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A systems biology approach to modelling the heart Denis Noble University of Oxford WUN Lecture 14 th December 2005 NOBLE, D (2002) Science 295, 1678-1682.

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Presentation on theme: "A systems biology approach to modelling the heart Denis Noble University of Oxford WUN Lecture 14 th December 2005 NOBLE, D (2002) Science 295, 1678-1682."— Presentation transcript:

1 A systems biology approach to modelling the heart Denis Noble University of Oxford WUN Lecture 14 th December 2005 NOBLE, D (2002) Science 295,

2 The problem There are around 25,000 genes in the Human Genome Coding for over 100,000 proteins We know the function of only a very small proportion – genes or proteins Yet most biological functions depend on many genes & proteins interacting

3 Illustrative calculation Assume each function depends on 2 genes (absurd, but still instructive) Total number of possible ‘functions’ would be 0.5 x 25,000 x 24,999 = 312,487,500 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 ) !

4 Consider the number of combinations of r objects taken out of n objects. Then nPr = n(n-1)(n-2) …… (n-r+1) = n !/(n-r) ! (Feytmans, Noble & Peitsch, 2005, Transactions on Computational Systems Biology, 1, 44-49).

5 We will only read the “Book of Life” by understanding how genes interact in co-operative ways in complex systems This means we must unravel & understand biological complexity – i.e. complement reduction with integration Experimentally and in simulation The solution

6 The reductionist causal chain organism organ tissue cellular sub- cellular pathways protein gene I know one approach that will fail, which is to start with genes, make proteins from them and to try to build things bottom-up Sydney Brenner, 2001

7 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 Systems level triggers of cell signalling Systems level controls of gene expression Protein machinery reads genes

8 Physiological systems & function Top-down Middle-out Bottom-up Molecular data & mechanisms systems organs tissues cells pathways organelles Sydney Brenner Nobel Prize2001 Middle-out!!

9 Modeling the heart Working to connect many levels: Genetic mutations Protein function Cellular machinery Tissue function Whole organ : the Virtual Heart

10 Modeling the heart Cell models

11 1960 : analysis of K + channels in the heart mV current I K1 IKIK Replotted from Hall, Hutter & Noble, 1963 Journal of Physiology, 166,

12 Model Construction 1960 I Na I K1 IKIK I Cl Purkinje fibre Channels

13 Noble, 1960 Nature 188,

14 Nature’s pact with the devil: 1. The good news The inward rectifier potassium current i K1 (Kir2.x) guarantees energy conservation

15 Nature’s pact with the devil: 2. The bad news The delayed potassium current i Kr (HERG + MinK) is highly promiscuous

16 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

17 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

18 SA node model – i bNa & i f Example of ‘gene knock-out’ EmEm IfIf I bNa 20% 40% 60%80%100% Noble, D., J. C. Denyer, H.F. Brown. & D DiFrancesco (1992). Proc Royal Society B 250:

19 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

20 Sodium channel molecular structure Four transmembrane domains each with six subunits

21 Heart sodium channel mutations green : IVF mutations red : long QT mutations (Chen et al, Nature, 19 March 1998)

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

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

24 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

25 Drug-gene interaction Threshold for EADs = 23 mVThreshold for EADs = 17 mV NOBLE, D. (2003). Will Genomics revolutionise pharmaceutical research and development? Trends in Biotechnology 21,

26 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: QT prolongation without arrhythmia?

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

28 Connecting levels Incorporation of cellular models into organ models Multi-level simulations use engineering principles: select lower-level models using higher-level insights

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

30 Peter Hunter – the Auckland model ventricle Launch of The Physiome Project Of IUPS movies

31 the Auckland model ventricle Modelling of fibre structure

32 Spread of excitation wave in whole ventricle model Model – Smith et al Experiment – Nash et al

33 Spread of excitation wave in whole ventricle model Model – Smith et al Experiment – Nash et al

34 Simulation of heart failure EADs leading to Torsades de Pointes arrhythmia Rai Winslow

35 Impact-induced arrhythmia Li, Kohl & Trayanova, 2004 Bi-domain modelling with full ionic cell models including stretch-activated channels

36 Breakdown of re-entrant arrhythmia into fibrillation Simulation – Panfilov, Experiment – Witkowsky

37 Ischaemia Project Nic Smith – coronary circulation model

38 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

39 Human cell model TEN TUSSCHER, NOBLE, D., NOBLE, P. J. & PANFILOV (2004). A model of the human ventricular myocyte. American Journal of Physiology 286, H Detailed channel, transporter and SR equations, but computationally very efficient

40 Human ventricular cell model Class III induced EAD [K] o reduced from 5.4 mM to 2.7 mM Then i Kr blocked by 90% mV seconds [K] o reduced i Kr blocked by 90%

41 Re-entrant arrhythmia in human model TEN TUSSCHER & PANFILOV (2004).


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