Systems Biology and Medicine: Understanding disease by understanding the networks of Life - Hans V....

Systems Medicine course Como 2016 26/09/2016

Westerhoff et al

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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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

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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


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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…..


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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

F. Diseases do not have a genetic originThe question will open when you start your session and slideshow.


# Votes: 17

Closed

How could we explain all these features of present day diseases?


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


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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


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


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


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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


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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?



# Votes: 19

Closed

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%


Closed

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









(Alexey Kolodkin)


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?


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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









(Alexey Kolodkin)


Inborn errors of metabolism

Vital constituent

food


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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

✗ ✗


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Can one use the map to design a therapy?


Phenylketone urine

✗

ProteinNutrition

dopa

dopamine

Nor‐epinephrin

Phe

Tyr

✗


✗ OK✗✗ ✗


Phenylketone urine

✗

ProteinNutrition

dopa

dopamine

Nor‐epinephrin

Phe

Tyr✗


✗✗ ✗

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



# Votes: 0

Closed

Why does PKU lead to mental retardation specifically?

A.

B.

Brain is the only tissue that contains protein

Blood brain barrier causes a difficulty

0.0%

0.0%

Closed

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Westerhoff et al

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✗

✗


Westerhoff et al

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


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(Alexey Kolodkin)


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)

∆ · ∆ ′



Westerhoff et al

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)


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


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


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


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?


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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?


=1%

DNA

mRNA

Protein

1 2

3 4

But Biology is ‘hierarchical’ and complex


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


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?


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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


Westerhoff et al

The menu Towards Precision Biology and Medicine








(Alexey Kolodkin)


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

Systems Biology and Medicine: Understanding disease by understanding the networks of Life - Hans V....

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