Challenge:  At  any  given  +me,  each  par+cle  can  react  with  any  number  of  other  par+cles  within  a  neighborhood.  How  to  choose  reagents  independently,  autonomously  and  consistently  across  the  whole  system  within  a  parallel  computa8onal  framework?  Example:  Par+cles  of  type  A  and  type  B  can  react  within  a  neighborhood  to  produce  Par+cle  C.  Mul+ple  objects  of  same  species  can  be  present  within  the  neighborhood.  Each  thread  has  to  be  aware  of  the  selec8on  of  other  threads  in  the  system  and  adjust  its  own  selec8on  accordingly.  

Solu8on:  We  introduce  a  novel  and  efficient  Parallel  Selec+on  Algorithm  that  maintains  the  state  of  the  system  and  selects  reagents  and  carries  out  reac+ons  in  parallel.  

Massively  Parallel  Implementa8on  On  a  massively  parallel  plaEorm  each  par+cle  is  assigned  a  work-­‐unit(thread)  which  independently  and  autonomously  simulates  each  par+cle’s  behavior,  by  broadly  following  the  steps:  

1)  Ini+alize  par+cle  posi+ons  and  movements.  2)  Choose  reagents  and  carryout  reac+ons  in  parallel.  3)  Update  the  state  of  reac+ons,  new  par+cles  and  par+cle  life  spans.  4)  Update  par+cle  co-­‐ordinates  by  avoiding  collisions.    5)  Increment  fixed  global  +me  step.  6)  Repeat  steps  2  through  5  un+l  simula+on  +me  is  reached.  

 Simula8on  and  Study  of  Large-­‐Scale  Bacteria-­‐Materials  Interac8ons  via  BioScape  Enabled  by  GPUs  Jie  Li,  Vishakha  Sharma,  Narayan  Ganesan,  and  Adriana  Compagnoni  

Department  of  Electrical  and  Computer  Engineering  Department  of  Computer  Science,  Stevens  Ins9tute  of  Technology  

Results  and  Conclusion  

BioScape   in   the   context   of   a   real   applica+on:   bio-­‐triggered   drug   delivery   system   for   infec+on-­‐resistant  medical  implants.  The  four  objects:  •   Planktonic  bacteria  (BACF)  •   Adsorbed  bacteria  (BACB)  •   Bound  an+bacterial  agent  (MOLB)  •   Released  an+bacterial  agent  (MOLF)  

Conclusions:  Realis+c  modeling  and  simula+on  studies  of  complex  biochemical  pathways,  such  as   glycosis,   signal   transduc+on,   cancer,   PI3K-­‐Mtor   etc.,   with   millions   of   interac+ng   par+cles   is  en+rely  possible  via  GPU-­‐Bioscape.  • Virtual   silicon-­‐based  experimental   test-­‐bed  can  greatly  help  narrow  down  and   reduce   the  +me  needed  for  costly  wet-­‐lab  experiments  and  accelerate  finding  cure  for  diseases.  

Concurrency  model  for  Bacteria-­‐Materials    interac5ons  depic5ng  the  objects  and  their  A<ributes.  

Example:  Modeling  Large-­‐Scale  Bacteria-­‐Materials  Interac8ons  

Figure(Top)  (a)  CPU  simula5on  of  Gillespie  Algorithm  for  1000  par5cles  (Compare  behavior  with  Fig  (b)).  (b)  GPU-­‐Bioscape  simula5on  of  a  total  of  100,000  par5cles  under  same  seJngs.  (c)  GPU-­‐BioScape  simula5on  of  a  total  of  500,000  par5cles.  The  seJngs  were  chosen  such  that  a  delicate  balance  between  bacteria  and  AmA  leads  to  pH  oscilla5ons.  (d)  Higher  ini5al  bacterial  concentra5on  leads  to  bacterial  prolifera5on.  

Introduc8on  and  Mo8va8on  •   Biological  systems  encompass  complexity  that  far  surpasses  most  man-­‐made  systems.  

•   Modeling  and  simula+on  of  large  and  complex  biological  systems  is  computa+onally  intensive.  

• Need  a  formal  modeling  language  and  large  computa+onal  power  in  order  to  simulate  the  behavior  of  a  complex  biological  system.  

• We  present  a  general  language  for  modeling  and  describing  biological  processes,  BioScape.  

• The   interac+on   and   dynamics   of   the   biological   system   are   implemented   on   GPUs     in   order   to  leverage  the  power  of  massively  parallel  processors.  

•   BioScape   is   based  on   the  Stochas5c  Pi-­‐Calculus,   and   it   is  mo+vated  by   the  need   for   individual-­‐based,   con+nuous   mo+on   and   con+nuous   space   in   modeling   complex   bacteria   materials  interac+ons  in  a  reac+ve  environment  in  3D  space.  

• Our  driving  example  is  a  bio-­‐triggered  drug  delivery  system  for  infec+on-­‐resistant  medical  implants.  

• The  modeling   and   simula+on   framework   helps   in   iden+fying   biological   targets   and  materials   to  treat  bacterial  infec+ons.  

Parallel  and  Consistent  Reac8on  Selec8on  

Computa+onal  Workgroup  

Par+cle  A:  Par+cle  B:  

Challenge:  How  to  manage  and  update  millions  of  par8cles  that  are  dynamically  created  and  consumed  in  various  reac8ons?  Solu8on:  The  efficient  dynamic  reconfigura+on  technique  collects  resources  from  inac+ve/dead  par+cles  and  reuses  them  for  newly  created  par+cles  with  very  liale  overhead.  

*Bound  AmA  par+cles  and  bound  Bacteria  not  shown  

Figure(Right):  Shows  the  concentra+on  profile  of  par+cles  in  the  bio-­‐film,  (a)  Ini+ally  at  +me  t=0,  (b)  Aeer  +me  t=100,  the  diffusion  of  AmA  par+cles  is  triggered  by  pH  change  due  to    metabolizing  bacteria.  

(Boaom):  Performance  of  GPU-­‐Bioscape  (ms/+mestep)  with  increasing  number  of  par+cles.  

Bioscape  example  release@0.004, 2.0 kill@0.001, 0.5 bind@3.0, 0.2 BacF()@resBF, stepBF, shapeBF = mov.BacF() + !bind.BacB() + delay@0.2.(BacF() | BacF()) + delay@0.005.(BacF() | HIon()) + ?kill.DeadBac()

Reaction rates and radius

Movement Bind Reproduction Metabolism Destroy

ACM  Conference  on  Bioinforma8cs,  Computa8onal  Biology  and  Biomedical  Informa8cs  (ACM  BCB),  Orlando,  FL,  October  7  –  10,  2012