Capturing the Immune System: From the wet-­lab to the robot, building better quality immune-­inspired engineering solutions - Mark Read

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Capturing the Immune System: From the wet-­lab to the robot, building better quality immune-­inspired engineering solutions - Mark Read

  1. 1. Capturing  the  Immune  System:   From  the  wet-­‐lab  to  the  robot,   building  be;er  quality  immune-­‐inspired   engineering  solu=ons   Dr.  Mark  Read   Department  of  Electronics,     The  University  of  York  
  2. 2.  Talk  Overview   •  Ar=ficial  immune  systems  are  engineered  systems  that   take  inspira=on  from  the  immune  systems’  organiza=on   and/or  func=on   Bio-­‐inspired  algorithms/systems   •  Immune  System  Func=on   –  Why  seek  immune  inspira=on?   •  Understanding  and  Capturing  Immune  System  Principles   –  How  to  replicate  that  which  you  do  not  understand?   •  Adop=ng  Immune  Inspira=on     –  Robots  with  Immune  Systems?  
  3. 3. The  Immune  System   A  rich  source  of  inspira=on  
  4. 4. The  immune  system  (IS)   •  Large  collec=on  of  cells,  molecules  and  organs   responsible  for  maintaining  health  of  the  host  
  5. 5. The  immune  system  (IS)   •  What  it  is  responsible  for:   –  Iden=fying  &  clearing  pathogens   –  Clearing  tumors   –  Clearing  dead  cells  and  debris   –  Growing  and  shaping  =ssues   –  Maintaining  general  health  of  the  host   •  IS  must:   –  Differen=ate  harmful/dangerous  and  healthy  contexts   –  Correlate  harm/health  with  causes   –  Not  a5ack  the  host   [Cohen  2004]  
  6. 6. IS  Complexity   •  Insanely  complex.     •  Data  needs  to  be  integrated  and  understood   •  IS  is  a  complex  system,  evolved  through  ages,  adop=ng   short  term  gains,  with  no  organizing  principles.     [Kindt  07]  
  7. 7. IS  func=on   •  Composed  of  a  great  many  cell  types   –  T,  B,  Macrophage,  DC,  NK,  NK-­‐T   –  More,  and  subsets  of  all  of  these     •  Roughly  composed  of  two  halves   –  Innate,  evolu=onarily  conserved   –  Adap=ve,  bespoke  reac=on  to  infec=on   •  Complement  system,  not  cellular   •  ‘Communica=on  channels’  also  complex   –  Cytokines/receptors  with  overlapping  func=on  
  8. 8. Innate  immunity   •  Fast  response  to  known  pathogens   •  Similar  from  one  individual  to  the  next   •  Skin   •  Phagocy=c  cells   •  DCs  and  Macrophages  secrete  soluble  factors   –  Complement  system   –  ROS,  NO,  other  harmful  chemicals   •  S=mulated  by  contact  with  par=cular  pa;erns   –  E.g.  bacteria,  evolu=onarily  conserved  structures   •  Differen=ates  harmful/not   •  Interacts  with  adap=ve  system  
  9. 9. Adap=ve  immunity   •  Slower  response  to   pathogens   –  An=bodies,  T  cells,  B  cells   •  Specific,  bespoke  for   par=cular  pathogen   •  Each  individual’s  adap=ve  IS   is  unique   •  Driven  by  the  innate   response   •  Specificity  improves  during   prolifera=on  (for  B  cells)  
  10. 10. Walk-­‐through  immune  response  
  11. 11. Primary  and  secondary  responses   •  IS  has  a  “memory”   •  Generally  get  sick  less  as  you  get  older  
  12. 12. Autoimmunity  &  Regula=on   •  Fast-­‐evolving  pathogens?   •  Genera=on  of  new  receptors   –  DNA  recombina=on     •  Thymus  &  bone  marrow  in  nega=ve  selec=on   •  Not  complete!     –  Auto-­‐immune  cells  reside  in  all  of  us     –  Ordinarily,  they  are  suppressed  
  13. 13. Autoimmunity  as  a  malfunc=on  of  regula=on   •  Peripheral  tolerance   •  ‘By-­‐stander’  Treg-­‐regula=on   •  Specific  regula=on  
  14. 14. Immune  system  proper=es   •  Interest  in  the  immune  system?   –  Adapta=on   –  Pa;ern  matching   –  Decentraliza=on   –  Self-­‐organizing   –  Self-­‐regula=ng   –  Op=miza=on   –  Memory   –  Homeostasis  
  15. 15. Understanding  and  Capturing   Immune  System  Principles   How  to  replicate  that  which  you  do  not  understand?  
  16. 16. So  you  want  to  create  an  AIS?   •  Typical  instan=a=ons   –  Anomaly  detec=on     •  Nega=ve  selec=on   •  Danger  theory   –  Op=miza=on   •  Clonal  selec=on   –  Clustering  &  classifica=on   •  Modified  clonal  selec=on   •  Not  a  very  diverse  range  of  inspira=on   •  Not  really  immune  “systems”   –  Integra=on  of  IS  principles  could  lead  to  more   sophis=cated  applica=ons?   [Hart  2008]  
  17. 17. Capturing  the  IS   •  IS  a  much  richer  source  of  inspira=on  than  has  been   typically  adopted   •  But  its  hard   •  The  typical  approach   –  Iden=fy  some  interes=ng  aspect  of  immunology   –  Read  a  textbook   –  `pretend’  that  you  understand  it  (trained  biologists  don’t   understand  a  lot  of  this)   –  Get  hacking!     [Stepney  2005]  
  18. 18. Capturing  the  IS   •  Are  there  be;er  ways  to  capture  IS  proper=es?   •  What  is  the  principle  challenge  here?   [Tieri  2012]  
  19. 19. Biological  complexity   •  We  don’t  really  understand  the  biological  systems   we  are  trying  to  capture.     •  Major  debates  in  immunology  about  fundamental   immune  func=on   –  CD4Th  ‘help’,  tolerance   •  Not  necessarily  a  problem   –  As  long  as  there  is  a  coherent  model,  we  can  run  with  it.   –  …  if  we  understand  it   [Andrews  2005]  
  20. 20. Conceptual  Framework   [Stepney  2005,  Andrews  08]   •  What  is  the  problem  domain?   •  How  do  you  select  appropriate  biological  inspira=on?    
  21. 21. Modelling  to  understand   •  Models  and  simula=ons  demonstrate  whether  our   theories  explain  what  we  observe   –  (they  usually  don’t)   •  What  is  important,  what  is  not?   •  What  can  be  len  out?   •  Giving  back:  In  silico  experimenta=on   •  Turns  out  we  don’t  even  know  how  to  build  models   par=cularly  well…  
  22. 22. Modelling  to  understand   •  So  how  do  you  go  about  modelling  biological   phenomenon?   •  Typical  approach   –  Iden=fy  some  interes=ng  aspect  of  immunology   –  Read  a  textbook   –  `pretend’  that  you  understand  it  (trained  biologists  don’t   understand  a  lot  of  this)   –  Get  hacking!     •  Sound  familiar?  
  23. 23. CoSMoS  Process   [Andrews  2010,  Bown  2012]   •  A  principled  approach  to  inves=ga=ng  complex  system   phenomena   •  Emphasizes  domain  expert  engagement  and   documen=ng  assump=ons    
  24. 24. Domain:  Experimental  Autoimmune   Encephalomyeli=s  (EAE)   [Kumar  1996  (redrawn)]   •  Murine  autoimmune  disease,  model  for  MS   •  Spontaneous  recovery,  iden=fying  cells  responsible  
  25. 25. Domain:  Experimental  Autoimmune   Encephalomyeli=s   [Read  2011]  
  26. 26. What  do  we  want  to  know?   •  Inves=gate  role  of  CD8Treg  in  media=ng  recovery.     •  How  efficient  is  this  killing?  
  27. 27. Domain  Modelling  I   •  Itera=ve,  DM  engagement   •  UML   [Read  2011,  2009,  manuscript  in  prep]  
  28. 28. Domain  Modelling  II   •  Ac=vity  diagrams   •  Capture  how   cellular  events   hypothesized  to  a   par=cular  outcome   •  Decompose  disease   into  manageable   subsets.   [Read  2011,  2009,  manuscript  in  prep]  
  29. 29. Domain  Modelling  III   •  State  machine   diagrams  capture   lowest  level  en=ty   behaviours   •  Ques=ons  concerning   orthogonality   [Read  2011,  2009,  manuscript  in  prep]  
  30. 30. Plasorm  Modelling   •  State  machine  diagrams   translated  into  code   •  Emergent  phenomena   removed   •  Implementa=on  details  added  
  31. 31. Simula=on  Plasorm  
  32. 32. Results  model   •  Compare  simula=on  results  with  real-­‐world   observa=ons   •  Perform  in  silico  experiments  
  33. 33. Baseline  results   Control   Disable  regula=on   Real  mice  Simula=on    
  34. 34. Characterizing  regulatory  efficacy  I   •  How  efficient  is  this  killing?  
  35. 35. Characterizing  regulatory  efficacy  II   Regulatory   Efficacy   Death   (%)   Clinical  Episodes  (%)   1   2   3   100%   15.0   99.8   0   0   20%   16.0   99.8   0   0   5%   22.0   99.4   0.6   0   2%   26.6   85.0   12.4   2.6   0%   29.0   56.8   31.1   12.2   Th1  @  40  days  control  
  36. 36. We  have  a  model   •  Now  what?   •  Extract  organizing  principles  from  the  models   •  Sensi=vity  analysis   –  Iden=fies  key  components  and  pathways   •  Need  to  find  the  analogy  between  the  model,  and   the  applica=on  domain   •  For  EAE?   Don’t  have  a  par=cular  domain  in  mind    
  37. 37. Mapping  IS  concepts  to  a  domain   •  For  swarm-­‐repair   •  Granulomas   [Ismail  11]    
  38. 38. Granuloma  Forma=on  Algorithm  
  39. 39. Par=al  Failure   [Ismail  2011]  
  40. 40. Par=al  Failure  +  Granuloma   [Ismail  2011]  
  41. 41. AdopDng  Immune  InspiraDon   Robots  with  immune  systems?  
  42. 42. Characterizing  the  ‘AIS  Prac==oner’   •  Engineers  don’t  speak  Immunologist.   •  Intermediary  between  immunology  &  engineering     [Hart  2013]  
  43. 43. Swarm  Robo=cs   •  Swarm  intelligence  +  robo=cs   •  Complex  group  behaviours  emerge  from  simple   decentralized  individuals   •  Robustness,  flexibility,  scalability   –  Apparently  not  without  limits  though   •  Applica=ons,  e.g.,  search  and  rescue   [Bayindir  2007,     Bjerknes    2010]  
  44. 44. CoCoRo  –  the  domain   •  Can  IS-­‐inspira=on  be  used  to  provide  fault  tolerance   in  CoCoRo?  
  45. 45. CoCoRo  Immunity   •  Fault  tolerance   •  Immunity  operates  at  3  levels                  
  46. 46. Receptor  Density  Algorithm  -­‐  Inspira=on   [Owens  2010]  
  47. 47. Receptor  Density  Algorithm  
  48. 48. Single  sensor  anomaly  detec=on   Gyroscope  data  
  49. 49. Single  sensor  anomaly  detec=on  result   Gyroscope  Y  
  50. 50. Single  sensor  anomaly  detec=on  result   Gyroscope  X  
  51. 51. Mul=-­‐sensor  anomaly  detec=on  
  52. 52. Mul=-­‐sensor  anomaly  detec=on   •  Correla=ons  in  sensor  stream  data   –  And  what  the  OS  thinks  the  AUV  is  supposed  to  be  doing   •  Sensors  give  overlapping  perspec=ves  of  same   secenario   •  Spot  the  odd  one  out   –  Contextualize  anomalies   –  Sensor/actuator  anomaly?  
  53. 53. Failure  Mode  Effect  Analysis  (FMEA)   •  Offline  algorithm  analysis   •  Iden=fy  the  algorithmic/swarm-­‐level  impact  of  hardware/ subsystem  failures  in  an  AUV   •  Informs  algorithmic  design,  recovery  mechanisms  design   •  Performed  on  shoaling   –  And  relay  chain…  but  I  don’t  want  to  give  anything  away  :o)   SHOALING  VIDEO  HERE  
  54. 54. FMEA  results  on  shoaling   Blue  light  transmission  failure   Leads  to  the  most  effects     Most  common  effects  are  collisions   and  ge}ng  lost     Anchoring  is  disrup=ve,  but  not     the  most  prevalent  fault    
  55. 55. CoCoRo  Immunity   •  How  does  this  all  fit  together?   •  EAE  again   –  Grading  controller  ‘disease’?   –  Recovery  likely  to  be  disrup=ve   –  Strength  of  response  linked  to  state  of  disease                  
  56. 56. Summary   •  IS  very  rich  source  of  interes=ng  behaviours   –  Pa;ern  recogni=on,  anomaly  detec=on,  memory,   decentraliza=on,  self-­‐organizing,  self-­‐regula=ng   •  Capturing  it  is  difficult   –  It  is  not  yet  well  understood   –  Methodologies  for  reasoning  by  model/simula=on   –  Extrac=ng  key  principles/components   –  Try  to  lose  the  immunological  nomenclature   •  Swarm  immunity  can  be  more  than  one  algorithm   –  Systemic,  with  layers  feeding  into  one  another  
  57. 57. The  possibili=es  
  58. 58. References   •  PS  Andrews.  An  Inves=ga=on  of  a  Methodology  for  the  Development  of  Ar=ficial  Immune  Systems:  A  Case-­‐Study  in  Immune  Receptor   Degeneracy,  PhD  Thesis,  the  University  of  York,  2008.     •  PS  Andrews,  J  Timmis.  Inspira=on  for  the  Next  Genera=on  of  Ar=ficial  Immune  Systems.  LNCS  3627:126-­‐138,  2005.   •  PS  Andrews  et  al.  The  CoSMoS  Process  Version  0.1:  A  Process  for  the  Modelling  and  Simula=on  of  Complex  Systems.  Technical  Report   Number  YCS-­‐2010-­‐453.  Department  of  Computer  Science,  University  of  York,  2010.       •  L  Bayindir  and  E  Sahin.  A  review  of  studies  in  swarm  robo=cs.  Turkish  journal  of  electrical  engineering  and  computer  science,  15(2): 115-­‐147,  2007.   •  J  Bjerknes  and  A  Winfield.  On  fault-­‐tolerance  and  scalability  of  swarm  robo=c  systems.  DARS  2010,  Springer  Tracks  in  advanced  robo=cs   series  1:1-­‐12,  2010.   •  J  Bown  et  al.  Engineering  Simula=ons  for  Cancer  Systems  Biology.  Current  Drug  Targets  13(12):1560-­‐1574,  2012.     •  IR  Cohen.  Tending  Adam’s  Garden  :  Evolving  the  Cogni=ve  Immune  Self.  Elsevier  Academic  Press,  August  2004.   •  E  Hart,  J  Timmis.  Applica=on  areas  of  AIS:  The  past,  the  present  and  the  future.  Applied  Son  Compu=ng  (8):191-­‐201,  2008.   •  E  Hart  et  al.  On  the  role  of  the  AIS  Prac==oner.  Abstract  accepted  to  ICARIS  track  at  ECAL  2013.     •  AR  Ismail.  Immune-­‐inspired  self-­‐healing  swarm  robo=c  systems.  PhD  Thesis,  the  University  of  York,  2011.   •  TJ  Kindt  et  al.  Kuby  Immunology.  W.  H.  Freeman  and  Company,  6th  edi=on,  2007.   •  V  Kumar,  K  Stellrecht  and  E  Sercarz.  Inac=va=on  of  T  Cell  Receptor  Pep=de-­‐specific  CD4  Regulatory  T  Cells  Induces  Chronic   Experimental  Autoimmune  Encephalomyeli=s  (EAE).  Journal  of  Experimental  Medicine  (184):1609-­‐1617,  1996   •  N  Owens.  From  Biology  to  Algorithms.  PhD  Thesis,  The  University  of  York,  2010.   •  M  Read  et  al.  A  Domain  Model  of  Experimental  Autoimmune  Encephalomyeli=s.  2nd  Workshop  on  Complex  Systems  Modelling  and   Simula=on.pp:9-­‐44,  2009.   •  M  Read  et  al.  Techniques  for  Grounding  Agent-­‐Based  Simula=ons  in  the  Real  Domain:  A  Case  Study  in  Experimental  Autoimmune   Encephalomyeli=s.  MCMDS  18(1):67-­‐86,  2012.   •  M  Read.  Sta=s=cal  and  Modelling  Techniques  to  Build  Confidence  in  the  Inves=ga=on  of  Immunology  through  Agent-­‐Based  Simula=on.   PhD  Thesis,  the  University  of  York,  2011.     •  S  Stepney  et  al.  Conceptual  Frameworks  for  Ar=ficial  Immune  Systems.  Interna=onal  Journal  of  Unconven=onal  Compu=ng  1(3): 315-­‐338,  2005.     •  J  Timmis  et  al.  Immuno-­‐Engineering.  IFIP  World  Computer  Congress,  IEEE  Press  268:3-­‐17,  2008.   •  P  Tieri  et  al.  Char=ng  the  NK-­‐kB  Pathway  Interactome  Map.  Plos  One  7(3):  e32678,  2012.    

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