• Save
A Systems Biology Perspective on Plant-Pathogen Interactions 2012-05-08, Turin
Upcoming SlideShare
Loading in...5
×
 

A Systems Biology Perspective on Plant-Pathogen Interactions 2012-05-08, Turin

on

  • 148 views

My presentation from 8th May 2012, at a workshop on Plant-Microbe Interactions, held at the Turin Botanical Gardens, University of Turin. The talk expands on concepts from this paper: Pritchard L, ...

My presentation from 8th May 2012, at a workshop on Plant-Microbe Interactions, held at the Turin Botanical Gardens, University of Turin. The talk expands on concepts from this paper: Pritchard L, Birch P (2011) A systems biology perspective on plant-microbe interactions: Biochemical and structural targets of pathogen effectors. Plant Science 180: 584–603. doi:10.1016/j.plantsci.2010.12.008.

Statistics

Views

Total Views
148
Views on SlideShare
148
Embed Views
0

Actions

Likes
0
Downloads
0
Comments
0

0 Embeds 0

No embeds

Accessibility

Categories

Upload Details

Uploaded via as Adobe PDF

Usage Rights

CC Attribution License

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Processing…
Post Comment
Edit your comment

A Systems Biology Perspective on Plant-Pathogen Interactions 2012-05-08, Turin A Systems Biology Perspective on Plant-Pathogen Interactions 2012-05-08, Turin Presentation Transcript

  • A  Systems  Biology  Perspec2ve  on   Plant-­‐Pathogen  Interac2ons   Leighton  Pritchard  
  • A  Con2nuum   l Pathogenicity  is  a  loaded  term:   l  o4en  reflects  human  interest  in  the  system   l  disease  on  crop  plants  could  be  coincidental  to  ‘wild  type’  interac<ons   l A  con<nuum  of  interac<on  modes,  including  symbiosis  and   pathogenicity   l The  loca<on  of  the  system  on  this  con<nuum  may  depend  on  context   l  e.g.  Pectobacterium  atrosep/cum:potato   no  impact   host  death  
  • A  basic  observa2on   Pathogen  Host   Biological  cells  (and  organisms)  can  be  represented     as  networks  
  • Biological  networks   l Common  way  to  represent  structure   l  Several  biological  subsystems  are  networks   l Universal  representa<on   l  All  biological  systems  have  parts  that  can  be  represented   as  networks   l Networks  (a.k.a.  graphs)  are  mathema<cally  well-­‐ understood:  Graph  Theory   l  Many  tools  exist,  relevant  to  biology  
  • Biological  networks   l Common  way  to  represent  structure   l  Several  biological  subsystems  are  networks   l Universal  representa<on   l  All  biological  systems  have  parts  that  can  be  represented   as  networks   l Networks  (a.k.a.  graphs)  are  mathema<cally  well-­‐ understood:  Graph  Theory   l  Many  tools  exist,  relevant  to  biology  
  • Biological  networks   l Metabolic  networks  (e.g.  KEGG)   (generic)  Michal  (Ed.),  Biochemical  Pathways,  John  Wiley  and  Sons,  New  York,  1999.    
  • Biological  networks   l Regulatory/signalling  networks   (mouse)  (Drosophila)  
  • Biological  networks   l Protein-­‐protein  interac<on  networks   (Arabidopsis/H.arabidopsidis/P.syringae)  (yeast)  
  • Biological  networks   l Common  way  to  represent  structure   l  Several  biological  subsystems  are  networks   l Universal  representa<on   l  All  biological  systems  have  parts  that  can  be  represented   as  networks   l Networks  (a.k.a.  graphs)  are  mathema<cally  well-­‐ understood:  Graph  Theory   l  Many  tools  exist,  relevant  to  biology  
  • What  is  a  network?   l Networks  have  nodes  (a.k.a.  ver<ces)   l  Nodes  typically  represent  ‘things’:   „ proteins,  chemical  compounds,  people,  towns,  junc<ons…   l Nodes  are  connected  by  edges  (a.k.a.  arcs)   l  Edges  typically  indicate  some  rela<onship  between  nodes   „ physical  interac<on,  substrate:product,  friends  on  Facebook   l  Edges  may  be  directed  (from  one  node  to  another)  or  undirected  (no  or   ambiguous  direc<on)   „ chemical  conversion:  directed;  interac<on:  undirected   n1   n2  
  • What  is  a  network?   l Networks  have  nodes  (a.k.a.  ver<ces)   l  Nodes  typically  represent  ‘things’:   „ proteins,  chemical  compounds,  people,  towns,  junc<ons…   l Nodes  are  connected  by  edges  (a.k.a.  arcs)   l  Edges  typically  indicate  some  rela<onship  between  nodes   „ physical  interac<on,  substrate:product,  friends  on  Facebook   l  Edges  may  be  directed  (from  one  node  to  another)  or  undirected  (no  or   ambiguous  direc<on)   „ chemical  conversion:  directed;  interac<on:  undirected   n1   n2  
  • What  is  a  network?   l Networks  have  nodes  (a.k.a.  ver<ces)   l  Nodes  typically  represent  ‘things’:   „ proteins,  chemical  compounds,  people,  towns,  junc<ons…   l Nodes  are  connected  by  edges  (a.k.a.  arcs)   l  Edges  typically  indicate  some  rela<onship  between  nodes   „ physical  interac<on,  substrate:product,  friends  on  Facebook   l  Edges  may  be  directed  (from  one  node  to  another)  or  undirected  (no  or   ambiguous  direc<on)   „ chemical  conversion:  directed;  interac<on:  undirected   n1   n2   n1   n2   n1   n2   n1   n2  
  • Many  things  are  networks   l My  Facebook  friends  network:   l  Nodes:  people   l  Edges:  friendships  between  people   l Useful  concepts  for  biology:   l  ‘friend  of  a  friend’;  ‘six  degrees  of  separa<on’;  clusters  of  friends     Solange Mateo Montalcini Maeve Price Peter Cock Catherine Tackley Gavin Cowie Steffi Keir Yvonne McAvoy Jennifer White Rachel Clewes Juan Morales Karen Faulds David Ian Ellis Laura Banasiak Andrea Semião Daniel Tackley Andrew Lipscombe Bleddyn Hughes Sue Stovell Laura Didymus Hywel Griffiths Charles Twist Christian Payne Helen Johnson Phil Parsonage Colin McGill Allan N. Gunn Will Allwood Katherine Hollywood Judith Robertson Andrew Murdoch David Broadhurst Lydia Castelli Miles Armstrong Paul Keir Fiona White Gagg Lizzie Wilberforce Joanne Fitchet Laura Baxter Alison Gilhespie Jorunn Bos James Gagg Andy Smith Clare Baxter Susan Somerville Neil Bhaduri Joanna Jones Colleen Gagg Susan Quinn McGhee Al Macmillan Norman StewartKevin Knox Susan BreenMichael Barrow Phil Dennison Andrew McKenzie Matthew Blackburn Christelle Robert Tim Arrowsmith Emma Robertson Jane Ballany Chris Thorpe Andrew Dalke Sonia Humphris Juan Morales Eleanor Gilroy Chris McDonald Natalie Homer Anna Åsman Ruth Polwart Tim Morley Kenny Duncan Iddo Friedberg Remco Stam Ramesh Vetukuri Louise Matheson Simon Easterman Philip Law Craig Shaddy Shadbolt Simon Garrett Agata Kaczmarek Simon Pendlebury Rays Jiang Christiane AusJena Pedro Mendes Iris Stone Ingo Hein Adriana Ravagnani Eduard Venter Charles Gordon David Cooke Jonathan Gagg Roger Jarvis Ross McMahon Stefan Engelhardt Edgar Huitema Thomas Pritchard Tracy Canham Sophien Kamoun Florietta Jupe Ambreen Owen Hazel McLellan
  • Many  things  are  networks   l My  Facebook  friends  network:   l  Nodes:  people   l  Edges:  friendships  between  people   l Useful  concepts  for  biology:   l  ‘friend  of  a  friend’;  ‘six  degrees  of  separa<on’;  clusters  of  friends     Solange Mateo Montalcini Maeve Price Peter Cock Catherine Tackley Gavin Cowie Steffi Keir Yvonne McAvoy Jennifer White Rachel Clewes Juan Morales Karen Faulds David Ian Ellis Laura Banasiak Andrea Semião Daniel Tackley Andrew Lipscombe Bleddyn Hughes Sue Stovell Laura Didymus Hywel Griffiths Charles Twist Christian Payne Helen Johnson Phil Parsonage Colin McGill Allan N. Gunn Will Allwood Katherine Hollywood Judith Robertson Andrew Murdoch David Broadhurst Lydia Castelli Miles Armstrong Paul Keir Fiona White Gagg Lizzie Wilberforce Joanne Fitchet Laura Baxter Alison Gilhespie Jorunn Bos James Gagg Andy Smith Clare Baxter Susan Somerville Neil Bhaduri Joanna Jones Colleen Gagg Susan Quinn McGhee Al Macmillan Norman StewartKevin Knox Susan BreenMichael Barrow Phil Dennison Andrew McKenzie Matthew Blackburn Christelle Robert Tim Arrowsmith Emma Robertson Jane Ballany Chris Thorpe Andrew Dalke Sonia Humphris Juan Morales Eleanor Gilroy Chris McDonald Natalie Homer Anna Åsman Ruth Polwart Tim Morley Kenny Duncan Iddo Friedberg Remco Stam Ramesh Vetukuri Louise Matheson Simon Easterman Philip Law Craig Shaddy Shadbolt Simon Garrett Agata Kaczmarek Simon Pendlebury Rays Jiang Christiane AusJena Pedro Mendes Iris Stone Ingo Hein Adriana Ravagnani Eduard Venter Charles Gordon David Cooke Jonathan Gagg Roger Jarvis Ross McMahon Stefan Engelhardt Edgar Huitema Thomas Pritchard Tracy Canham Sophien Kamoun Florietta Jupe Ambreen Owen Hazel McLellan
  • Many  things  are  networks   l  Google  Maps   l  Nodes:  road  junc<ons  (and  end  points  in  culs  de  sacs)   l  Edges:  roads   l  Structure  view   l  Flow/traffic  view     l  Useful  concepts  for  biology:   l  Network  ‘flow’  or  ‘flux’;  distance  on  a  network;  shortest  path  
  • Many  things  are  networks   l  Google  Maps   l  Nodes:  road  junc<ons  (and  end  points  in  culs  de  sacs)   l  Edges:  roads   l  Structure  view   l  Flow/traffic  view     l  Useful  concepts  for  biology:   l  Network  ‘flow’  or  ‘flux’;  distance  on  a  network;  shortest  path  
  • Many  things  are  networks   l  Google  Maps   l  Nodes:  road  junc<ons  (and  end  points  in  culs  de  sacs)   l  Edges:  roads   l  Structure  view   l  Flow/traffic  view     l  Useful  concepts  for  biology:   l  Network  ‘flow’  or  ‘flux’;  distance  on  a  network;  shortest  path  
  • Networks  are  abstract   l Networks  are  collec<ons  of  nodes  and  edges   l Proper<es  of  the  network  are  the  proper<es  of  that  collec<on   l  What  a  node  or  edge  represents  is  not  important   l If  a  network  describes  biology  well…   l  …what  is  true  about  the  network  will  be  true  about  the  biology   l  (some  networks  describe  biology  be`er  than  others)   l Abstract  truths  about  networks  can  be  true  about  biology   l  If  a  network  of  type  X  is  robust  to  random  damage,  and  a  biological   network  is  of  type  X,  we  can  say  that  the  biological  network  is  robust  to   random  damage.  
  • Networks  are  abstract   l Networks  are  collec<ons  of  nodes  and  edges   l Proper<es  of  the  network  are  the  proper<es  of  that  collec<on   l  What  a  node  or  edge  represents  is  not  important   l If  a  network  describes  biology  well…   l  …what  is  true  about  the  network  will  be  true  about  the  biology   l  (some  networks  describe  biology  be`er  than  others)   l Abstract  truths  about  networks  can  be  true  about  biology   l  If  a  network  of  type  X  is  robust  to  random  damage,  and  a  biological   network  is  of  type  X,  we  can  say  that  the  biological  network  is  robust  to   random  damage.  
  • Networks  are  abstract  
  • Networks  are  abstract  
  • Networks  are  abstract   l Networks  are  collec<ons  of  nodes  and  edges   l Proper<es  of  the  network  are  the  proper<es  of  that  collec<on   l  What  a  node  or  edge  represents  is  not  important   l If  a  network  describes  biology  well…   l  …what  is  true  about  the  network  will  be  true  about  the  biology   l  (some  networks  describe  biology  be`er  than  others)   l Abstract  truths  about  networks  can  be  true  about  biology   l  If  a  network  of  type  X  is  robust  to  random  damage,  and  a  biological   network  is  of  type  X,  we  can  say  that  the  biological  network  is  robust  to   random  damage.  
  • Networks  are  abstract   l Networks  are  collec<ons  of  nodes  and  edges   l Proper<es  of  the  network  are  the  proper<es  of  that  collec<on   l  What  a  node  or  edge  represents  is  not  important   l If  a  network  describes  biology  well…   l  …what  is  true  about  the  network  will  be  true  about  the  biology   l  (some  networks  describe  biology  be`er  than  others)    
  • Networks  are  abstract   l Networks  are  collec<ons  of  nodes  and  edges   l Proper<es  of  the  network  are  the  proper<es  of  that  collec<on   l  What  a  node  or  edge  represents  is  not  important   l If  a  network  describes  biology  well…   l  …what  is  true  about  the  network  will  be  true  about  the  biology   l  (some  networks  describe  biology  be`er  than  others)     n1   n2   n3   n4   n5  
  • Networks  are  abstract   l Networks  are  collec<ons  of  nodes  and  edges   l Proper<es  of  the  network  are  the  proper<es  of  that  collec<on   l  What  a  node  or  edge  represents  is  not  important   l If  a  network  describes  biology  well…   l  …what  is  true  about  the  network  will  be  true  about  the  biology   l  (some  networks  describe  biology  be`er  than  others)   l Any  network  with  this  structure  has  the  same     behaviour   l  Behaviour  of  specific  regulatory  network  is  dictated   by  its  structure:   l  Behaviour  dependent  on  structure  of  system  as  a     whole:  need  to  understand  this  at  a  systems  level   MacLean  and  Studholme.  A  Boolean  model  of  the  Pseudomonas  syringae  hrp  regulon  predicts  a  <ghtly  regulated  system.  PLoS  ONE  (2010)  vol.  5  (2)  pp.  e9101   doi:10.1371/journal.pone.0009101  
  • Networks  are  abstract   l Networks  are  collec<ons  of  nodes  and  edges   l Proper<es  of  the  network  are  the  proper<es  of  that  collec<on   l  What  a  node  or  edge  represents  is  not  important   l If  a  network  describes  biology  well…   l  …what  is  true  about  the  network  will  be  true  about  the  biology   l  (some  networks  describe  biology  be`er  than  others)   l Abstract  truths  about  networks  can  be  true  about  the  biology  they   represent   l  If  a  network  of  type  X  is  robust  to  random  damage,  and  a  biological   network  is  of  type  X,  we  can  say  that  the  biological  network  is  robust  to   random  damage.  
  • Choosing  a  representa2on   l Network  should  be  an  adequate  representa<on  of  biology   l  Choice  of  representa<on  should  suit  biological  ques<on   l  e.g.  do  we  represent  chemical  compounds,  or  moie<es?  
  • Choosing  a  representa2on   l Network  should  be  an  adequate  representa<on  of  biology   l  Choice  of  representa<on  should  suit  biological  ques<on   l  e.g.  do  we  represent  chemical  compounds,  or  moie<es?  
  • Choosing  a  representa2on   l Network  should  be  an  adequate  representa<on  of  biology   l  Choice  of  representa<on  should  suit  biological  ques<on   l  e.g.  do  we  represent  chemical  compounds,  or  moie<es?  
  • Choosing  a  representa2on   l What  does  this  diagram  mean?   l  Are  all  enzymes   expressed  at  same  <me?   l  Are  all  enzymes   expressed  in  all  <ssues?   l  Are  all  metabolites   always  available?   l  30-­‐40%  of  metabolic   ac<vity  has  no  known   gene  associated  with  it   (Chen  and  Vitkup.  Distribu<on  of  orphan   metabolic  ac<vi<es.  Trends  Biotechnol   (2007)  vol.  25  (8)  pp.  343-­‐348  doi: 10.1016/j.<btech.2007.06.001)   Michal  (Ed.),  Biochemical  Pathways,  John  Wiley  and  Sons,  New  York,  1999.    
  • Choosing  a  representa2on   l What  does  this  diagram  mean?   l  Are  all  enzymes   expressed  at  same  <me?   l  Are  all  enzymes   expressed  in  all  <ssues?   l  Are  all  metabolites   always  available?   l  30-­‐40%  of  metabolic   ac<vity  has  no  known   gene  associated  with  it   (Chen  and  Vitkup.  Distribu<on  of  orphan   metabolic  ac<vi<es.  Trends  Biotechnol   (2007)  vol.  25  (8)  pp.  343-­‐348  doi: 10.1016/j.<btech.2007.06.001)   Michal  (Ed.),  Biochemical  Pathways,  John  Wiley  and  Sons,  New  York,  1999.    
  • Choosing  a  representa2on   l Biological  networks  are  dynamic   l  There  may  be  homeostasis,  but  it’s  dynamic  homeostasis   l  “The  only  steady-­‐state  is  death”   l What  kind  of  dynamics?   l  Kine<c  equa<ons   l  ODE/Stochas<c  representa<on  of  processes   „ e.g.  enzyme  kine<cs   E + S ⌦ ES ⌦ EP ! E + P v = [S]Vmax [S] + [Km]
  • Choosing  a  representa2on   l Biological  networks  are  dynamic   l  There  may  be  homeostasis,  but  it’s  dynamic  homeostasis   l  “The  only  steady-­‐state  is  death”   l What  kind  of  dynamics?   l  Kine<c  equa<ons   l  ODE/Stochas<c  representa<on  of  processes   „ e.g.  enzyme  kine<cs   E + S ⌦ ES ⌦ EP ! E + P v = [S]Vmax [S] + [Km]
  • Choosing  a  representa2on   l Biological  networks  are  dynamic   l  There  may  be  homeostasis,  but  it’s  dynamic  homeostasis   l  “The  only  steady-­‐state  is  death”   l What  kind  of  dynamics?   l  Boolean  (on/off,  0/1)   „ e.g.  regula<on/signalling   nodes   <me  
  • Host-­‐pathogen  interac2on   Pathogen  Host   A  representa<on  of  host  and  pathogen  as  two  networks  
  • Host-­‐pathogen  interac2on   Pathogen  Host   PAMP/MAMP  detec<on:  host  immune  receptor  detects  (interacts   with)  non-­‐self  chemical  species  derived  from  microbe/pathogen  
  • Host-­‐pathogen  interac2on   Pathogen  Host   Effector  ac<on  I:  pathogen-­‐derived  species  (probably  protein)   interacts  with  host  network  component  
  • Host-­‐pathogen  interac2on   Pathogen  Host   Effector  ac<on  II:  pathogen-­‐derived  species  (probably  protein)   manipulates  (interacts  with)  host  network  process  
  • Host-­‐pathogen  interac2on   Pathogen  Host   Effector-­‐triggered  resistance  I:  host  immune  receptor  interacts  with   pathogen-­‐derived  effector  
  • Host-­‐pathogen  interac2on   Pathogen  Host   Effector-­‐triggered  resistance  II:  host  immune  receptor  detects  self-­‐   modifica<on  (induced  by  pathogen  effector)  
  • Host-­‐pathogen  interac2on   Pathogen  Host   Host-­‐pathogen  interac2on  is  the  coming  together  of  two  networks  into  a  single   network:  different  proper2es  than  either  network  alone  
  • Host-­‐pathogen  interac2on   Pathogen  Host   How  does  this  affect  culturability?     Tight  connec2on  correlates  with  obligate  biotrophy,  hence  difficult  to  culture?  
  • Host-­‐pathogen  interac2on  
  • Host-­‐pathogen  interac2on   Pathogen   Host   l How  does  host/pathogen  network  respond  to  interac<on?   l What  is  best  way  to  a`ack  a  network?   l What  is  best  way  to  defend  against  mul<ple  a`ack  strategies?   l Are  some  parts  of  a  network  predictably  more  influen<al  than  others?  
  • Host-­‐pathogen  interac2on   Pathogen   Host   l How  does  host/pathogen  network  respond  to  interac<on?   l What  is  best  way  to  a`ack  a  network?   l What  is  best  way  to  defend  against  mul<ple  a`ack  strategies?   l Are  some  parts  of  a  network  predictably  more  influen<al  than  others?  
  • Host-­‐pathogen  interac2on   Pathogen   Host   l How  does  host/pathogen  network  respond  to  interac<on?   l What  is  best  way  to  a`ack  a  network?   l What  is  best  way  to  defend  against  mul<ple  a`ack  strategies?   l Are  some  parts  of  a  network  predictably  more  influen<al  than  others?  
  • Host-­‐pathogen  interac2on   Pathogen   Host   l How  does  host/pathogen  network  respond  to  interac<on?   l What  is  best  way  to  a`ack  a  network?   l What  is  best  way  to  defend  against  mul<ple  a`ack  strategies?   l Are  some  parts  of  a  network  predictably  more  influen<al  than  others?  
  • Influence  in  networks   l Efficient  a`ackers:     l  cause  greatest  favourable  host  disrup<on  for  least  effort     l  should  target  influen<al  points  in  host  network   l Efficient  defenders:   l  protect  against  greatest  amount  of  poten<al  change  for  least  effort   l  protect  against  most  commonly-­‐targeted  points  in  network   l  should  target  influen<al  points  in  host  network   l Greatest  benefit  for  least  cost   l What  are  the  most  influen<al  points  in  a  network?  
  • Influence  in  networks   l Efficient  a`ackers:     l  cause  greatest  favourable  host  disrup<on  for  least  effort     l  should  target  influen<al  points  in  host  network   l Efficient  defenders:   l  protect  against  greatest  amount  of  poten<al  change  for  least  effort   l  protect  against  most  commonly-­‐targeted  points  in  network   l  should  target  influen<al  points  in  host  network   l Greatest  benefit  for  least  cost   l What  are  the  most  influen<al  points  in  a  network?  
  • Influence  in  networks   l Efficient  a`ackers:     l  cause  greatest  favourable  host  disrup<on  for  least  effort     l  should  target  influen<al  points  in  host  network   l Efficient  defenders:   l  protect  against  greatest  amount  of  poten<al  change  for  least  effort   l  protect  against  most  commonly-­‐targeted  points  in  network   l  should  target  influen<al  points  in  host  network   l Greatest  benefit  for  least  cost   l What  are  the  most  influen<al  points  in  a  network?  
  • Influence  in  networks   l Efficient  a`ackers:     l  cause  greatest  favourable  host  disrup<on  for  least  effort     l  should  target  influen<al  points  in  host  network   l Efficient  defenders:   l  protect  against  greatest  amount  of  poten<al  change  for  least  effort   l  protect  against  most  commonly-­‐targeted  points  in  network   l  should  target  influen<al  points  in  host  network   l Greatest  benefit  for  least  cost   l What  are  the  most  influen2al  points  in  a  network?   l  can  we  predict/iden<fy  them?  
  • Robustness  in  biological  networks   l Biological  networks  are  typically  robust  and  error-­‐tolerant   l  (necessary  for  descent  with  modifica<on)   l  e.g.  only  17%  of  yeast  genes  essen<al  to  cell  viability  in  rich  media   Winzeler  et  al.  Func<onal  characteriza<on  of  the  S.  cerevisiae  genome  by  gene  dele<on   and  parallel  analysis.  Science  (1999)  vol.  285  (5429)  pp.  901-­‐906  
  • Robustness  in  biological  networks   l Biological  networks  are  typically  robust  and  error-­‐tolerant   l  (necessary  for  descent  with  modifica<on)   l  e.g.  only  17%  of  yeast  genes  essen<al  to  cell  viability  in  rich  media   Winzeler  et  al.  Func<onal  characteriza<on  of  the  S.  cerevisiae  genome  by  gene  dele<on   and  parallel  analysis.  Science  (1999)  vol.  285  (5429)  pp.  901-­‐906  
  • Structural  robustness  in  biological  networks   l Some  network  structures  enhance  robustness   l  Many  biological  networks  have  converged  to  same  network  structures   Barabási  and  Oltvai.  Network  biology:  understanding  the  cell's  func<onal  organiza<on.  Nat  Rev  Genet  (2004)  vol.  5  (2)  pp.  101-­‐13  doi: 10.1038/nrg1272   Kitano.  Biological  robustness.  Nat  Rev  Genet  (2004)  vol.  5  (11)  pp.  826-­‐37  doi:10.1038/nrg1471   •  Aa:  random  Erdös-­‐Renyi  graph:  not  robust  to  random  a`ack  (not  common  in  biology)   •  Ba:  random  ‘scale-­‐free’  network:  robust  to  random  a`ack  (most  biological  networks)   •  Ca:  hierarchical  network:  robust  to  random  a`ack  (many  signalling  networks)  
  • l Some  network  structures  enhance  robustness   l  Many  biological  networks  have  converged  to  same  network  structures   Barabási  and  Oltvai.  Network  biology:  understanding  the  cell's  func<onal  organiza<on.  Nat  Rev  Genet  (2004)  vol.  5  (2)  pp.  101-­‐13  doi: 10.1038/nrg1272   Kitano.  Biological  robustness.  Nat  Rev  Genet  (2004)  vol.  5  (11)  pp.  826-­‐37  doi:10.1038/nrg1471   •  Aa:  random  Erdös-­‐Renyi  graph:  not  robust  to  random  a`ack  (not  common  in  biology)   •  Ba:  random  ‘scale-­‐free’  network:  robust  to  random  a`ack  (most  biological  networks)   •  Ca:  hierarchical  network:  robust  to  random  a`ack  (many  signalling  networks)   Structural  robustness  in  biological  networks  
  • l Network  bridges/bo`lenecks   l  essen<al  intermediate  nodes  in  a  network   l  dele<on  or  disrup<on  dissociates  (breaks)  the  network   Structural  robustness  in  biological  networks   •  Pathways  from  detec<on  (e.g.  immune   recep<on)  to  host  response   •  Signalling  pathways   •  E.g.  Cladosporum  fulvum  Avr4  suppresses   produc<on  of  chi<n,  a  ‘bridge’  
  • l Network  bridges/bo`lenecks   l  essen<al  intermediate  nodes  in  a  network   l  dele<on  or  disrup<on  dissociates  (breaks)  the  network   Structural  robustness  in  biological  networks   MAMP   detec2on   •  Pathways  from  detec<on  (e.g.  immune   recep<on)  to  host  response   •  Signalling  pathways   •  e.g.  Cladosporum  fulvum  Avr4  suppresses   produc<on  of  chi<n,  a  ‘bridge’  
  • l Network  bridges/bo`lenecks   l  essen<al  intermediate  nodes  in  a  network   l  dele<on  or  disrup<on  dissociates  (breaks)  the  network   Structural  robustness  in  biological  networks   MAMP   detec2on   •  Pathways  from  detec<on  (e.g.  immune   recep<on)  to  host  response   •  Signalling  pathways   •  E.g.  Cladosporum  fulvum  Avr4  suppresses   produc<on  of  chi<n,  a  ‘bridge’   chi<n   chi<nase  
  • l Network  bridges/bo`lenecks   l  essen<al  intermediate  nodes  in  a  network   l  dele<on  or  disrup<on  dissociates  (breaks)  the  network   Structural  robustness  in  biological  networks   MAMP   detec2on   •  Pathways  from  detec<on  (e.g.  immune   recep<on)  to  host  response   •  Signalling  pathways   •  E.g.  Cladosporum  fulvum  Avr4  suppresses   produc<on  of  chi<n,  a  ‘bridge’   chi<n   chi<nase   Avr4  
  • l Network  bridges/bo`lenecks   l  essen<al  intermediate  nodes  in  a  network   l  dele<on  or  disrup<on  dissociates  (breaks)  the  network   Structural  robustness  in  biological  networks   MAMP   detec2on   •  Redundancy  and  cross-­‐talk  in  signalling   pathways  protects  against  this  fragility   •  e.g.  PTI/ETI  cross-­‐talk  
  • l Network  hubs   l  highly-­‐connected  nodes   l  characteris<c  of  ‘scale-­‐free’  (and  similar)  networks   l  dele<on  or  disrup<on  dissociates  (breaks)  the  network   Structural  robustness  in  biological  networks   •  Why  do  hubs  occur?   •  How  many  hubs  do  we  expect?   •  How  are  they  related  to  biology?  
  • l Network  hubs   l  highly-­‐connected  nodes   l  characteris<c  of  ‘scale-­‐free’  (and  similar)  networks   l  dele<on  or  disrup<on  dissociates  (breaks)  the  network   Structural  robustness  in  biological  networks   •  Why  do  hubs  occur?   •  How  many  hubs  do  we  expect?   •  How  are  they  related  to  biology?  
  • l Power-­‐law  (a.k.a.  ‘scale-­‐free’)  networks   l  Robust  because  of  node  degree  distribu<on   l  Very  few  ‘hubs’;  most  nodes  make  few  connec<ons   l  Random  dele<on  more  likely  to  remove  node  with  few  connec<ons   Structural  robustness  in  biological  networks   Albert  et  al.  Error  and  a`ack  tolerance  of  complex  networks.  Nature  (2000)  vol.  406  (6794)  pp.  378-­‐82  doi: 10.1038/35019019  
  • l Power-­‐law  (a.k.a.  ‘scale-­‐free’)  networks   l  Robust  because  of  node  degree  distribu<on   l  Very  few  ‘hubs’;  most  nodes  make  few  connec<ons   l  Random  dele<on  more  likely  to  remove  node  with  few  connec<ons   Structural  robustness  in  biological  networks   Albert  et  al.  Error  and  a`ack  tolerance  of  complex  networks.  Nature  (2000)  vol.  406  (6794)  pp.  378-­‐82  doi: 10.1038/35019019  
  • l Power-­‐law  (a.k.a.  ‘scale-­‐free’)  networks   l  Diagnos<c  ‘degree  distribu<on’  (count  of  connec<ons  to  each  node)   l  Yeast  protein  interac<on  network  has  power-­‐law  distribu<on   l  Essen<al  17%  of  genes  correlated  with  highly-­‐connected  nodes  (hubs)   Structural  robustness  in  biological  networks  
  • l Power-­‐law  (a.k.a.  ‘scale-­‐free’)  networks   l  Diagnos<c  ‘degree  distribu<on’  (count  of  connec<ons  to  each  node)   l  Yeast  protein  interac<on  network  has  power-­‐law  distribu<on   l  Essen<al  17%  of  genes  correlated  with  highly-­‐connected  nodes  (hubs)   Structural  robustness  in  biological  networks  
  • l Power-­‐law  (a.k.a.  ‘scale-­‐free’)  networks   l  Most  studied  biological  networks  are  ‘scale-­‐free’   l  ‘Scale-­‐free’  property  proposed  to  arise  from  network  evolu<on   l  ‘older’  nodes  more  likely  to  be  hubs   l  ‘older’  nodes  more  likely  to  be  func<onally-­‐conserved,  sequence   constrained?   l  Hubs  are  good  targets  for  network  disrup<on:  what  role  do  they  play  in   pathogen/host  evolu<on?   Structural  robustness  in  biological  networks  
  • l Power-­‐law  (a.k.a.  ‘scale-­‐free’)  networks   l  Most  studied  biological  networks  are  ‘scale-­‐free’   l  ‘Scale-­‐free’  property  proposed  to  arise  from  network  evolu<on   l  ‘older’  nodes  more  likely  to  be  hubs   l  ‘older’  nodes  more  likely  to  be  func<onally-­‐conserved,  sequence   constrained?   l  Hubs  are  good  targets  for  network  disrup<on:  what  role  do  they  play  in   pathogen/host  evolu<on?   Structural  robustness  in  biological  networks  
  • l Bacterial  Type  III  effectors  engage  a  limited  set  of  host  processes   across  host  kingdoms  e.g.:   l  turnover  by  modula<on  of  ubiqui<na<on     l  altera<on  of  transcrip<on   l  altera<on  of  phosphoryla<on     l Strategies  such  as  the  targe<ng  of  ubiqui<na<on  are  used  by  bacterial   fungal  and  oomycete  pathogens  across  a  range  of  hosts   Structural  robustness  in  biological  networks  
  • l Bacterial  Type  III  effectors  engage  a  limited  set  of  host  processes   across  host  kingdoms  e.g.:   l  turnover  by  modula<on  of  ubiqui<na<on     l  altera<on  of  transcrip<on   l  altera<on  of  phosphoryla<on     l Strategies  such  as  the  targe<ng  of  ubiqui<na<on  are  used  by  bacterial   fungal  and  oomycete  pathogens  across  a  range  of  hosts   Structural  robustness  in  biological  networks  
  • The  Guard  Hypothesis   l The  Guard  Hypothesis  describes  indirect  R  gene:effector  interac<on   l  Direct  R  gene:effector  interac<on  could  lead  to  overwhelming  R  gene  load   l  A.  thaliana  has  ≈200  R  genes  (1%  of  gene  complement)   l If  ‘hubs’  are  common  targets  for  pathogens…   l  …guarding  the  hub  with  one  R  gene  is     more  efficient  than  gene-­‐for-­‐gene     interac<ons   l  …network  topology  implies  the  Guard     Hypothesis   l If  ‘hubs’  are  universal  targets…   l  …network  topology  determines  which     nodes  are  likely  to  be  involved  in     host-­‐pathogen  interac<on   Dangl  and  Jones.  Plant  pathogens  and  integrated     defence  responses  to  infec<on.  Nature  (2001)     vol.  411  (6839)  pp.  826-­‐33  doi:10.1038/35081161  
  • The  Guard  Hypothesis   l The  Guard  Hypothesis  describes  indirect  R  gene:effector  interac<on   l  Direct  R  gene:effector  interac<on  could  lead  to  overwhelming  R  gene  load   l  A.  thaliana  has  ≈200  R  genes  (1%  of  gene  complement)   l If  ‘hubs’  are  common  targets  for  pathogens…   l  …guarding  the  hub  with  one  R  gene  is     more  efficient  than  gene-­‐for-­‐gene     interac<ons   l  …network  topology  implies  the  Guard     Hypothesis   l If  ‘hubs’  are  universal  targets…   l  …network  topology  determines  which     nodes  are  likely  to  be  involved  in     host-­‐pathogen  interac<on   Dangl  and  Jones.  Plant  pathogens  and  integrated     defence  responses  to  infec<on.  Nature  (2001)     vol.  411  (6839)  pp.  826-­‐33  doi:10.1038/35081161  
  • Dangl  and  Jones.  Plant  pathogens  and  integrated     defence  responses  to  infec<on.  Nature  (2001)     vol.  411  (6839)  pp.  826-­‐33  doi:10.1038/35081161   The  Guard  Hypothesis   l The  Guard  Hypothesis  describes  indirect  R  gene:effector  interac<on   l  Direct  R  gene:effector  interac<on  could  lead  to  overwhelming  R  gene  load   l  A.  thaliana  has  ≈200  R  genes  (1%  of  gene  complement)   l If  ‘hubs’  are  common  targets  for  pathogens…   l  …guarding  the  hub  with  one  R  gene  is     more  efficient  than  gene-­‐for-­‐gene     interac<ons   l  …network  topology  implies  the  Guard     Hypothesis   l If  ‘hubs’  are  universal  targets…   l  …network  topology  determines  which     nodes  are  likely  to  be  involved  in     host-­‐pathogen  interac<on  
  • Interac2ons  with  hubs   l Host:  Arabidopsis  thaliana   l Pathogens:  Pseudomonas  syringae,  Hyaloperonospora  arabidopsidis   l  Independent  effector  evolu<on   l  Matrix-­‐2-­‐hybrid  (yeast-­‐2-­‐hybrid)   l  Pathogen  effectors  share  more  common   targets  than  expected  (if  random)   l  Common  targets  more  highly  connected   (i.e.  are  ‘hubs’)  than  expected  (if  random)   Mukhtar  MS,  et  al.  (2011)  Independently  evolved  virulence  effectors  converge  onto  hubs  in  a  plant  immune   system  network.  Science  333:  596–601.  doi:10.1126/science.1203659.  
  • Interac2ons  with  hubs   l Host:  Arabidopsis  thaliana   l Pathogens:  Pseudomonas  syringae,  Hyaloperonospora  arabidopsidis   l  Independent  effector  evolu<on   l  Matrix-­‐2-­‐hybrid  (yeast-­‐2-­‐hybrid)   l  Pathogen  effectors  share  more  common   targets  than  expected  (if  random)   l  Common  targets  more  highly  connected   (i.e.  are  ‘hubs’)  than  expected  (if  random)   Mukhtar  MS,  et  al.  (2011)  Independently  evolved  virulence  effectors  converge  onto  hubs  in  a  plant  immune   system  network.  Science  333:  596–601.  doi:10.1126/science.1203659.  
  • Interac2ons  with  hubs   l Host:  Arabidopsis  thaliana   l Pathogens:  Pseudomonas  syringae,  Hyaloperonospora  arabidopsidis   l  Independent  effector  evolu<on   l  Matrix-­‐2-­‐hybrid  (yeast-­‐2-­‐hybrid)   l  Pathogen  effectors  share  more  common   targets  than  expected  (if  random)   l  Common  targets  more  highly  connected   (i.e.  are  ‘hubs’)  than  expected  (if  random)   Mukhtar  MS,  et  al.  (2011)  Independently  evolved  virulence  effectors  converge  onto  hubs  in  a  plant  immune   system  network.  Science  333:  596–601.  doi:10.1126/science.1203659.  
  • Modules  in  networks   l Mo<fs  are  small  subnetworks   l  Many  have  specific  dynamic  and  logic   behaviour:   „ Accelerate/slow  response   „ Enforce  sequen<al  responses   „ Lock  signal  on  or  off   „ Filter  out  noise  in  signals   „ Generate  pulse  in  response  to     signal   „ Generate  oscilla<ons   „ Integrate  and  process  mul<ple     signals   Shoval  and  Alon.  SnapShot:  network  mo<fs.  Cell  (2010)  vol.  143  (2)  pp.  326-­‐e1  doi:10.1016/j.cell.2010.09.050  
  • Modules  in  networks   l Mo<fs  are  small  subnetworks   l  Many  have  specific  dynamic  and  logic   behaviour:   „ Accelerate/slow  response   „ Enforce  sequen<al  responses   „ Lock  signal  on  or  off   „ Filter  out  noise  in  signals   „ Generate  pulse  in  response  to     signal   „ Generate  oscilla<ons   „ Integrate  and  process  mul<ple     signals   Shoval  and  Alon.  SnapShot:  network  mo<fs.  Cell  (2010)  vol.  143  (2)  pp.  326-­‐e1  doi:10.1016/j.cell.2010.09.050  
  • Modules  in  networks   l Mo<fs  are  small  subnetworks   l  Many  have  specific  dynamic  and  logic   behaviour:   „ Generate  pulse  in  response  to     signal   „ Generate  oscilla<ons   Shoval  and  Alon.  SnapShot:  network  mo<fs.  Cell  (2010)  vol.  143  (2)  pp.  326-­‐e1  doi:10.1016/j.cell.2010.09.050  
  • Modules  in  networks   l Bow-­‐<e  structure   l Many  inputs  →  restricted  set  of  intermediates  →  many  outputs  
  • Modules  in  networks   l Bow-­‐<e  structure   l Many  inputs  →  restricted  set  of  intermediates  →  many  outputs   l  e.g.  complex  nutrients  →  metabolic  intermediates  →  complex  compounds  
  • Modules  in  networks   l Open  ques<ons:   l  Do  a`ackers  preferen<ally  target  (or  introduce)  par<cular  mo<fs?   l  Do  a`ackers  preferen<ally  target  the  ‘knots’  of  bow-­‐<e  structures?  
  • Influence  in  networks   l Network  structure  (topology)  is  not  everything   l Network  topology  is  determined  by  dynamic  processes   n1   n2   n3   n4   n5   n1   n2   n3   n4   n5   n1   n2   n3   n4   n5   Idealised  topology     Expression  pa`ern  1   Expression  pa`ern  2  
  • Influence  in  networks   l Network  structure  (topology)  is  not  everything   l Dynamic  processes  are  overlaid  on  topology   n1   n2   n3   n4   n5   n1   n2   n3   n4   n5   Idealised  topology     Reac<on  kine<cs  dictate   rela<ve  flux   v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km]
  • Metabolic  Control  Analysis  (MCA)   l Metabolic  Control  Analysis  (MCA)   l Some  processes  more  influen<al  because  of  dynamic  (kine<c)   considera<ons   l  ODE  representa<on  of  biochemical   network   l  Used  to  understand  biochemical     pathways   l  Used  in  ra<onal  drug  design:   target/priori<se  elements  with   large  control  coefficients   n1   n2   n3   n4   n5   Reac<on  kine<cs  dictate   rela<ve  flux   v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] Kacser  and  Burns.  The  molecular  basis  of  dominance.  Gene/cs  (1981)  vol.  97  (3-­‐4)  pp.  639-­‐66   Kacser  and  Burns.  The  control  of  flux.  Biochem  Soc  Trans  (1995)  vol.  23  (2)  pp.  341-­‐66   Westerhoff  and  Kell.  What  biotechnologists  knew  all  along  ...?.  J  Theor  Biol  (1996)  vol.  182  (3)   pp.  411-­‐420   Sato  et  al.  Network  Modeling  Reveals  Prevalent  Nega<ve  Regulatory  Rela<onships  between   Signaling  Sectors  in  Arabidopsis  Immune  Signaling.  PLoS  Pathog  (2010)  vol.  6  (7)  pp.  E1001011   doi:10.1371/journal.ppat.1001011  
  • Metabolic  Control  Analysis  (MCA)   l Metabolic  Control  Analysis  (MCA)   l Some  processes  more  influen<al  because  of  dynamic  (kine<c)   considera<ons   l  ODE  representa<on  of  biochemical   network   l  Used  to  understand  biochemical     pathways   l  Used  in  ra<onal  drug  design:   target/priori<se  elements  with   large  control  coefficients   n1   n2   n3   n4   n5   Reac<on  kine<cs  dictate   rela<ve  flux   v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] Kacser  and  Burns.  The  molecular  basis  of  dominance.  Gene/cs  (1981)  vol.  97  (3-­‐4)  pp.  639-­‐66   Kacser  and  Burns.  The  control  of  flux.  Biochem  Soc  Trans  (1995)  vol.  23  (2)  pp.  341-­‐66   Westerhoff  and  Kell.  What  biotechnologists  knew  all  along  ...?.  J  Theor  Biol  (1996)  vol.  182  (3)   pp.  411-­‐420   Sato  et  al.  Network  Modeling  Reveals  Prevalent  Nega<ve  Regulatory  Rela<onships  between   Signaling  Sectors  in  Arabidopsis  Immune  Signaling.  PLoS  Pathog  (2010)  vol.  6  (7)  pp.  E1001011   doi:10.1371/journal.ppat.1001011  
  • Metabolic  Control  Analysis  (MCA)   l Metabolic  Control  Analysis  (MCA)   l Key  points:   l  Rela<ve  change  in  pathway  flux  in  response  to  a  change   in  [enzyme]  is  the  flux  control  coefficient   l  Rela<ve  change  in  [metabolite]  in  response  to  a  change   in  [enzyme]  is  the  concentra2on  control  coefficient   l  Control  coefficient  =  0  ⇒  no  influence   l  Control  coefficient  =  1  ⇒  strong  posi<ve   influence   l  Control  coefficient  =  -­‐1  ⇒  strong  nega<ve   influence   n1   n2   n3   n4   n5   Reac<on  kine<cs  dictate   rela<ve  flux   v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km]
  • Metabolic  Control  Analysis  (MCA)   l Metabolic  Control  Analysis  (MCA)   l Key  points:   l  Rela<ve  change  in  pathway  flux  in  response  to  a  change   in  [enzyme]  is  the  flux  control  coefficient   l  Rela<ve  change  in  [metabolite]  in  response  to  a  change   in  [enzyme]  is  the  concentra2on  control  coefficient   l  Control  coefficient  =  0  ⇒  no  influence   l  Control  coefficient  =  1  ⇒  strong  posi<ve   influence   l  Control  coefficient  =  -­‐1  ⇒  strong  nega<ve   influence   l We    might  expect  aWackers  to  target  network     elements  with  large  control  coefficients   n1   n2   n3   n4   n5   Reac<on  kine<cs  dictate   rela<ve  flux   v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km]
  • Metabolic  Control  Analysis  (MCA)   l Metabolic  Control  Analysis  (MCA)   l Key  points:   l  Control  coefficients  dependent  on  rest  of  network:   calculated  at  same  <me   l  Control  coefficients  are  a  system-­‐level  property   (can’t  be  determined  in  isola<on)   l  It  is  unusual  for  any  single  element  to  have   complete  control  over  any  part  of  the  network   l  (Nearly)  no  rate-­‐limi<ng  steps   l  Any  part  of  the  network  is  typically  under   control  of  mul<ple  other  network  elements   l  Distributed/democra<c  control  is  the  norm   n1   n2   n3   n4   n5   Reac<on  kine<cs  dictate   rela<ve  flux   v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] Pritchard  and  Kell.  Schemes  of  flux  control  in  a  model  of  Saccharomyces  cerevisiae  glycolysis.  Eur  J  Biochem  (2002)  vol.  269  (16)   pp.  3894-­‐904   D.  Fell,  Understanding  the  Control  of  Metabolism,  first  ed.,  Portland  Press,  1997.    
  • Metabolic  Control  Analysis  (MCA)   l Metabolic  Control  Analysis  (MCA)   l Key  points:   l  Control  coefficients  dependent  on  rest  of  network:   calculated  at  same  <me   l  Control  coefficients  are  a  system-­‐level  property   (can’t  be  determined  in  isola<on)   l  It  is  unusual  for  any  single  element  to  have   complete  control  over  any  part  of  the  network   l  (Nearly)  no  rate-­‐limi<ng  steps   l  Any  part  of  the  network  is  typically  under   control  of  mul<ple  other  network  elements   l  Distributed/democra<c  control  is  the  norm   n1   n2   n3   n4   n5   Reac<on  kine<cs  dictate   rela<ve  flux   v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] Pritchard  and  Kell.  Schemes  of  flux  control  in  a  model  of  Saccharomyces  cerevisiae  glycolysis.  Eur  J  Biochem  (2002)  vol.  269  (16)   pp.  3894-­‐904   D.  Fell,  Understanding  the  Control  of  Metabolism,  first  ed.,  Portland  Press,  1997.    
  • Metabolic  Control  Analysis  (MCA)   l Metabolic  Control  Analysis  (MCA)   l Key  points:   l  Control  coefficients  dependent  on  rest  of  network:   calculated  at  same  <me   l  Control  coefficients  are  a  system-­‐level  property   (can’t  be  determined  in  isola<on)   l  It  is  unusual  for  any  single  element  to  have   complete  control  over  any  part  of  the  network   l  (Nearly)  no  rate-­‐limi<ng  steps   l  Any  part  of  the  network  is  typically  under   control  of  mul<ple  other  network  elements   l  Distributed/democra2c  control  is  the  norm   n1   n2   n3   n4   n5   Reac<on  kine<cs  dictate   rela<ve  flux   v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] v = [S]Vmax [S] + [Km] Pritchard  and  Kell.  Schemes  of  flux  control  in  a  model  of  Saccharomyces  cerevisiae  glycolysis.  Eur  J  Biochem  (2002)  vol.  269  (16)   pp.  3894-­‐904   D.  Fell,  Understanding  the  Control  of  Metabolism,  first  ed.,  Portland  Press,  1997.    
  • Metabolic  Control  Analysis  (MCA)   l Yeast  glycolysis   l Most  enzyme  kine<c  parameters  known   l Fit  to  known  fluxes,  then  parameter-­‐scan  (>8000   dis<nct  simula<ons)   l Three  regimes  of  control  found:   l  Main  regime:  only  significant  control  by   hexose  transport  (HXT)  and  hexokinase  (HK)   l  Minor  regime:  HXT,  HK  and  alcohol  dehydrogenase   (ADH)   l  Biologically  inaccessible  regime:  [GLCi]  ≈  300mM     phosphofructokinase  (PFK)  control   Pritchard  and  Kell.  Schemes  of  flux  control  in  a  model  of  Saccharomyces  cerevisiae   glycolysis.  Eur  J  Biochem  (2002)  vol.  269  (16)  pp.  3894-­‐904  
  • Metabolic  Control  Analysis  (MCA)   l Yeast  glycolysis   l Most  enzyme  kine<c  parameters  known   l Fit  to  known  fluxes,  then  parameter-­‐scan  (>8000   dis<nct  simula<ons)   l Three  regimes  of  control  found:   l  Main  regime:  only  significant  control  by   hexose  transport  (HXT)  and  hexokinase  (HK)   l  Minor  regime:  HXT,  HK  and  alcohol  dehydrogenase   (ADH)   l  Biologically  inaccessible  regime:  [GLCi]  ≈  300mM     phosphofructokinase  (PFK)  control   Pritchard  and  Kell.  Schemes  of  flux  control  in  a  model  of  Saccharomyces  cerevisiae   glycolysis.  Eur  J  Biochem  (2002)  vol.  269  (16)  pp.  3894-­‐904  
  • Metabolic  Control  Analysis  (MCA)   l Yeast  glycolysis   l Most  enzyme  kine<c  parameters  known   l Fit  to  known  fluxes,  then  parameter-­‐scan  (>8000   dis<nct  simula<ons)   l Three  regimes  of  control  found:   l  Main  regime:  only  significant  control  by   hexose  transport  (HXT)  and  hexokinase  (HK)   l  Minor  regime:  HXT,  HK  and  alcohol  dehydrogenase   (ADH)   l  Biologically  inaccessible  regime:  [GLCi]  ≈  300mM     under  phosphofructokinase  (PFK)  control   Pritchard  and  Kell.  Schemes  of  flux  control  in  a  model  of  Saccharomyces  cerevisiae   glycolysis.  Eur  J  Biochem  (2002)  vol.  269  (16)  pp.  3894-­‐904  
  • Metabolic  Control  Analysis  (MCA)   l Yeast  glycolysis   l HXT  dominates  pathway  control   l External  [hexose]  is  a  signal,  as  HXT   is  sensi<ve  to  it.     Pritchard  and  Kell.  Schemes  of  flux  control  in  a  model  of  Saccharomyces  cerevisiae   glycolysis.  Eur  J  Biochem  (2002)  vol.  269  (16)  pp.  3894-­‐904  
  • Distributed  Control   l MCA  implies  distributed  control  of  networks   l Network  topology  also  implies  distributed  control    (minimal  interven<on  sets:  MIS)   l What  does  this  imply  for  host-­‐pathogen   interac<ons?   l  Several  points  in  network  are  influen<al   „ Can  be  predicted  with  sufficient  informa<on   about  system   l  A  pathway/network  element  may  be  under     distributed  control   „ May  need  to  hit  several  parts  of  the   network  to  produce  change   „ Single  effectors  unlikely  to  be  sufficient  
  • Distributed  Control   l MCA  implies  distributed  control  of  networks   l Network  topology  also  implies  distributed  control    (minimal  interven<on  sets:  MIS)   l What  does  this  imply  for  host-­‐pathogen   interac<ons?   l  Several  points  in  network  are  influen<al   „ Can  be  predicted  with  sufficient  informa<on   about  system   l  A  pathway/network  element  may  be  under     distributed  control   „ May  need  to  hit  several  parts  of  the   network  to  produce  change   „ Single  effectors  unlikely  to  be  sufficient  
  • Distributed  Control   l MCA  implies  distributed  control  of  networks   l Network  topology  also  implies  distributed  control    (minimal  interven<on  sets:  MIS)   l What  does  this  imply  for  host-­‐pathogen   interac<ons?   l  Several  points  in  network  are  influen<al   „ Can  be  predicted  with  sufficient  informa<on   about  system   l  A  pathway/network  element  may  be  under     distributed  control   „ May  need  to  hit  several  parts  of  the   network  to  produce  change   „ Single  effectors  unlikely  to  be  sufficient  
  • Distributed  Control   l MCA  implies  distributed  control  of  networks   l Network  topology  also  implies  distributed  control   l What  does  this  imply  for  host-­‐pathogen   interac<ons?   l  Several  points  in  network  are  influen<al   l  A  pathway/network  element  may  be  under     distributed  control   „ Pathogens  may  require  ‘sets’  of  effectors   „ Implies  ‘Redundant  Effector  Groups’  and     func2onal  redundancy?   Kvitko  et  al.  Dele<ons  in  the  repertoire  of  Pseudomonas  syringae  pv.  tomato  DC3000  type  III  secre<on  effector  genes  reveal   func<onal  overlap  among  effectors.  PLoS  Pathog  (2009)  vol.  5  (4)  pp.  E1000388  doi:10.1371/journal.ppat.1000388  
  • Distributed  Control   l MCA  implies  distributed  control  of  networks   l Network  topology  also  implies  distributed  control   l What  does  this  imply  for  host-­‐pathogen   interac<ons?   l  Context-­‐dependence  of  effector  func<on:   „ H.arabidopsidis  ATR13  suppresses  callose  deposi<on   „ P.  syringae  HopM1  suppresses  callose  deposi<on   „ ATR13  complements  callose  deposi<on,  but  does  not  fully  restore   virulence  in  HopM1  mutant  (EDV)   K.H.  Sohn,  R.  Lei,  A.  Nemri,  J.D.G.  Jones,  The  downy  mildew  effector  proteins  ATR1  and  ATR13  promote  disease   suscep<bility  in  Arabidopsis  thaliana,  Plant  Cell  19  (2007)  4077–4090.  
  • Distributed  Control   l We  can  consider  ‘system’  as  defining  a  landscape,   permi~ng  types  of  control   l Autocra<c  control:   l  Flat  landscape   l  Can  move  any  network  element  to  any  ‘state’   l Democra<c  control:   l  Rugged  landscape  (constrained  by  rest  of  network)   l  Network  elements  restricted  to  ‘valleys’  in  the   landscape   Bar-­‐Yam  et  al.  Systems  biology.  A`ractors  and  democra<c  dynamics.  Science  (2009)   vol.  323  (5917)  pp.  1016-­‐7  doi:10.1126/science.1163225  
  • Distributed  Control   l We  can  consider  ‘system’  as  defining  a  landscape,   permi~ng  types  of  control   l Autocra<c  control:   l  Flat  landscape   l  Can  move  any  network  element  to  any  ‘state’   l Democra<c  control:   l  Rugged  landscape  (constrained  by  rest  of  network)   l  Network  elements  restricted  to  ‘valleys’  in  the   landscape   Bar-­‐Yam  et  al.  Systems  biology.  A`ractors  and  democra<c  dynamics.  Science  (2009)   vol.  323  (5917)  pp.  1016-­‐7  doi:10.1126/science.1163225  
  • Distributed  Control   l We  can  consider  ‘system’  as  defining  a  landscape,   permi~ng  types  of  control   l Autocra<c  control:   l  Flat  landscape   l  Can  move  any  network  element  to  any  ‘state’   l Democra<c  control:   l  Rugged  landscape  (constrained  by  rest  of  network)   l  Network  elements  restricted  to  ‘valleys’  in  the   landscape   Bar-­‐Yam  et  al.  Systems  biology.  A`ractors  and  democra<c  dynamics.  Science  (2009)   vol.  323  (5917)  pp.  1016-­‐7  doi:10.1126/science.1163225  
  • Distributed  Control   l We  can  consider  ‘system’  as  defining  a  landscape,   permi~ng  types  of  control   l Autocra<c  control:   l  Flat  landscape   l  Can  move  any  network  element  to  any  ‘state’   l Democra<c  control:   l  Rugged  landscape  (constrained  by  rest  of  network)   l  Network  elements  restricted  to  ‘valleys’  in  the   landscape   l Pathogens  introduce  new  elements  that  change   the  landscape:  effectors   Bar-­‐Yam  et  al.  Systems  biology.  A`ractors  and  democra<c  dynamics.  Science  (2009)   vol.  323  (5917)  pp.  1016-­‐7  doi:10.1126/science.1163225  
  • A  state-­‐based  model  of  interac2on   l Prevailing  model:  zig-­‐zag(-­‐zig…)   Hein  et  al.  The  zig-­‐zag-­‐zig  in  oomycete-­‐plant  interac<ons.  Mol  Plant  Pathol  (2009)  vol.  10  (4)  pp.  547-­‐62  doi:10.1111/j. 1364-­‐3703.2009.00547.x   Jones  and  Dangl.  The  plant  immune  system.  Nature  (2006)  vol.  444  (7117)  pp.  323-­‐9  doi:10.1038/nature05286  
  • A  state-­‐based  model  of  interac2on   l Prevailing  model:  zig-­‐zag(-­‐zig…)   l Has  some  problems:   l  scope  (only  host  immune  system,  not  rest   of  interac<on  with  pathogen)   l  ordering  of  events  (are  PTI/ETI  etc.  dis<nct   and  well-­‐ordered?)   l  <mescale  (evolu<onary,  or  during  interac<on?)   l  size  scale  (organism  or  cell  level)   l  Quan<ta<ve  or  qualita<ve  (what  is  the  ‘amplitude’  of  defence?)  
  • A  state-­‐based  model  of  interac2on   l Prevailing  model:  zig-­‐zag(-­‐zig…)   l Has  some  problems:   l  scope  (only  host  immune  system,  not  rest   of  interac<on  with  pathogen)   l  ordering  of  events  (are  PTI/ETI  etc.  dis<nct   and  well-­‐ordered?)   l  <mescale  (evolu<onary,  or  during  interac<on?)   l  size  scale  (organism  or  cell  level)   l  Quan<ta<ve  or  qualita<ve  (what  is  the  ‘amplitude’  of  defence?)  
  • A  state-­‐based  model  of  interac2on   l Prevailing  model:  zig-­‐zag(-­‐zig…)   l Has  some  problems:   l  scope  (only  host  immune  system,  not  rest   of  interac<on  with  pathogen)   l  ordering  of  events  (are  PTI/ETI  etc.  dis<nct   and  well-­‐ordered?)   l  <mescale  (evolu<onary,  or  during  interac<on?)   l  size  scale  (organism  or  cell  level)   l  Quan<ta<ve  or  qualita<ve  (what  is  the  ‘amplitude’  of  defence?)  
  • A  state-­‐based  model  of  interac2on   l Prevailing  model:  zig-­‐zag(-­‐zig…)   l Has  some  problems:   l  scope  (only  host  immune  system,  not  rest   of  interac<on  with  pathogen)   l  ordering  of  events  (are  PTI/ETI  etc.  dis<nct   and  well-­‐ordered?)   l  <mescale  (evolu<onary,  or  during  interac<on?)   l  size  scale  (organism  or  cell  level)   l  Quan<ta<ve  or  qualita<ve  (what  is  the  ‘amplitude’  of  defence?)  
  • A  state-­‐based  model  of  interac2on   l Prevailing  model:  zig-­‐zag(-­‐zig…)   l Has  some  problems:   l  scope  (only  host  immune  system,  not  rest   of  interac<on  with  pathogen)   l  ordering  of  events  (are  PTI/ETI  etc.  dis<nct   and  well-­‐ordered?)   l  <mescale  (evolu<onary,  or  during  interac<on?)   l  size  scale  (organism  or  cell  level)   l  Quan<ta<ve  or  qualita<ve  (what  is  the  ‘amplitude’  of  defence?)  
  • A  state-­‐based  model  of  interac2on   l Prevailing  model:  zig-­‐zag(-­‐zig…)   l Has  some  problems:   l  scope  (only  host  immune  system,  not  rest   of  interac<on  with  pathogen)   l  ordering  of  events  (are  PTI/ETI  etc.  dis<nct   and  well-­‐ordered?)   l  <mescale  (evolu<onary,  or  during  interac<on?)   l  size  scale  (organism  or  cell  level)   l  Quan<ta<ve  or  qualita<ve  (what  is  the  ‘amplitude’  of  defence?)   l Is  there  a  more  general  framework  for  host-­‐pathogen  interac2ons?   Pritchard  L,  Birch  P  (2011)  A  systems  biology  perspec<ve  on  plant-­‐microbe  interac<ons:  Biochemical  and   structural  targets  of  pathogen  effectors.  Plant  Science  180:  584–603.  doi:10.1016/j.plantsci.2010.12.008.    
  • A  state-­‐based  model  of  interac2on   l Biological  cells  can  be  represented  as  networks   l Each  element  in  the  network  can  be  quan<fied:   l  enzyme  concentra<on  (or  expression  level)   l  metabolite  concentra<on   l  phosphoryla<on/ubiqui<na<on/charge  states  as  dis<nct   en<<es   l  etc.   l We  represent  lists  of  values  as  vectors   [v1, v2, v3, . . . , vk]
  • A  state-­‐based  model  of  interac2on   l Biological  cells  can  be  represented  as  networks   l Each  element  in  the  network  can  be  quan<fied:   l  enzyme  concentra<on  (or  expression  level)   l  metabolite  concentra<on   l  phosphoryla<on/ubiqui<na<on/charge  states  as  dis<nct   en<<es   l  etc.   l We  represent  ordered  lists  of  values  as  vectors   [v1, v2, v3, . . . , vk]
  • A  state-­‐based  model  of  interac2on   l Biological  cells  can  be  represented  as  networks   l Each  element  in  the  network  can  be  quan<fied:   l  enzyme  concentra<on  (or  expression  level)   l  metabolite  concentra<on   l  phosphoryla<on/ubiqui<na<on/charge  states  as  dis<nct   en<<es   l  etc.   l We  represent  ordered  lists  of  values  as  vectors   [v1, v2, v3, . . . , vk]
  • A  state-­‐based  model  of  interac2on   l Vectors  are  co-­‐ordinates  in  space   l  vectors  of  length  two:  points  on  a  surface  (2D  space)   l  vectors  of  length  three:  points  in  3D  space   l  vectors  of  length  k:  points  in  k-­‐dimensional  space   l Points  that  are  close  together  are  ‘similar’  
  • A  state-­‐based  model  of  interac2on   l Vectors  are  co-­‐ordinates  in  space   l  vectors  of  length  two:  points  on  a  surface  (2D  space)   l  vectors  of  length  three:  points  in  3D  space   l  vectors  of  length  k:  points  in  k-­‐dimensional  space   l Points  that  are  close  together  are  ‘similar’  
  • A  state-­‐based  model  of  interac2on   l Let  our  vector  represent  the  measured  state  of  the  cell   (e.g.  host-­‐pathogen)  system   l  enzyme/metabolite  concentra<ons,  etc.   l Each  point  in  k-­‐space  represents  a  different  state  of  the   system   l  similar  states  are  close  together  in  k-­‐space   [v1, v2, v3, . . . , vk]
  • A  state-­‐based  model  of  interac2on   l Let  our  vector  represent  the  measured  state  of  the  cell   (e.g.  host-­‐pathogen)  system   l  enzyme/metabolite  concentra<ons,  etc.   l Each  point  in  k-­‐space  represents  a  different  state  of  the   system   l  similar  states  are  close  together  in  k-­‐space   [v1, v2, v3, . . . , vk]
  • A  state-­‐based  model  of  interac2on   l States  that  lead  to  similar  phenotypes  can  be  grouped  in   phases:   l  regions  of  space  where  cell   state  corresponds  to  named   behaviour   l Temporal  evolu<on  of  a  cell  can   be  viewed  as  a  transi<on     through  states   v1   v2   apoptosis   ROS  produc<on   seed   leaf   root   HR  
  • A  state-­‐based  model  of  interac2on   l States  that  lead  to  similar  phenotypes  can  be  grouped  in   phases:   l  regions  of  space  where  cell   state  corresponds  to  named   behaviour   l Temporal  evolu<on  of  a  cell  can   be  viewed  as  a  transi<on     through  states   v1   v2   apoptosis   ROS  produc<on   seed   leaf   root   HR  
  • A  state-­‐based  model  of  interac2on   l Complex  systems  can  behave  in  complex  ways   l A  common  feature  of  complex  systems  is  aJractors   l  A`ractors  are  ‘endpoints’:  states,  or  sets   of  states,  to  which  the  system  is   ‘a`racted’   l  Analogous  to  stable  equilibria:     when  the  system  is  perturbed,     it  returns  to  its  a`ractor.   l  Do  cell  phenotypes   correspond  to  a`ractors?  
  • A  state-­‐based  model  of  interac2on   l Complex  systems  can  behave  in  complex  ways   l A  common  feature  of  complex  systems  is  aJractors   l  A`ractors  are  ‘endpoints’:  states,  or  sets   of  states,  to  which  the  system  is   ‘a`racted’   l  Analogous  to  stable  equilibria:     when  the  system  is  perturbed,     it  returns  to  its  a`ractor.   l  Do  cell  phenotypes   correspond  to  a`ractors?  
  • A  state-­‐based  model  of  interac2on   l Complex  systems  can  behave  in  complex  ways   l A  common  feature  of  complex  systems  is  aJractors   l  A`ractors  are  ‘endpoints’:  states,  or  sets   of  states,  to  which  the  system  is   ‘a`racted’   l  Analogous  to  stable  equilibria:     when  the  system  is  perturbed,     it  returns  to  its  a`ractor.   l  Do  cell  phenotypes   correspond  to  a`ractors?  
  • A  state-­‐based  model  of  interac2on   l Complex  systems  can  behave  in  complex  ways   l A  common  feature  of  complex  systems  is  aJractors   l  A`ractors  are  ‘endpoints’:  states,  or  sets   of  states,  to  which  the  system  is   ‘a`racted’   l  Analogous  to  stable  equilibria:     when  the  system  is  perturbed,     it  returns  to  its  a`ractor.   l  Do  cell  phenotypes   correspond  to  a`ractors?   apoptosis   ROS  produc<on   seed   leaf   root   HR  
  • A  state-­‐based  model  of  interac2on   l A`ractors  are  associated  with   the  regions  of  space  that  lead   to  them:  ‘basins’   l A`ractors  can  be:   l  Single  points   l  Cycles   l  Complex  ‘regions’  
  • A  state-­‐based  model  of  interac2on   l A`ractors  are  associated  with   the  regions  of  space  that  lead   to  them:  ‘basins’   l A`ractors  can  be:   l  Single  points   l  Cycles   l  Complex  ‘regions’  
  • A  state-­‐based  model  of  interac2on   l Interac<on  of  a  pathogen  with  the  host   can  push  the  system  from  one  basin  of   aJrac/on  to  another   l There  may  be  mul<ple  routes  between   basins  of  a`rac<on,  depending  on  the   direc<on  or  <ming  of  perturba<on   l  There  may  be  more  than  one  way  to   provoke  a  specific  outcome  from  the   host  (or  from  the  pathogen)  
  • A  state-­‐based  model  of  interac2on   l Interac<on  of  a  pathogen  with  the  host   can  push  the  system  from  one  basin  of   aJrac/on  to  another   l There  may  be  mul<ple  routes  between   basins  of  a`rac<on,  depending  on  the   direc<on  or  <ming  of  perturba<on   l  There  may  be  more  than  one  way  to   provoke  a  specific  outcome  from  the   host  (or  from  the  pathogen)  
  • A  state-­‐based  model  of  interac2on   l Effectors  may  divert  the  expected  WT  system  trajectory:   l ‘Pushing’  the  host  cell  state     towards  a  different     aJractor/state   l ‘State’  may  be  a  developmental   checkpoint   l Diversion  of  the  trajectory  may     also  be  beneficial  to  the  host   l The  pathogen  may  detect  the  host   state  and  respond  accordingly  (e.g.  <ssue-­‐specific  effector  produc<on   in  Us/lago  maydis;  stage-­‐  and  <ssue-­‐specific  oomycete  effectors)   v1   v2   nutrient     produc<on   PTI   seed   Epidermal  cell   root   HR  
  • A  state-­‐based  model  of  interac2on   l The  Jones-­‐Dangl  Zig-­‐Zag(-­‐Zig)  model  is   encapsulated  within  a  state-­‐based  model   PTI   No   challenge   ETI   ETS  
  • A  state-­‐based  model  of  interac2on   l The  Jones-­‐Dangl  Zig-­‐Zag(-­‐Zig)  model  is   encapsulated  within  a  state-­‐based  model   PTI   No   challenge   ETI   ETS  
  • A  state-­‐based  model  of  interac2on   l The  Jones-­‐Dangl  Zig-­‐Zag(-­‐Zig)  model  is   encapsulated  within  a  state-­‐based  model   PTI   No   challenge   ETI   ETS  
  • A  state-­‐based  model  of  interac2on   l The  Jones-­‐Dangl  Zig-­‐Zag(-­‐Zig)  model  is   encapsulated  within  a  state-­‐based  model   PTI   No   challenge   ETI   ETS  
  • A  state-­‐based  model  of  interac2on   l The  Jones-­‐Dangl  Zig-­‐Zag(-­‐Zig)  model  is   encapsulated  within  a  state-­‐based  model   PTI   No   challenge   ETI   ETS  
  • A  state-­‐based  model  of  interac2on   l The  Jones-­‐Dangl  Zig-­‐Zag(-­‐Zig)  model  is   encapsulated  within  a  state-­‐based  model   PTI   No   challenge   ETI   ETS  
  • A  state-­‐based  model  of  interac2on   l The  state-­‐based  model  has  advantages:   l  Scope:  can  include  host  and  pathogen,   and  extend  beyond  host  immunity   l  Ordering:  explicit  ordering  of  events   represented  by  paths  in  the  model   (determined  by  model)   l  Timescale:  explicit  (determined  by   model)   l  Size  scale:  can  include  mul<cellular   systems   l  Quan2ta2ve  or  qualita2ve:  explicit   (dependent  on  model)     PTI   No   challenge   ETI   ETS  
  • A  state-­‐based  model  of  interac2on   l The  state-­‐based  model  has  advantages:   l  Scope:  can  include  host  and  pathogen,   and  extend  beyond  host  immunity   l  Ordering:  explicit  ordering  of  events   represented  by  paths  in  the  model   (determined  by  model)   l  Timescale:  explicit  (determined  by   model)   l  Size  scale:  can  include  mul<cellular   systems   l  Quan2ta2ve  or  qualita2ve:  explicit   (dependent  on  model)     PTI   No   challenge   ETI   ETS  
  • A  state-­‐based  model  of  interac2on   l The  state-­‐based  model  has  advantages:   l  Scope:  can  include  host  and  pathogen,   and  extend  beyond  host  immunity   l  Ordering:  explicit  ordering  of  events   represented  by  paths  in  the  model   (determined  by  model)   l  Timescale:  explicit  (determined  by   model)   l  Size  scale:  can  include  mul<cellular   systems   l  Quan2ta2ve  or  qualita2ve:  explicit   (dependent  on  model)     PTI   No   challenge   ETI   ETS  
  • A  state-­‐based  model  of  interac2on   l The  state-­‐based  model  has  advantages:   l  Scope:  can  include  host  and  pathogen,   and  extend  beyond  host  immunity   l  Ordering:  explicit  ordering  of  events   represented  by  paths  in  the  model   (determined  by  model)   l  Timescale:  explicit  (determined  by   model)   l  Size  scale:  can  include  mul<cellular   systems   l  Quan2ta2ve  or  qualita2ve:  explicit   (dependent  on  model)     PTI   No   challenge   ETI   ETS  
  • A  state-­‐based  model  of  interac2on   l The  state-­‐based  model  has  advantages:   l  Scope:  can  include  host  and  pathogen,   and  extend  beyond  host  immunity   l  Ordering:  explicit  ordering  of  events   represented  by  paths  in  the  model   (determined  by  model)   l  Timescale:  explicit  (determined  by   model)   l  Size  scale:  can  include  mul<cellular   systems   l  Quan2ta2ve  or  qualita2ve:  explicit   (dependent  on  model)     PTI   No   challenge   ETI   ETS  
  • Summary   l Biological  systems  have  natural  network  representa<ons   l  But  representa<on  must  be  reasonable  and  suit  the  ques<on  being  asked   l Interac<on  of  host  and  pathogen  makes  a  new  single  network  from  two   ini<al  networks   l Network  topology  affects   l  Network  behaviour   l  Suscep<bility  to  a`ack  (hubs,  bridges)   l Network  dynamics  affect   l  Network  behaviour   l  Suscep<bility  to  a`ack  (distributed  control)   l A  state-­‐based  framework  may  be  useful  for  understanding  host-­‐ pathogen  interac<ons  
  • Acknowledgements   l Systems  Biology  at  Aberystwyth/Manchester   l  Doug  Kell,  David  Broadhurst,  Pedro  Mendes,  Roy  Goodacre,  Andy   Woodward,  Simon  Garre`,     l Computa<onal  biology  at  JHI   l  Peter  Cock   l Phytophthora  research  at  JHI   l  Paul  Birch,  Steve  Whisson,  Miles  Armstrong   l Bacteriology  research  at  JHI   l  Ian  Toth,  Sonia  Humphris,  Nicola  Holden   l Many,  many  discussions  with  colleagues  
  • Danger  Theory   l Proposed  by  computer  scien<sts  in  machine  learning:  avoids   detec<on  ‘bloat’  of  one  ‘recogni<on  gene’  per  threat.   l Popular  in  (animal)  immunology;  Analogous  to  Guard  Hypothesis  and   Dense  Overlapping  Regions  (DORs)   l Integra<on  of  mul<ple  signals  and  contextual  cues   Aickelin  et  al.  Danger  theory:  The  link  between  AIS  and  IDS?.  Lect  Notes  Comput  Sc  (2003)  vol.  2787  pp.  147-­‐155  
  • Danger  Theory   l Some  signals  ‘cri<cal’  and  require  immediate  response  (e.g.     avirulence  gene  products?)   l Other  signals  contextual  –  require  ‘processing’  (e.g.  MAMPs)   Boller  and  Felix.  A  renaissance  of  elicitors:  percep<on  of  microbe-­‐associated  molecular  pa`erns  and  danger  signals   by  pa`ern-­‐recogni<on  receptors.  Annu.  Rev.  Plant.  Biol.  (2009)  vol.  60  pp.  379-­‐406  doi:10.1146/annurev.arplant. 57.032905.105346  
  • Danger  Theory   l Context  dependence  and  non-­‐linear  signal  may  lead  to  problems   of  interpreta<on  in  experiments.   l Danger  R  when  signal  ≥  5   l  a+b+c+d  =  6  ⇒  R   l  a+b+c  =  4  ⇒  no  R   l  a+b+d  =  4  ⇒  no  R   l  a+c+d  =  5  ⇒  R   l  b+c+d  =  5  ⇒  R   l  a+b  =  3  ⇒  no  R   l {c  and  d}  required  for  R?  
  • Danger  Theory   l Context  dependence  and  non-­‐linear  signal  may  lead  to  problems   of  interpreta<on  in  experiments.   l Danger  R  when  signal  ≥  5   l  a+b+c+d  =  6  ⇒  R   l  a+b+c  =  4  ⇒  no  R   l  a+b+d  =  4  ⇒  no  R   l  a+c+d  =  5  ⇒  R   l  b+c+d  =  5  ⇒  R   l  a+b  =  3  ⇒  no  R   l {c  and  d}  required  for  R?  
  • Danger  Theory   l Context  dependence  and  non-­‐linear  signal  may  lead  to  problems   of  interpreta<on  in  experiments.   l Danger  R  when  signal  ≥  5   l  a+b+c+d  =  6  ⇒  R   l  a+b+c  =  4  ⇒  no  R   l  a+b+d  =  4  ⇒  no  R   l  a+c+d  =  5  ⇒  R   l  b+c+d  =  5  ⇒  R   l  a+b  =  3  ⇒  no  R   l {c  and  d}  required  for  R?  
  • Danger  Theory   l Context  dependence  and  non-­‐linear  signal  may  lead  to  problems   of  interpreta<on  in  experiments.   l Danger  R  when  signal  ≥  5   l  a+b+c+d  =  6  ⇒  R   l  a+b+c  =  4  ⇒  no  R   l  a+b+d  =  4  ⇒  no  R   l  a+c+d  =  5  ⇒  R   l  b+c+d  =  5  ⇒  R   l  a+b  =  3  ⇒  no  R   l {c  and  d}  required  for  R?   l No:  a+b+c+e,  a+b+d+e  ⇒  R  
  • Danger  Theory   l Context  dependence  and  non-­‐linear  signal  may  lead  to  problems   of  interpreta<on  in  experiments.   l Danger  R  when  signal  ≥  5   l  All  single  knockouts  ⇒  R   ∴  all  receptors  redundant?   l  a+c+e  =  4  ⇒  no  R   a+b+c+e  =  5  ⇒  R   ∴  {a,b}  non-­‐redundant?   l  a+c+d+e  =  5  ⇒  R   b+c+d+e  =  5  ⇒  R   ∴  {a,b}  redundant?   l ‘unequal  gene2c  redundancy’