ Exchange

Ch’en et al Progress in Biophysics, 1998 – inhibition of Na-Ca exchange predicted NOT to protect against ischaemic arrhythmias Hashimoto et al Jap J Electrocardiol 2000 – “A new selective Na-Ca exchange inhibitor failed to show anti-arrhythmic effects on canine coronary ischaemia and reperfusion induced arrhythmias.” Na-Ca pharmacological intervention

Novartis Foundation Symposium 247 ‘In Silico’ Simulation of Biological Processes Wiley, 2002 ‘Modelling Complex Biological Systems’ BioEssays, 24, Dec 2002 Further reading