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

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SYSTEMS BIOLOGY AND SYSTEMS MEDICINE: TOWARDS A PRECISION MEDICINE
September 26-30, 2016

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

  1. 1. 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 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 Prepare to vote Voting is anonymous TXT 1 2 Internet 1 2 Twitter 1 2 The text on this slide will instruct your audience on how to vote. This text will only appear once you start a free or a credit session. Please note that the text and appearance of this slide (font, size, color, etc.) cannot be changed. 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 The question will open when you start your session and slideshow. Internet This text box will be used to describe the different message sending methods. TXT The applicable explanations will be inserted after you have started a session. Twitter It is possible to move, resize and modify the appearance of this text box. # Votes: 0 # Persons: 0 Closed
  2. 2. 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 Posting messages is anonymous TXT 1 2 Internet 1 2 Twitter 1 2 The text on this slide will instruct your audience on how to post. This text will only appear once you start a free or a credit session. Please note that the text and appearance of this slide (font, size, color, etc.) cannot be changed. What remained to be discovered in 2000? 1. Your audience's responses will appear here. Please feel free to change the font, color etc. This text disappears after starting your session and slideshow. 2. Your audience's responses will appear here. Please feel free to change the font, color etc. This text disappears after starting your session and slideshow. 3. Your audience's responses will appear here. Please feel free to change the font, color etc. This text disappears after starting your session and slideshow. Internet This text box will be used to describe the different message sending methods. TXT The applicable explanations will be inserted after you have started a session. Twitter It is possible to move, resize and modify the appearance of this text box. # Messages: 0 (0% correct) 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
  3. 3. 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 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…..
  4. 4. Systems Medicine course Como 2016 26/09/2016 Westerhoff et al Page 4 What (type of) evidence would show that a disease is monofactorial? 1. Your audience's responses will appear here. Please feel free to change the font, color etc. This text disappears after starting your session and slideshow. 2. Your audience's responses will appear here. Please feel free to change the font, color etc. This text disappears after starting your session and slideshow. 3. Your audience's responses will appear here. Please feel free to change the font, color etc. This text disappears after starting your session and slideshow. Internet This text box will be used to describe the different message sending methods. TXT The applicable explanations will be inserted after you have started a session. Twitter It is possible to move, resize and modify the appearance of this text box. # Messages: 0 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 The question will open when you start your session and slideshow. Internet This text box will be used to describe the different message sending methods. TXT The applicable explanations will be inserted after you have started a session. Twitter It is possible to move, resize and modify the appearance of this text box. # Votes: 17 Closed How could we explain all these features of present day diseases? Internet This text box will be used to describe the different message sending methods. TXT The applicable explanations will be inserted after you have started a session. Twitter It is possible to move, resize and modify the appearance of this text box. 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
  5. 5. 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 all three pathways have been incapacitated X X X 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 all three pathways have been incapacitated X X X Cause 2 The new paradigm: Network disease 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
  6. 6. 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 cancer Hanahan & Weinberg
  7. 7. Systems Medicine course Como 2016 26/09/2016 Westerhoff et al Page 7 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? The question will open when you start your session and slideshow. Internet This text box will be used to describe the different message sending methods. TXT The applicable explanations will be inserted after you have started a session. Twitter It is possible to move, resize and modify the appearance of this text box. # 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% Internet This text box will be used to describe the different message sending methods. TXT The applicable explanations will be inserted after you have started a session. Twitter It is possible to move, resize and modify the appearance of this text box. 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 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 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?
  8. 8. 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 ( 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 Inborn errors of metabolism Vital constituent food
  9. 9. 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 ✗ Protein Nutrition dopa dopamine Nor‐epinephrin OK Phe Tyr ✗ Example of map utilization tyrosine metabolism: nurture Phenylketone   urine ✗ Protein Nutrition dopa dopamine Nor‐epinephrin Phe Tyr ✗ OKX Phe is essential amino acid ✗ ✗ Example of map utilization tyrosine metabolism: nature Phenylketone   urine ✗ Protein Nutrition dopa dopamine Nor‐epinephrin Phe (Tyr) ✗ OKX Phenylketonuria (PKU) = IEM ✗ ✗
  10. 10. 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 ✗ Protein Nutrition dopa dopamine Nor‐epinephrin Phe Tyr ✗ Phenylketonuria (PKU) = IEM ✗ OK✗ ✗ ✗ Example of map utilization tyrosine metabolism: nature Phenylketone   urine ✗ Protein Nutrition 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 The question will open when you start your session and slideshow. Internet This text box will be used to describe the different message sending methods. TXT The applicable explanations will be inserted after you have started a session. Twitter It is possible to move, resize and modify the appearance of this text box. # 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 We will set these example results to zero once you've started your session and your slide show. In the meantime, feel free to change the looks of your results (e.g. the colors).
  11. 11. Systems Medicine course Como 2016 26/09/2016 Westerhoff et al Page 11 Example of map utilization tyrosine metabolism: Brain: adrenalin Phenylketone   urine ✗ 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) Example of map utilization tyrosine metabolism: Multiple tyrosinemias Phenylketone   urine ✗ Protein ` Nutrition dopa dopamine Nor‐epinephrin Phe Tyr ✗ alkaptonuria tyrosinaemia III ✗ tyrosinaemia I ✗ ✗
  12. 12. 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
  13. 13. 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 ( 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 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) ∆ · ∆ ′ 7/4/2016 Westerhoff:  78
  14. 14. 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) 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) 7/4/2016 Westerhoff:  79 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 7/4/2016 Westerhoff:  80 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 7/4/2016 Westerhoff:  81 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 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?
  15. 15. 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 50 100 150 200 250 300 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 7/4/2016 Westerhoff:  87 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?
  16. 16. Systems Medicine course Como 2016 26/09/2016 Westerhoff et al Page 16 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? 1. Your audience's responses will appear here. Please feel free to change the font, color etc. This text disappears after starting your session and slideshow. 2. Your audience's responses will appear here. Please feel free to change the font, color etc. This text disappears after starting your session and slideshow. 3. Your audience's responses will appear here. Please feel free to change the font, color etc. This text disappears after starting your session and slideshow. Internet This text box will be used to describe the different message sending methods. TXT The applicable explanations will be inserted after you have started a session. Twitter It is possible to move, resize and modify the appearance of this text box. # Messages: 0 (0% correct) 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
  17. 17. 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 ( 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 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

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