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Understanding Innovation in Biological, Social, Cultural and Technological Systems: A Developmental Evolu/on Perspective

Understanding Innovation in Biological, Social, Cultural and Technological Systems: A Developmental Evolu/on Perspective

Manfred D. Laubichler
Arizona State University
Marine Biological Laboratory
Santa Fe Ins/tute

Ee683edf7b765d56acd6f8ba903607f1?s=128

Insite Project

April 02, 2013
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Transcript

  1.    Understanding  Innova/on  in  Biological,   Social,  Cultural  and  Technological

      Systems:   A  Developmental  Evolu/on  Perspec/ve   Manfred  D.  Laubichler   Arizona  State  University   Marine  Biological  Laboratory   Santa  Fe  Ins/tute  
  2. Two  Theses     1.  Developmental  Evolu/on  provides  a  mechanis/c

      understanding  of  innova/on  in  biological  systems     2.  Innova/ons  in  complex  adap/ve  systems  (CAS)  are   governed  by  a  set  of  isomorphic  principles     Two  Case  studies     1.  Phenotypic  evolu/on  in  biological  systems     2.  Innova/on  in  knowledge  systems—the  history  of  science  
  3. Is  Developmental  Evolu0on  a  case  of  a     Scien0fic

     Revolu0on?—      And,  if  so,  what  kind  of  Scien0fic  Revolu0on  is  it?        (Kuhn  read  through  the  lenses  of  biology)   =>  A  revolu/on  that  has  been  in  the  making  for  a  long  /me     =>  Accumula/on  of  transforma/ve  changes  in  the    genome  of  evolu/onary  theory     =>  Visible  shiT  in  the  body-­‐plan/phenotype  of    evolu/onary  theory  emerging  over  the  last  15+years  
  4. The  standard  historical  narra0ve  of  Evolu0onary  Biology   Darwin  

    Mendel/Morgan  &   Popula0on  Gene0cs   Modern  Synthesis   Evo  Devo   Common  Descent,  Natural  Selec/on,  Gradualism,  Open  Ques)on  of  Inheritance     Rules  of  transmission  gene/cs,  Physical  Basis  of  Heredity,   Genes  as  abstrac/ons  (factors),  sta/s/cal  approaches,  Open   Ques/ons  related  to  effects  of  genes  (other  than  sta/s/cal)   Common  explanatory  framework:  (adap/ve)  dynamics  of  popula/ons  are  the  primary      explana/on  for  phenotypic  evolu/on,  developmental  mechanisms  are      secondary  (complexity  of  the  genotype-­‐phenotype  map)   Dynamics  of  Alleles  connected  to   Adapta/on  and  Specia/on;     Simple  Genotype-­‐Phenotype  Map   Gradualism   Complex  GT—PT  Map,  constraints,   conserva/on,  comparison   “to  complete  the  Modern  Synthesis”  
  5. An  alterna0ve  history  of  Developmental  Evolu0on   Darwin   Boveri,

     Cell  Biology  &   Entwicklungsmechanik   Kühn,  Goldschmidt  &   Developmental  Physiological   Gene0cs   Regulatory  Evolu0on,   GRNs  &  Synthe0c   Experimental  Evolu0on   Common  Descent,  Natural  Selec/on,  Gradualism,  Open  Ques/on  of  Inheritance,   Developmental  Considera)ons  about  the  Origin  of  Varia)on     Role  of  the  Nucleus  in  Development  and  Heredity,   Experimental  Approaches,  Specula/ve  Ideas  about  the   Hereditary  Material  as  a  Structured  System  governing   Development   Common  explanatory  framework:  Mechanis/c  Explana/on  of  Development  and  Evolu/on  as   primary;  Development  as  the  Origin  of  Phenotypic  Varia/on,  Adap/ve  Dynamics  as     secondary     Physiological  Gene  Ac/on,     Macroevolu/on,  Gene   Pathways  
  6. The  BriNen-­‐Davidson  Model  (1969)—      A  conceptual/logical  Framework  for

     Developmental  Evolu0on •  Logical  structure  of    “regula/on  of  gene  ac/vity”   •  Based  on  a  hierarchical  and  func/onal  structure  of  the  genome   •  Explicit  recogni/on  as  a  mechanism  of  phenotypic  evolu/on   •  Offered  a  construc/ve-­‐mechanis/c  alterna/ve  theory  of  phenotypic   evolu/on     Open  Ques/on:  Specific  Structure  of  the  Network            (-­‐>experimental  challenge)    
  7. Underlying  Assump/ons  in  Evolu/onary  Theory  about   Phenotypic  Evolu/on:  

         =>  “Muta/ons  will  get  you  there”        =>  Problem:  What  is  the  Effect  of  a  Muta/on    =>  Problem:  What  is  the  Structure  of  the  Genotype-­‐      Phenotype  Map     Part  of  the  long  quest  to  understand  the  origins  of   varia/on  and  the  pagerns  of  phenotypic  diversity  (think   body  plans)  
  8. Problem   Both  sides  in  the  current  debate  between  the

     primacy  of   regulatory  or  standard  adap/ve  evolu/on  have  ample  empirical   evidence     =>  This  is  a  debate  about  epistemology,  not  data  (but  data  help)  
  9. Measuring  Pleiotropy:  Mouse  Skeletal  Characters  

  10. Measuring  Pleiotropy:  S/ckleback  Skeletal  Characters  

  11. The  data  on  gene/c  pleiotropy   suggest   which,  together

     with   over  three  decades  of   molecular   developmental  biology,    lead  to  =>  
  12. Eric  Davidson’s  Concept  of  Gene  Regulatory  Networks  

  13. The  dynamic  four  dimensional  regulatory  genome   Tradi/onal  defini/on:  

     =>  Genome  is  oTen  equated  with  the  complete  DNA  sequence     However,    =>  Genome  is  the  en/rety  of  the  hereditary  informa/on  of  an   organism    =>  heredity  involves  a  whole  range  of  complex  regulatory   processes  and  mechanisms  (development)    =>  heredity  therefore  implies  the  unfolding  of  the  gene/c   informa/on  in  space  and  /me  during  development  and  evolu/on    (1)  the  genome  is  thus  a  spa/al-­‐temporal  sequence  of   regulatory  states    (2)  the  genome  anchors  all  other  regulatory  processes  that   affect  development  and  heredity  
  14. Analyzing  and  Expanding  Gene  Regulatory  Networks  

  15. Sub-­‐circuit  Repertoire  of  Developmental  GRNs  

  16. Logic  Reconstruc/on  of  a  Developmental  GRN  

  17. The  Developmental  Evolu/on  of  the  Superorganism  

  18. A  Hierarchical  Expansion  of  the  GRN  Framework   Developmental  Evolu/on

     in  Social  Insects:  Regulatory  Networks  from  Genes  to  Socie/es  
  19. More  than  a  Century  later  —  Boveri  realized   “to

     transform  one  organism  in  front  or  our  eyes  into  another”   Synthe'c  Experimental  Evolu'on     “to  mold  arbitrary  abnormali/es  into   true  experiments…”     •  Requires  both  detailed  knowledge  AND  a   clear  theore/cal  framework  of   developmental  evolu/on     •Transforms  research  on  phenotypic   evolu/on    =>  Compara/ve  GRN  research    =>  emphasis  on  the  mechanisms  of   (genomic)  regulatory  control    =>  Experimental  interven/on  (re-­‐ construc/ng  GRNs)   Erwin  and  Davidson,  2009  
  20. Novel  Computa/onal  Possibili/es  

  21. Peter  et  al.,  2012  

  22. Peter  et  al.,  2012  

  23. Peter  et  al.,  2012  

  24. Peter  et  al.,  2012  

  25. Future  Possibili'es   Common  explanatory  framework:  Mechanis/c  Explana/on  of  Development

     and  Evolu/on   is  primary;  Development  as  the  Origin  of  Phenotypic  Varia/on,  Adap/ve  Dynamics  as     secondary     The  combina)on  of  empirical  data—from  many  recent  empirical   studies—and  new  computa)onal  approaches  allows  us  to  fulfill  the   promise  of  developmental  evolu)on  as  a  mechanis)c  science  
  26. Further  development  of  computa/onal  GRN  models  for  mul/ple  systems  to:

         1.  Explore  the  future  evolu)onary  poten)al  of  a  given  genome  based  on  the    introduc/on  of  known  gain  of  func/on  elements      2.  Reconstruct  specific  evolu)onary  trajectories  (=>  compara/ve  analysis  of    GRNs  based  on  phylogene/c  hypotheses)        3.  Develop  predic)ons  of  evolu)onary  transi)ons  (for  experimental        verifica/on)      4.  Further  refine  the  hierarchical  expansion  of  the  GRN  perspec)ve  to  include    the  effects  of  post-­‐transcrip/onal  and  environmental/epigene/c  regulatory    systems     Future  Direc/ons   Synthe0c  in  silico  experimental  evolu0on  
  27. Part  Two   Innova/on  in  Knowledge  Systems  —  The  case

      of  the  History  of  Science  
  28. Computational History of Science!

  29. The  Idea  of  Culturomics   (Big  Data,  Computa/onal)   Current

     Phase  <=>  Single  Gene  Stage  
  30. The Need for Big and Open Data in the History

    of Science! Challenges! =>  The  Shortcomings  of  the  Biographical  Model   =>  The  Need  for  Transparency   =>  The  Need  for  Novel  Publica/on  Forms   Real! Problems! =>  The  Increasing  Orders  of  Magnitude  Gap  and  the  “Model   Scien/st”  Trap     Questions! =>  How  to  Detect  Novel/es  (Inventions  and  Innovations)?   =>  How  to  Detect  Diffusion/Dissemina/on?   =>  How  to  Detect  the  Effects  of  Epistemologies,  Technologies   and  the  Social  Structure  of  Science?    
  31. One  of  the  Main  Ques/ons  in  Evolu/onary  Theory   =>

    The Origin of Evolutionary Innovations!
  32. Experimental  Evolu/on  in  E.coli   =>  long  term  study  (50,000

     genera/ons)   =>  detailed  reconstruc/ons  of  phenotypic   innova/ons   =>  corresponding  understanding  of  underlying   gene/c  changes   =>  new  phenotypes  are  the  consequence  of   rearrangements  of  complex  genomic  networks  
  33. Isomorphisms between Complex Adaptive Information Systems! Genomes to Knowledge Systems!

    =>  Complex  Networks  and  Graphs   =>  Inven/on  and  Innova/on   =>  Hierarchical  Expansion  of  Causal  Networks,  including  Social   Networks   =>  Causal  Networks  Involving  Many  Different  Kinds  of  Elements   =>  Contextual  Meaning   =>  Developmental  Evolu/onary  Dynamics   The Common Theoretical Core also Enables the Use of Shared Methodologies!
  34. From Whence? And How?

  35. From Whence? And How?

  36. Conceptual  Pre-­‐Condi/ons   •  “Popula/on  thinking”   •  Selec/on  acts

     at  individual/genic  level   •  Evolu/on  can  occur  on  ecologically-­‐relevant  /me-­‐scales   •  Evolu/on  can  occur  on  small  geo-­‐spa/al  scales   1950   1940   1930   1920   1960   1970   1980   “Modern  Evolu/onary  Synthesis”   ✤  Modern  evolu/onary  synthesis   ✤  Group-­‐selec/on  controversy   ✤  ?   ✤  ?   Evolutionary Population Ecology
  37. 1950   1940   1930   1920   1960  

    1970   1980   “Modern  Evolu/onary  Synthesis”   Anthony David Bradshaw (1926 - 2008)! “...man  has  only  exercised    influence  on  the  vegeta/on  for  the  last   six thousand years, which gives little chance  for  the  bulk  of  the   vegeta/on  to  evolve  in  rela/on  to  these  effects.”   1948   “We  are  brought  up  to  think  that  the  /me  scale  of  evolu/on  is   millennia.  This  may  be  true  for  the  history  of  life  but  it is not true for the immediate process of evolution within species.”   1965   Experimental Taxonomy Genecology
  38. Why did Bradshaw change his mind about the temporal and

    spatial scales of adaptive evolution?!
  39. 1.  Early  decisions  about  methods  had  a  huge   impact

     on  his  ideas  about  scale.   2.  The  methods  that  he  chose  were  influenced   heavily  by  the  network of researchers  that  he  was   a  part  of.   4.  His  ideas  about  scale  were  stabilized  by  a  network of other concepts,  which  were   themselves  objects  of  debate.  (e.g.  “ecotype”)   3.  That  network  of  researchers  was  shaped   enormously  by  the  institutional context   (agricultural  development)  in  which  he  operated.   Some Clues:
  40. Why did Bradshaw change his mind about the temporal and

    spatial scales of adaptive evolution?! What about everybody else?!
  41. 1950   1940   1930   1920   1960  

    1970   1980   “Modern  Evolu/onary  Synthesis”   “Experimental  Taxonomy”  /  “Genecology”   “Popula/on  Biology”  /   “Evolu/onary  Ecology”   Changes  in  priori/es  and  paradigm  for  agricultural  development.   Debates  about  sampling  methods,  and  rela/onships  to  concepts  and   interpreta/on.   Lots  of  boundary-­‐work  surrounding  “genecology”  and   “experimental  taxonomy.”   Ins/tu/onal  disagreements  about  epistemology  (e.g.  Edinburgh  vs.   Birmingham)   ?! Evolu/onary  Popula/on  Ecology   From Whence? And How?
  42. Changing relationships between scientists, institutions, organisms, methods, and technologies.! Changing

    topology of research literature: dominant topics, questions, problem areas, research fronts.! Changing relationships between concepts.! 1.   2.   3.  
  43. 1.   2.   3.  

  44. 1.   2.   3.   b.  Encode historical interactions

     and  associa/ons  based  on   primary  and  secondary  texts.   a.  Collect  everything that these people wrote  between  1940  and   1970,  and  everything  wrigen  about  them.  
  45. Triples  +    

  46. None
  47. None
  48. Changing topology of research literature: dominant topics, questions, problem areas,

    research fronts.! 1.   2.   3.   Clustering texts based on topical affinity.! Identifying the terms & literatures that “hold” these clusters together.!
  49. 1.   2.   3.   Citation-based clustering! Topic modeling!

    e.g.  bibliographic  coupling   e.g.  Latent  Seman/c  Analysis   +!
  50. 1.   2.   3.   Changing relationships between concepts.!

    deme   ecotype   race   coenospecies   variety   cline   graded  patchwork   ?! ?! ?! ?! ?! ?! ?! ?! ?! ?! ?! ?! selec/on   adapta/on   ?! ?! ?! genotype   ?! ?!
  51. 1.   2.   3.   Statistical modeling! Functional annotation!

    e.g.  BEAGLE   Vogon  +  Quadriga   +!
  52. None
  53. 1.   2.   3.   Historical settings & relationships!

    Topology of research literature! Conceptual relationships! Change Over Time! 1950   1940   1930   1920   1960   1970   1980  
  54. Conclusion   1.  Innova/on  in  CAS  are  the  product  of

     a  complex  interplay   between  internal  and  external  condi/ons     2.  The  origin  of  varia/on  (phenotypic  of  scien/fic)  is  a   consequence  of  changes  to  the  (internal)  complex   regulatory  networks  that  govern  CAS     3.  These  isomorphic  proper/es  enable  a  transfer  of  both   concepts  and  methods  between  different  fields  concerned   with  innova/on     4.  Developmental  Evolu/on  is  a  more  adequate  mechanis/c   framework  for  understanding  innova/on  than  simple   popula/on  dynamics  
  55. Acknowledgments   For  intellectual  discussions/collabora)ons:   Eric  Davidson   Günter

     Wagner   Jane  Maienschein   Robert  Page   Bert  Hölldobler   Jürgen  Renn   Doug  Erwin   Colin  Allen   Hans-­‐Jörg  Rheinberger   Horst  Bredekamp   Olof  Leimar   Sander  van  der  Leeuw     Graduate  Students:   Erick  Peirson   Kate  MacCord   Guido  Caniglia   Yawen  Zhou   Lijing  Jiang   Nah  Zhang   Steve  Elliog   Julia  Damerow   Mark  Uleg   For  Financial  Support:     Na/onal  Science  Founda/on   Max  Planck  Society   WissenschaTskolleg  zu  Berlin   Arizona  State  University  
  56. None