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

Systems Medicine course Como 2016 26/09/2016
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Systems Biology and
Medicine:
Understanding disease
by understanding the
networks of Life
Hans V. Westerhoff
and friends
Synthetic Systems Biology, SILS, NISB, the University of Amsterdam, and
Molecular Cell Physiology, NISB, VU University Amsterdam, Amsterdam, NL, EU, and
Manchester Centre for Integrative Systems Biology, Manchester, UK, EU
The second Systems Biology & Systems Medicine  (SyBSyM)
School, 25‐29 September 2016, Como
Please logon to wifi:  SVILUPPOCOMO  PASSWORD:  SEE NOTES OR grumello20
Towards Individualized
Systems Medicine
Hans V. Westerhoff
and friends
Synthetic Systems Biology, SILS, NISB, the University of Amsterdam, and
Molecular Cell Physiology, NISB, VU University Amsterdam, Amsterdam, NL, EU, and
Manchester 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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# Votes: 0
# Persons: 0
Closed

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 the
interactions
Now it is also time
for Systems Medicine

Where Systems Biology made the difference
genomicstranscriptomics
proteomics
metabolomics
structural biology
biophysics biology
biochemistry
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 format
Predicting 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
+ X
Cause
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…..

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 origin
# 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%
0.0%
0.0%
0.0%
0.0%
The old paradigm:
Disease is due to a sick molecule
Impaired function
+ X
Cause
Our new paradigm: Network disease
Impaired function
X
Cause 3
X
Cause 1
X
Cause 2
A network disease
is caused by a
combination of
possibly remote
factors

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 all
three pathways have been incapacitated
X
X
X
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 all
three pathways have been incapacitated
X
X
X
Cause 2
Impaired function
X
Cause 3
X
Cause 1
X
SNP 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

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 cancer
Hanahan & Weinberg

Major Systems Biology accomplishments
for the understanding of disease
• Systems Biology has shown that there is little basis of
looking for the molecule 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 ( Stephania
Astrologo)
– 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
Astrologo)
inflammation
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?

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
Astrologo)
inflammation
Inborn errors of metabolism
Vital constituent
food

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
✗
Protein
Nutrition
dopa
dopamine
Nor‐epinephrin
OK
Phe
Tyr
✗
nurture
✗
Protein
Nutrition
dopa
dopamine
Nor‐epinephrin
Phe
Tyr
✗
OKX
Phe is essential amino acid
✗ ✗
nature
✗
Protein
Nutrition
dopa
dopamine
Nor‐epinephrin
Phe
(Tyr)
✗ OKX
Phenylketonuria (PKU) = IEM
✗ ✗

Can one use the map to design a
therapy?
nature
✗
Protein
Nutrition
dopa
dopamine
Nor‐epinephrin
Phe
Tyr
✗
✗ OK✗
✗ ✗
nature
✗
Protein
Nutrition
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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Brain: adrenalin
✗
Protein
Nutrition
dopa
dopamine
Nor‐epinephrin
Epinephrin=adrenalin
Phe
Tyr
✗ OKX
✗
✗
✗
✗
✗ ✗
Another riddle
Reduced Phe‐intake therapy works
better than Tyr supplementation:
Apparently the problem is not just lack
of tyrosine for protein synthesis
Tyr enters brain in exchange for Phe
63
Phe
Tyr
B
B
B
Westerhoff on Systems Toxicology; slide
Mapping beyond the pathway
64
Also other diseases?
Yes, multiple related diseases
Phenylketonuria (PKU)
Multiple tyrosinemias
✗
Protein
`
Nutrition
dopa
dopamine
Nor‐epinephrin
Phe
Tyr
✗
alkaptonuria
tyrosinaemia III
✗
tyrosinaemia I
✗
✗

Can it help design drug therapy?
67
(Cautioning vis‐à‐vis) drug therapy
✗
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

Astrologo)
inflammation
The Janus head of cells and
Is 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)
∆ · ∆ ′

Example:
Uncertain prediction of Cetuximab
effect on colon cancer
Zalcberg et al, NEJM 2008
(K‐ras wild type)
SU
RV
I
VA
L
Time in months
Some colon cancer patients respond positively to treatment with EGFR
receptor antagonists, whereas others respond much less: Δeffect is large for
any individual (uncertain prediction)
The Bohr‐Einstein debate
Bohr: Fundamentally we cannot know energy and
time precisely for any particle: the particle is a
wave of uncertainty.  This means that in every
new experiment the particle at time t=0 will have
a different energy.
Einstein:  Gott würfelt nicht (God does not throw
dice):  it is just that we do not have sufficient
information about the individual particles.
Statistical:  One measures many particles anyway,
or one over a long time: E can be measured
through the average
Patients with mutated K‐
ras: no effect of
cetuximab
SU
RV
I
VA
L
Time in months Zalcberg et al, NEJM 2008
But for a small group of patients where we have information, we can
predict:
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: Fundamentally we cannot know energy and
time precisely for any particle: the particle is a
wave of uncertainty.  This means that in every
new experiment the particle at time t=0 will have
a different energy.
Einstein:  Gott würfelt nicht (God does not throw
dice):  it is just that we do not have sufficient
information about the individual particles.
Statistical:  One measures many particles anyway,
or one over a long time: E can be measured
through 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?

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 50 100 150 200 250 300
0
50
100
150
# of Protein Molecules
# of Simulations
0 50 100 150 200 250 300
0
50
100
150
# of Product Molecules
# of Simulations
1 2
3 4
DNA
mRNA
Protein
ProductSubstrate
5 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 60
0
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 (σ2/µ)
Hierarchies explain noise
Can we understand noise in
biology?
Yes, caused by hierarchies
and other mechanisms
But this does not explain mRNA
noise
But with RNA bursting, can this give rise to 2 distinct subpopulations?

State oscillations
Stochastic 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 bursting
But 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

The menu Towards Precision Biology and Medicine
Astrologo)
inflammation
Systems Biology and
Medicine:
Understanding disease
by understanding the
networks of Life
Hans V. Westerhoff
and friends:
Thierry Mondeel
Stefania Astrologo
Alexey Kolodkin
Ablikim Abulikemu
Samrina Rehman
Malkhey Verma
Lilia Alberghina and SYSBIO-IT

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

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

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

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