Post on 09-Jan-2017
Systems Medicine course Como 2016 26/09/2016
Westerhoff et al Page 1
Systems Biology and Medicine:
Understanding disease by understanding the
networks of Life
Hans V. Westerhoff
and friendsSynthetic Systems Biology, SILS, NISB, the University of Amsterdam, andMolecular Cell Physiology, NISB, VU University Amsterdam, Amsterdam, NL, EU, andManchester Centre for Integrative Systems Biology, Manchester, UK, EU
The second Systems Biology & Systems Medicine (SyBSyM)School, 25‐29 September 2016, Como
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Towards Individualized Systems Medicine
Hans V. Westerhoff
and friendsSynthetic Systems Biology, SILS, NISB, the University of Amsterdam, andMolecular Cell Physiology, NISB, VU University Amsterdam, Amsterdam, NL, EU, andManchester Centre for Integrative Systems Biology, Manchester, UK, EU
The first Systems Biology & Systems Medicine (SyBSyM)School, 21‐27 September 2014, Como
Systems Medicine 2016
A unique course:Small and intensive
The menu
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What is special about 1996?
A. First recombinant DNA implementation
B. First sequenced genomes published
C. Structure of DNA discovered
D. Anti sense RNA discovered
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Systems Medicine course Como 2016 26/09/2016
Westerhoff et al Page 2
What is special about 1996?
Closed
A.
B.
C.
D.
First recombinant DNA implementation
First sequenced genomes published
Structure of DNA discovered
Anti sense RNA discovered
26.7%
66.7%
0.0%
6.7%
State of the field in 1995
Components
and physiology
?But no robust understanding of their relationships
Prepare to react
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What remained to be discovered in 2000?
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What remained to be discovered
• Origin of Life
• Why present day diseases tend to elude molecule based therapies
• Why diseases are ‘undemocratic’
• How diseases are multifactorial
• Why individuals and cell populations are heterogeneous
• Why diseases are sometimes unpredictable
It was time for Systems Biology
• i.e. a new Science
• aiming to understand
• principles governing
• how the biological functions
• arise from theinteractions
Now it is also time for Systems Medicine
Systems Medicine course Como 2016 26/09/2016
Westerhoff et al Page 3
Where Systems Biology made the difference
genomicstranscriptomics
proteomics
metabolomics
structural biology
biophysics biologybiochemistry
physiology
Systems Biology: ‐integrates different types of data into predictive models‐makes data predictive and function predictable‐uniquely shows how networking produces (dis‐)function
Example 1: the genome wide metabolic map:components integration into function
food1
food2
food3
Data concerning all metabolic genes have hereby been integrated into a predictive formatPredicting how every molecule in our body is made by our body
Example 2: The old (<2000) paradigm was: Disease is due to a sick molecule
Impaired function+ XCause
If you think that this was (is) not a dominant view of disease, then
consider:‘This is the key disfunction in this disease’
‘Key gene’‘Blockbuster drug’
‘The rate limiting …..’The search for the oncogene
First paradigm: Disease is caused by a single factor
• Pest• Malaria• Tuberculosis
• Cancer• Obesity• Heart disease• ….• Ulcers…..
Systems Medicine course Como 2016 26/09/2016
Westerhoff et al Page 4
What (type of) evidence would show that a disease is monofactorial?
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What (type of) evidence is there then for most diseases that they are
monofactorial?
Cancer Diabetes Heart dis Malaria TBC
Quarantaine helps Single pathology Immunization helps Mendelian inheritance GWAS giving factors with high penetrance Single drug helps Is there from embryo onwards
How could we explain all these features of present day diseases?
A. In reality each disease is: many different yet similar diseases
B. Diseases are due to a malfunctioning network
C. Gene redundancy
D. Many proteins consists of multiple polypeptide chains
E. Proteins can become phosphorylated
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How could we explain all these features of present day diseases?
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Closed
A.
B.
C.
D.
E.
F.
In reality each disease is: many different yet similar diseases
Diseases are due to a malfunctioning network
Gene redundancy
Many proteins consists of multiple polypeptide chains
Proteins can become phosphorylated
Diseases do not have a genetic origin
23.5%
76.5%
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The old paradigm: Disease is due to a sick molecule
Impaired function+ XCause
Our new paradigm: Network disease
Impaired function
XCause 3
XCause 1
XCause 2
A network disease is caused by a combination of possibly remote factors
Systems Medicine course Como 2016 26/09/2016
Westerhoff et al Page 5
The new paradigm: Network disease
Impaired function
Cause 3
Cause 1
Cause 2
A network disease is caused by a combination of possibly remote factors
Why is this?
The impaired function depends on a commodity that is delivered by a number of parallel pathways
Therefore the disease does not appear until allthree pathways have been incapacitated
X
XX
The new paradigm: Network disease
Impaired function
Cause 3
Cause 1
A network disease is caused by a combination of possibly remote factors and these need not be the same factors
Why is this?
The impaired function depends on a commodity that is delivered by a number of parallel pathways
Therefore the disease does not appear until allthree pathways have been incapacitated
XXX
Cause 2
The new paradigm: Network disease
Impaired function
XCause 3
XCause 1
XSNP 2
A network disease is caused by a combination of possibly remote factors that differ between individual patients (because they already have the factors as SNPs)
Diseases are multifactorial in three ways
• Multiple faults required for the disease
• For each fault there are alternative faults
• Differences between individual patients
Indeed,
If the problem sits with the network then we need to deal with the network
From the molecules and the network is needed for comprehension
Systems Medicine course Como 2016 26/09/2016
Westerhoff et al Page 6
Impaired function
to the network
The example cancer
The Oncogene
In the 1980’s everyone searched for the oncogene.
It was never found………..
The oncogene…?..; No: there are many! The oncogene…?..; No: there are many!
The Hallmarks of cancerHanahan & Weinberg
Systems Medicine course Como 2016 26/09/2016
Westerhoff et al Page 7
Major Systems Biology accomplishmentsfor the understanding of disease
• Systems Biology has shown that there is little basis of looking for themolecule that causes a disease (for most diseases):– It is a network malfunction
• Systems Biology acknowledges complexity such as through epigenetics rather than simplifying away from it– Genetic network, epigenetic network, transcription‐tranlation
network, signaling network, metabolic network all integrated
• Systems Biology shows that there are three different aspects to multifactorial disease– More than one cause; not always the same set of causes for
the same disease; different between individuals
In a GWAS one does not find genes that correlate with breast cancer for more than 10%. Is this because
A. Breast cancer is caused by lack of a factor that is delivered by three alternative pathways?
B. it is caused by at least one pathway with more than10 gene products on it?
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In a GWAS one does not find genes that correlate with breast cancer for more than 10%. Is this because
A.
B.
Breast cancer is caused by lack of a factor that is delivered by three alternative pathways?
it is caused by at least one pathway with more than10 gene products on it?
36.8%
63.2%
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Towards Precision Biology and Medicine
• The Future in 2000– What remained to be discovered
• Life at the edge and the origin of Life– How to get your Carbon and Gibbs energy precisely (‐‐> Thierry Mondeel)
• Towards precision medicine
– Individualized medicine from PKU to Parkinson’s? (Alexey Kolodkin)
– Transcription dynamics, cell‐cell heterogeneity and cancer ( StephaniaAstrologo)
– How understanding might matter: the Janus head of acute and chronic inflammation
• Serving the community – Infrastructure systems Biology Europe (ISBE@NL): make me a model coalition
(Alexey Kolodkin)
– Replica models, virtual human
Towards Precision Biology and Medicine
• The Future in 2000– What remained to be discovered
• Life at the edge and the origin of Life– How to get your Carbon and Gibbs energy precisely (‐‐> Thierry Mondeel)
• Towards precision medicine
– Individualized medicine from PKU to Parkinson’s? (Alexey Kolodkin)
– Transcription dynamics, cell‐cell heterogeneity and cancer ( StephaniaAstrologo)
– How understanding might matter: the Janus head of acute and chronic inflammation
• Serving the community – Infrastructure systems Biology Europe (ISBE@NL): make me a model coalition
(Alexey Kolodkin)
– Replica models, virtual human
The early Earth
• H2
• CO
• CO2
• No O2
Life needs organic (complexed) Carbon (similated CO or CO2)
Gibbs energy (ATP)
Are there organisms that can do this?
Systems Medicine course Como 2016 26/09/2016
Westerhoff et al Page 8
The genome wide metabolic map, i.e. all the network can make from any nutrition
food1
food2
food3
Predicted flux distribution to produce acetate on the Schuchmann and Müller GEMM: makes no net ATP
Possible!
Extend the C. ljungdahlii GEMM with Schuchman’s reactions
Try all combinations of electron donor alternatives
The menu
Towards Precision Biology and Medicine
• The Future in 2000– What remained to be discovered
• Life at the edge and the origin of Life– How to get your Carbon and Gibbs energy precisely (‐‐> Thierry Mondeel)
• Towards precision medicine
– Individualized medicine from PKU to Parkinson’s? (Alexey Kolodkin)
– Transcription dynamics, cell‐cell heterogeneity and cancer ( StephaniaAstrologo)
– How understanding might matter: the Janus head of acute and chronic inflammation
• Serving the community – Infrastructure systems Biology Europe (ISBE@NL): make me a model coalition
(Alexey Kolodkin)
– Replica models, virtual human
Inborn errors of metabolism
Vital constituent
food
Systems Medicine course Como 2016 26/09/2016
Westerhoff et al Page 9
Vital constituent
food
The network topology predicting disease for inborn errors of metabolism
Would this work?
Could it lead to cures?
Could it help manage toxicity?
50
Would this work?
Could it lead to cures?
51
Example of map utilization tyrosine metabolism:nurture
Phenylketone urine
✗
ProteinNutrition
dopa
dopamine
Nor‐epinephrin
OK
Phe
Tyr✗
Example of map utilization tyrosine metabolism:nurture
Phenylketone urine
✗
ProteinNutrition
dopa
dopamine
Nor‐epinephrin
Phe
Tyr
✗OKX
Phe is essential amino acid
✗ ✗
Example of map utilization tyrosine metabolism:nature
Phenylketone urine
✗
ProteinNutrition
dopa
dopamine
Nor‐epinephrin
Phe
(Tyr)
✗ OKX
Phenylketonuria (PKU) = IEM
✗ ✗
Systems Medicine course Como 2016 26/09/2016
Westerhoff et al Page 10
Can one use the map to design a therapy?
Example of map utilization tyrosine metabolism:nature
Phenylketone urine
✗
ProteinNutrition
dopa
dopamine
Nor‐epinephrin
Phe
Tyr
✗
Phenylketonuria (PKU) = IEM
✗ OK✗✗ ✗
Example of map utilization tyrosine metabolism:nature
Phenylketone urine
✗
ProteinNutrition
dopa
dopamine
Nor‐epinephrin
Phe
Tyr✗
Phenylketonuria (PKU) = IEM
✗✗ ✗
Nutrition therapy
PKU: lack of brain development
Why brain specifically?
Why does PKU lead to mental retardation specifically?
A. Brain is the only tissue that contains protein
B. Blood brain barrier causes a difficulty
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Why does PKU lead to mental retardation specifically?
A.
B.
Brain is the only tissue that contains protein
Blood brain barrier causes a difficulty
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0.0%
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Systems Medicine course Como 2016 26/09/2016
Westerhoff et al Page 11
Example of map utilization tyrosine metabolism:Brain: adrenalin
Phenylketone urine
✗
ProteinNutrition
dopa
dopamine
Nor‐epinephrin
Epinephrin=adrenalin
Phe
Tyr
✗ OKX
✗✗
✗✗
✗ ✗Another riddle
Reduced Phe‐intake therapy worksbetter than Tyr supplementation:
Apparently the problem is not just lackof tyrosine for protein synthesis
Tyr enters brain in exchange for Phe
63
Phe
Tyr
BBB
Westerhoff on Systems Toxicology; slide
Mapping beyond the pathway
64
Also other diseases?
Yes, multiple related diseases
Phenylketonuria (PKU)
Example of map utilization tyrosine metabolism:Multiple tyrosinemias
Phenylketone urine
✗
Protein
`
Nutrition
dopa
dopamine
Nor‐epinephrin
Phe
Tyr
✗alkaptonuria
tyrosinaemia III✗
tyrosinaemia I✗
✗
Systems Medicine course Como 2016 26/09/2016
Westerhoff et al Page 12
Can it help design drug therapy?
67
Example of map utilization tyrosine metabolism:(Cautioning vis‐à‐vis) drug therapy
Phenylketone urine
✗
Protein
`
Nutrition
dopa
dopamine
Nor‐epinephrin
Phe
Tyr
✗alkaptonuria
|‐‐‐‐‐‐‐‐ Nitisinone?tyrosinaemia III✗
Associations with unrelated diseases?
Phe Tyr Dopamine
Neuron functioning
Energy supply
PKU
ROS management
Astrocytes
Synuc
DJ1
Other mutation
Mitochondria
Parkinson’s disease
Westerhoff on Live maps for Life
70
Detailed model of ROS management: in silico discovery
Alexey Kolodkin The menu
Posters: all breaks
Poster flashes
Systems Medicine course Como 2016 26/09/2016
Westerhoff et al Page 13
Towards Precision Biology and Medicine
• The Future in 2000– What remained to be discovered
• Life at the edge and the origin of Life– How to get your Carbon and Gibbs energy precisely (‐‐> Thierry Mondeel)
• Towards precision medicine
– Individualized medicine from PKU to Parkinson’s? (Alexey Kolodkin)
– Transcription dynamics, cell‐cell heterogeneity and cancer ( StephaniaAstrologo)
– How understanding might matter: the Janus head of acute and chronic inflammation
• Serving the community – Infrastructure systems Biology Europe (ISBE@NL): make me a model coalition
(Alexey Kolodkin)
– Replica models, virtual human
The Janus head of cells andIs Life computable/predictable?
Or is it just too chaotic?
The Janus head of cells;is it predictable which way it turns?
Social (multicellular organism)
Selfish (Unicellular or cancer)
Reason to doubt predictability
• For many diseases, falling ill is not democratic (i.e. unequal probabilities)
• Approved drugs only work for 40%
• There is just too much noise in Biology (??)
Heisenberg’s uncertainty principle
• If one looks at a particle that arrives at a precise time, then its energy will remain uncertain
• If one looks at the average of particles arriving over a long period of time, then one knows their average energy much more precisely
∆ · ∆
7/4/2016 Westerhoff: 77
Drug therapy uncertainty principle?
• Drug effectiveness for any individual patient: low certainty
• For the average effect on multiple patients:
much certainty
• When more interaction information available (genome sequence; nutrition) more certainty also for the individual (individualized systems medicine)
∆ · ∆ ′
7/4/2016 Westerhoff: 78
Systems Medicine course Como 2016 26/09/2016
Westerhoff et al Page 14
Example:Uncertain prediction of Cetuximab
effect on colon cancer
Zalcberg et al, NEJM 2008
(K‐ras wild type)
SURVIVAL
Time in months
Some colon cancer patients respond positively to treatment with EGFR receptor antagonists, whereas others respond much less: Δeffect is large forany individual (uncertain prediction)
7/4/2016 Westerhoff: 79
The Bohr‐Einstein debate
Bohr: Fundamentallywe cannot know energy andtime precisely for any particle: the particle is a wave of uncertainty. This means that in everynew experiment the particle at time t=0 will have a different energy.
Einstein: Gott würfelt nicht (God does not throwdice): it is just that we do not have sufficientinformation about the individual particles.
Statistical: One measures many particles anyway, or one over a long time: E can be measuredthrough the average
7/4/2016 Westerhoff: 80
Patients with mutated K‐ras: no effect of
cetuximab
SURV I VAL
Time in monthsZalcberg et al, NEJM 2008
But for a small group of patients where we have information, we canpredict:
Patients with tumors with K‐ras mutations do not respond
Colon Cancer
7/4/2016 Westerhoff: 81
Conclusion
Individualized systems medicine may reduce the impredictability
Knowledge removes uncertainty (Einstein)
The Bohr‐Einstein debate
Bohr: Fundamentallywe cannot know energy andtime precisely for any particle: the particle is a wave of uncertainty. This means that in everynew experiment the particle at time t=0 will have a different energy.
Einstein: Gott würfelt nicht (God does not throwdice): it is just that we do not have sufficientinformation about the individual particles.
Statistical: One measures many particles anyway, or one over a long time: E can be measuredthrough the average
7/4/2016 Westerhoff: 83
But Albert, there is also intrinsic
noise!
Indeed, cancer may be an exception
• Based on intrinsic noise (somatic mutations) and selection
• Indeed, clonal cell lines still show differences between individual cells
• The individual cells in tissues differ between each other due to genetic mutations and epigenetic mutations
But shouldn’t noise be small because molecule numbers are large?
Systems Medicine course Como 2016 26/09/2016
Westerhoff et al Page 15
In most traditional pathways noise is small because molecule numbers are large
Non-equilibrium pathways: Fano factor is also approximately equal to 1
Molecule numbers are >>10000. Where does cell-cell heterogeneity come from then?
7/4/2016 Westerhoff: 85
=1%
DNA
mRNA
Protein
1 2
3 4
But Biology is ‘hierarchical’ and complex
7/4/2016 Westerhoff: 86
0 50 100 150 200 250 3000
50
100
150
# of Protein Molecules
# of Simulations
0 50 100 150 200 250 3000
50
100
150
# of Product Molecules
# of Simulations
1 2
3 4
DNA
mRNA
Protein
ProductSubstrate5 6
1
3
5
= 0.5*DNA
= 0.5*mRNA
= 0.5*Protein*Substrate
2
4
6
= 0.1*mRNA
= 0.1*Protein
= 0.1*Product
0 20 40 600
20
40
60
80
100
Time
# of Molecules
DNA
mRNA
Protein
Product
1.0098
3.5313
13.0332
0
5
10
15
mRNA Protein Product
Fano Factor (σ
2/µ)
Hierarchies explain noise
7/4/2016 Westerhoff: 87
Can we understand noise in biology?
Yes, caused by hierarchiesand other mechanisms
But this does not explain mRNA noise
But with RNA bursting, can this give rise to 2 distinct subpopulations?
Systems Medicine course Como 2016 26/09/2016
Westerhoff et al Page 16
State oscillationsStochastic mRNA bursting
Can bursting give rise to bimodality(two distinct subpopulations)?
Stephania Astrologo (poster here): Yes
Conclusion: There could be a non permanent Janus head (heterogeneity)
due to burstingBut this would not make the
aberrant (tumor) cells selectable
Could you think of a way in which this dynamic heterogeneity could lead to tumorigenesis?
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Unless there is capture of the state, because it produces a single event such as metastasis
Selection pressure for tumorigenesis?X
Could (epi)mutations also give rise to, then selectable heterogeneity?
• Chiara Damiani: Yes
• She developed an FBA method that generates diverse in silico cells with diverse functions
Systems Medicine course Como 2016 26/09/2016
Westerhoff et al Page 17
The menu Towards Precision Biology and Medicine
• The Future in 2000– What remained to be discovered
• Life at the edge and the origin of Life– How to get your Carbon and Gibbs energy precisely (‐‐> Thierry Mondeel)
• Towards precision medicine
– Individualized medicine from PKU to Parkinson’s? (Alexey Kolodkin)
– Transcription dynamics, cell‐cell heterogeneity and cancer ( StephaniaAstrologo)
– How understanding might matter: the Janus head of acute and chronic inflammation
• Serving the community – Infrastructure systems Biology Europe (ISBE@NL): make me a model coalition
(Alexey Kolodkin)
– Replica models, virtual human
Systems Biology and Medicine:
Understanding disease by understanding the
networks of Life
Hans V. Westerhoff and friends:Thierry Mondeel
Stefania AstrologoAlexey Kolodkin
Ablikim AbulikemuSamrina RehmanMalkhey Verma
Lilia Alberghina and SYSBIO-IT