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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 be0.5 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 e302);
for all combinations (~ 2 e166713)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, 18-20.
NOBLE, D (2002) Nature Reviews Molecular Cell Biology 3, 460-463.
Unravelling complexityNeed to work in an integrative way at all levels:
organism organtissue
cellular sub-cellularpathwaysprotein
gene
There are feed-downs as well as upward between all these levels
higher levels controlgene expression
higher levels controlcell 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, 460-463
Sidney Brenner2001
ExampleModeling the heart
Cell models
Model Construction 2000
INa
IClIK1 IK Ito
ICaChannels
I Na/K
I NaCaNa/H Na/HCO3 Cl/OH
Cl/HCO3
Carriers
Ca
pH
ATP
Glucose
Fatty Acids
Amino Acids
H/Lactate
SubstratesAng II1
2
NO
ßM
Receptors
Example of protein interaction in a cell model Reconstructing the heart’s pacemaker
Sinus rhythm generated by ion channel interaction
ICaL
IKr
Em
If is example of fail-safe ‘redundancy’
Rhythm abolished when interaction prevented
Acceleration of sinus rhythm by adrenaline
If
All 3 protein levels up-regulated
Disease insightModelling arrhythmias
Mutations in various ionic channels can predispose to repolarization failure
This simulation is of a sodium channelmis-sense mutation responsible foridiopathic 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 mVshift18 mVshift
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 causingQT problems is therefore a foreseeable possibility
Noble D (2002) Unravelling the genetics and mechanisms of cardiac arrhythmia. Proc Natl Acad Sci USA 99, 5755-6
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, 1678-1682
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 levelEnabling 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 – ibNa & if
Example of ‘gene knock-out’
Em
If
IbNa
20% 40% 60% 80% 100%
Creating a pacemaker : gene transfer experiment
Miake, Marban & NussNature, 12 Sept 2002 Ventricular cell model
This example shows
• effect of 90% block of IK alone (pure class III)
• effect of additional 20% block of Ica,L
Multiple site drugs
• Normal action potential
• Block of IK alone
• Partial block of ICaL
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
INaCa
0 100 200 300
Time (seconds)
20
10
0
[Na+]i
(mM)
Voltage
[Na+ ]i
[Ca2+]i( M)
0
-20
-40
0
-100
1.0
0
INaCa
(nA)
Voltage(mV)
[Ca2+ ]i
Ca oscillator activated as Ca oscillator activated as [Na][Na]ii rises rises
0 100 200 300
Time (seconds)
20
10
0
[Na+]i
(mM)
Voltage
INaCa
[Na+ ]i
[Ca2+]i( M)
0
-20
-40
0
-100
1.0
0
INaCa
(nA)
Voltage(mV)
[Ca2+ ]i
Down-regulated NaDown-regulated Na++-Ca-Ca2+ 2+
ExchangeExchange
0 100 200 300
Time (seconds)
20
10
0
[Na+]i
(mM)
Voltage
INaCa
[Na+ ]i
[Ca2+]i( M)
0
-20
-40
0
-100
1.0
0
INaCa
(nA)
Voltage(mV)
[Ca2+ ]i
Up-regulated NaUp-regulated Na++-Ca-Ca2+ 2+
ExchangeExchange
• 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