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