Computa(onal  modelling  of  the  regula(on  of  cell  adhesion,  a  cri(cal  step  of  EMT  

 •  The  current  model  predicts  a  role  of  signals  from  neighbouring  cells  (RPTP,  FAT4  and  DELTA),  which  

would  now  need  to  be  explored  experimentally.  •  SystemaEc  analyses  of  single,  double  and  triple  perturbaEons  on  model  components  may  generate  

further  testable  predicEons.  •  Model  will  be  extended  to  encompass  cell  populaEon  dynamics  to  further  invesEgate  the  role  of  the  

microenvironment  in  the  control  of  cell  adhesion  (use  of  EpiLog,  a  tool  for  the  logical  modelling  of  2D  grids  of  cells,  hIp://epilog-­‐tool.org).    

Ricardo  J.  Pais,  Claudine  Chaouiya        {rpais,chaouiya}@igc.gulbenkian.pt    

Ins$tuto  Gulbenkian  de  Ciência,  Oeiras,  Portugal  

1.  Savagner  P.    Curr  Top  Dev  Biol.  112,  273–300  (2015).    2.  Lamouille,  S.  et  al.  Nat.  Rev.  Mol.  Cell  Biol.  15,  178–96  (2014).  3.  Farahani,  E.  et  al.  Carcinogenesis  35,  747–59  (2014).  4.  Kawauchi  T.  Int.  J.  Mol.  Sci.    13,  4564–4590  (2012).  5.  Krakhmal  N.  V.  et  al.  &    Acta  Naturae    7(2),  17–28  (2015).  6.  Chaouiya,  C.  et  al.  Methods  Mol.  Biol.  808,  463–79  (2012).  7.  Mendes,  N.  et  al.  arXiv:1411.3539  (2014).  

This  work  was  supported  by  the  Fundação  para  a  Ciência  e  Tecnologia   (FCT  grant  PTDC/BEX-­‐BCB/0772/2014).   RP   is   funded   by   the   FCT   (grant  SFRH/BD/52175/2013).   CC   is   funded   by   the  Fundação   Calouste   Gulbenkian.   RP   and   CC  further   thank   F.   Janody   and   J.   Carneiro   for  fruieul  discussions.    

Changes  in  cell  adhesion  properEes  have  been  associated  to  Epithelial  to  Mesenchymal  TransiEon  (EMT)1-­‐3.  During  EMT,  epithelial  cells  lose  the  E-­‐cadherin  mediated  cell-­‐cell  adhesion,  increase  cell–matrix  (focal)  adhesion  and  convert  into  mesenchymal  cells  with  migratory  capacity2,3.  This  process  is  thought  to  contribute  for  cancer  invasion2,3.  However,  intermediate  phenotypes  with  different  adhesive  properEes  have  been  found  in  invasive  cancers4,5.  MulEple  micro-­‐environmental  cues,  signalling  pathways  and  regulatory  modules  with  crosstalk  points  and  feedbacks  regulate  the  cell  adhesion  status2-­‐4.    To  understand  how  cell  adhesion  is  governed,  we  use  on  a  computaEonal  modelling  approach,  building  a  logical  model  of  the  involved  network  under  the  control  of  relevant  micro-­‐environmental  cues.  Here,  we  present  our  modelling  approach  and  some  preliminary  results  of  the  model  analysis.    

Phenotypes  depend  on  micro-­‐environment;  These  plots  provide  the  percentages  of  stable  states  corresponding  to  specific  phenotypes,  for  different  combinaEons  of  cell-­‐cell  contact  signals   and   for   wild-­‐type   and   perturbed   condiEons   (alteraEons   documented   in   tumour  cells).  The  effect  of  cell-­‐cell  contact  signals  (RPTP  and  FAT4  ligands)  is  shown.  

 

Cell-­‐matrix  adhesion    (Boolean)      

FOCAL_ADHESION  = 1  IF    ITGab  &  F-­‐acEn_FA    &  !E-­‐Cad:2      (high) 0  otherwise                      (  low)

,,,,  

Challenging  the  model  by  comparing  the  phenotypes  (stable  states)  reached  from  an  epithelial  state,   with   experimental   observaEons   on   adhesive   properEes   and   molecular   alteraEons   in  mammalian  epithelial  cell  lines  exposed  to  various  micro-­‐environments  or  mutaEons.  

HGF=1  

Wnt=1  

StarEng  state  

Published  experimental  data  on  epithelial  cell  lines    

 EGF  addiEon    collagen  type  I  addiEon    

EGF  +  collagen  type  I  addiEon  

HGF  addiEon  

Wnt  addiEon  

TGFb  addiEon    

KO  of  120    

b-­‐Catenin/E-­‐cadherin  mutaEon  

CK1  overexpression  

CD44  overexpression  

SRC  overexpression  

SimulaEons  

Multi-valued Boolean  

AcEvaEon  

Slower  processes  

Dual  

Inpu

t  con

diEo

ns  or  p

erturbaE

ons  (Fixed  compo

nents)  

EGF=1  

ECM=1  

ECM=1  &  EGF=1  

TGFb=1  

 p120=0  

 b-­‐Cat_m=0  

 CK1=1  

 CD44=1  

 SRC=1  

INTRODUCTION  

LOGICAL  MODELLING  Discrete  variables  

CDH1   0      InacEve  E-­‐cadherin  transcripEon  1      AcEve  E-­‐cadherin  transcripEon  

E-­‐Cad    <<  0        Absence  of  E-­‐cadherin  protein  1  Low  funcEonal  E-­‐cadherin    2  High  funcEonal  E-­‐cadherin    

Example:  regulaEon  of  E-­‐cadherin/catenins  complex  

Regulatory  graph    

Asynchronous  vs  priority  classes  (to  discard  irrelevant  trajectories)  Fast  (1st  class):  processes  occurring  in  a  Eme  scale  of  seconds  Slow  (2nd  class):    processes  occurring  in  a  Eme  scale  of  minutes  

Upda(ng  scheme  

Logical  func(ons    (Logical  operators    &  (AND),  |  (OR),  !  (NOT))  E-­‐Cad  =  2  if  CDH1  &  p120  &  Rap1  E-­‐Cad  =  1  if  CDH1  &  Rap1  &  !p120  E-­‐Cad  =  0  otherwise  

 CDH1  =  1  if  !ZEB  &  !  SNAIL    CDH1  =  0  otherwise  

A   logical   model   is   defined   by   a  regulatory   graph,   discrete   variables  associated   to   the   nodes   and   logical  func(ons  defining  the  evoluEon  of  these  variables6.  Discrete   dynamics   are   constructed  following   an   updaEng   scheme,   e.g.   the  asynchronous   dynamics   includes   all  concurrent  transiEons.  ProperEes  of  interest  relate  to  aXractors  and   their   reachability   proper(es.   These  are   checked   on   State   TransiEon   Graph  (STG)   represenEng   the   states   and  trajectories.    

hIp://ginsim.org  

AB

STR

AC

TION

Model        

Biological  process  

CELL  ADHESION  REGULATORY  NETWORK  

MODEL  VALIDATION  

References

Switching   phenotypes   upon   changes   of   signals   from   neighbouring   cells   (RPTPL,   FAT4   and  DELTA),   remaining   inputs   maintained   constant,   reflecEng   a   prototypic   “tumoral”  microenvironment  (IL6  =1    Hypoxia=1    ECM=1      HGF=1      EGF=0).  SimulaEons  performed  in  GINsim  under   asynchronous   update.   Reachability   probabiliEes   in   the   case   of  mulE-­‐stability  were  obtained  by  running  10  000  random  simulaEons.  

RPTP  =  1  

RPTP=FAT4=0  RPTP=0  &  FAT4=1  

RPTP=1  

Wild-­‐Type   RPTP=0  &  FAT4=1  

RPTP=FAT4=0  

DELTA=1  

RPTP  =  1  &  DELTA=0  

RPTP=FAT4=0  &  DELTA=0      RPTP=DELTA=0  &  FAT4=1    

TGFb  E1    

RPTP=1  &  DELTA=0  

DELTA=1  

p=0.7  

p=0.3  

Acknowledgements CONCLUSION / PROSPECTS

MODEL  PREDICTIONS  

Model  outputs  (readouts)  

Cell-Cell Adhesion  0    No  cell-­‐cell  adhesion  1    Low  cell-­‐cell  adhesion    2    High  cell-­‐cell  adhesion    

0    Normal  cell-­‐ECM  adhesion  1  Increased  cell-­‐ECM  adhesion  2  Enhanced  cell-­‐ECM  adhesion    

Focal Adhesion  Inputs  (micro-­‐environment)

Membrane    protein

InhibiEon  

Signalling  cascade   TranscripEon  Factor Gene/RNA

Autocrine  signal  

The  model  includes  51  components  and  121  interacEons.  Growth  factors  (HGF  and  EGF),  interleukin   6,   hypoxia,   ECM   sEffness   and   signals   from   adjacent   cells   (DELTA,   RPTP   and  FAT4  ligands)  are  input  components  (micro-­‐environmental  cues).  The  output  components  indicate  the  degrees  of  cell-­‐to-­‐cell  and  cell-­‐matrix  adhesion  (readouts).    

Focal  Adhesion  

Cell-­‐Ce

ll  Ad

hesio

n  

MigraEon  potenEal  

MigraEon  potenEal

Wild   type   stable   states   (unperturbed   model),  recapitulaEng   cel l   phenotypes   with   the  corresponding  values  of  internal  components  

Epithelial-like

Amoeboid-like Mesenchymal-like

Hybrid

Mesenchymal  markers   Epithelial  markers  

0

Cell  phenotypes  given  by  the  model  outputs  

RPTP  =  0    &  FAT4  =  0  

Distrib

uEon

 of  SS/ph

enotype  

12 16

48

16 16

112

32

12

36

1 2

0 1

2

Images  from  ref  4  

RPTP  =  1    &  FAT4  =  *    RPTP  =  0  &  FAT4  =1