A mathematical model coupling polarity signaling to cell adhesion explains diverse cell migration patterns
Rho GTPases are central regulators that control cell polarization and migration 15 , 16 , embedded in complex signaling networks of interacting components 17 ., Two members of this family of proteins , Rac1 and RhoA , have been identified as key players , forming a central hub that orchestrates the polarity and motility response of cells to their environment 18 , 19 ., Rac1 ( henceforth “Rac” ) works in synergy with PI3K to promote lamellipodial protrusion in a cell 16 , whereas RhoA ( henceforth “Rho” ) activates Rho Kinase ( ROCK ) , which activates myosin contraction 20 ., Mutual antagonism between Rac and Rho has been observed in many cell types 19 , 21 , 22 , and accounts for the ability of cells to undergo overall spreading , contraction , or polarization ( with Rac and Rho segregated to front versus rear of a cell ) ., The extracellular matrix ( ECM ) is a jungle of fibrous and adhesive material that provides a scaffold in which cells migrate , mediating adhesion and traction forces ., ECM also interacts with cell-surface integrin receptors , to trigger intracellular signaling cascades ., Important branches of these pathways are transduced into activating or inhibiting signals to Rho GTPases ., On one hand , ECM imparts signals to regulate cell shape and cell motility ., On the other hand , the deformation of a cell affects its contact area with ECM , and hence the signals it receives ., The concerted effect of this chemical symphony leads to complex cell behavior that can be difficult to untangle using intuition or verbal arguments alone ., This motivates our study , in which mathematical modeling of GTPases and ECM signaling , combined with experimental observations is used to gain a better understanding of cell behavior , in the context of experimental data on melanoma cells ., There remains the question of how to understand the interplay between genes ( cell type ) , environment ( ECM ) and signaling ( Rac , Rho , and effectors ) ., We and others 19 , 21–27 have previously argued that some aspects of cell behavior ( e . g . , spreading , contraction , and polarization or amoeboid versus mesenchymal phenotype ) can be understood from the standpoint of Rac-Rho mutual antagonism , with fine-tuning by other signaling layers 28 ., Here we extend this idea to couple Rac-Rho to ECM signaling , in deciphering the behavior of melanoma cells in vitro ., There are several overarching questions that this study aims to address ., In experiments of Park et al . 11 melanoma cells were cultured on micro-fabricated surfaces comprised of post density arrays coated with fibronectin ( FN ) , representing an artificial extracellular matrix ., The anisotropic rows of posts provide inhomogeneous topographic cues along which cells orient ., In 11 , cell behavior was classified using the well-established fact that PI3K activity is locally amplified at the lamellipodial protrusions of migrating cells 36 ., PI3K “hot spots” were seen to follow three distinct patterns about the cell perimeters: random ( RD ) , oscillatory ( OS ) , and persistent ( PS ) ., These classifications were then associated with three distinct cell phenotypes: persistently polarized ( along the post-density axis ) , oscillatory with two lamellipodia at opposite cell ends oscillating out of phase ( protrusion in one lamellipod coincides with retraction of the other , again oriented along the post-density axis ) , and random dynamics , whereby cells continually extend and retract protrusions in random directions ., The fraction of cells in each category was found to depend on experimental conditions ., Here , we focus on investigating how experimental manipulations influence the fraction of cells in different phenotypes ., For simplicity , we focus on the polarized and oscillatory phenotypes which can be most clearly characterized mathematically ., The following experimental observations are used to test and compare our distinct models of cell signaling dynamics ., For a graphical summary of cell phenotypes and experimental observations , see Fig 1 ., We discuss three model variants , each composed of ( A ) a subsystem endowed with bistability , and ( B ) a subsystem responsible for negative feedback ., In short , Model 1 assumes ECM competition for ( A ) and feedbacks mediated by GTPases for ( B ) ., In contrast , in Model 2 we assume GTPase dynamics for ( A ) and ECM mediated feedbacks for ( B ) ., Model 3 resembles Model 2 , but further assumes limited total pool of each GTPase ( conservation ) , which turns out to be a critical feature ., See Tables 1 and 2 for details ., We analyze each model variant as follows: first , we determine ( bi/mono ) stable regimes of subsystem ( A ) in isolation , using standard bifurcation methods ., Next , we parameterize subsystem ( B ) so that its slow negative feedback generates oscillations when ( A ) and ( B ) are coupled in the model as a whole ., For this to work , ( B ) has to force ( A ) to transition from one monostable steady state to the other ( across the bistable regime ) as shown in the relaxation loop of Fig 2d ., This requirement informs the magnitude of feedback components ., Although these considerations do not fully constrain parameter choices , we found it relatively easy to then parameterize the models ( particularly Models 1b and 3 ) ., This implies model robustness , and suggests that broad regions of parameter space lead to behavior that is consistent with experimental observations ., Parameters associated with rates of activation and/or feedback strengths are summarized in the S1 Text ., The parameters γi represent the strengths of feedbacks 1 or 2 in Fig 2, ( b ) and 2, ( c ) ., γR controls the positive feedback ( 2 ) of Rac ( via lamellipod spreading ) on ECM signaling , and γρ represents the magnitude of negative feedback ( 1 ) from Rho to ECM signaling ( due to lamellipod contraction ) ., γE controls the strength of ECM activation of Rho in both feedbacks ( 1 ) and ( 2 ) ., When these feedbacks depend on cell state variables , we typically use Hill functions with magnitude γi , or , occasionally , linear expressions with slopes γ ¯ i ., ( These choices are distinguished by usage of overbar to avoid confusing distinct units of the γ’s in such cases . ), Experimental manipulations in 11 ( described in Section “Experimental observations constraining the models” ) can be linked to the following parameter variations ., In view of this correspondence between model parameters and experimental manipulations , our subsequent analysis and bifurcation plots will highlight the role of feedback parameters γR , ρ , E in the predictions of each model ., Rather than exhaustively mapping all parameters , our goal is to use 1 and 2-parameter bifurcation plots with respect to these parameters to check for ( dis ) agreement between model predictions and experimental observations ( O1–O3 ) ., This allows us to ( in ) validate several hypotheses and identify the eventual model ( the Hybrid , Model 3 ) and set of hypotheses that best account for observations ., We first investigated the possibility that lamellipod competition is responsible for bistability and that GTPases interactions create negative feedback that drives the oscillations observed in some cells ., To explore this idea , we represented the interplay between lamellipodia ( e . g . , competition for growth due to membrane tension or volume constraints ) , using an elementary Lotka-Volterra ( LV ) competition model ., For simplicity , we assume that AE , LE depend linearly on Rac and Rho concentration , and set BE = 0 ., ( This simplifies subsequent analysis without significantly affecting qualitative conclusions . ), With these assumptions , the ECM Eq ( 3c ) reduce to the well-known LV species-competition model ., First consider Eq ( 3c ) as a function of parameters ( AE , LE ) , in isolation from GTPase input ., As in the classical LV system 45 , competition gives rise to coexistence , bistability , or competitive exclusion , the latter two associated with a polarized cell ., These regimes are indicated on the parameter plane of Fig 3a with the ratios of contractile ( LE ) and protrusive ( AE ) strengths in each lamellipod as parameters ., ( In the full model , these quantities depend on Rac and Rho activities; the ratios LE ( ρk ) /AE ( Rk ) for lamellipod k = 1 , 2 lead to aggregate parameters that simplify this figure ., ) We can interpret the four parameter regimes in Fig 3a as follows: I ) a bistable regime: depending on initial conditions , either lamellipod “wins” the competition ., II ) Lamellipod 1 always wins ., III ) Lamellipod 2 always wins ., IV ) Lamellipods 1 and 2 coexist at finite sizes ., Regimes I-III represent strongly polarized cells , whereas IV corresponds to an unpolarized ( or weakly polarized ) cell ., We next asked whether , and under what conditions , GTPase-mediated feedback could generate relaxation oscillations ., Such dynamics could occur provided that slow negative feedback drives the ECM subsystem from an E1-dominated state to an E2-dominated state and back ., In Fig 3a , this correspond to motion along a path similar to one labeled, ( d ) in Panel ( a ) , with the ECM subsystem circulating between Regimes II and III ., This can be accomplished by GTPase feedback , since both Rho and Rac modulate LE ( contractile strength ) and AE ( protrusion strength ) ., We show this idea more explicitly in Fig 3, ( c ) –3, ( e ) by plotting E1 vs LE1 while keeping LE1 + LE2 constant ., ( Insets similarly show E2 vs LE1 . ), Each of Panels ( c-e ) corresponds to a 1-parameter bifurcation plot along the corresponding path labeled ( c-e ) in Panel ( a ) ., We find the following possible transitions: In Fig 3c , we find two distinct polarity states: either E1 or E2 dominate while the other is zero regardless of the value of LE1; a transition between such states does not occur ., In Fig 3d , there is a range of values of LE1 with coexisting stable low and high E1 values ( bistable regime ) flanked by regimes where either the lower or higher state loses stability ( monostable regimes ) ., As indicated by the superimposed loop , a cycle of protrusion ( green ) and contraction ( blue ) could then generate a relaxation oscillation as the system traverses its bistable regime ., In Fig 3e , a third possibility is that the system transits between E1-dominated , coexisting , and E2-dominated states ., In brief , for oscillatory behavior , GTPase feedback should drive the ECM-subsystem between regimes I , II , and III without entering regime IV ., Informed by this analysis , we next link the bistable ECM submodel to a Rac-Rho system ., To ensure that the primary source of bistability is ECM dynamics , a monostable version of the Rac-Rho sub-system is adopted by setting n = 1 in the GTPase activation terms AR , Aρ in Eqs ( 3a ) and ( 3b ) ., We consider three possible model variants ( 1a-1c ) for the full ECM / GTPase model ., In view of the conclusions thus far , we now explore the possibility that bistability stems from mutual antagonism between Rac and Rho , rather than lamellipod competition ., To do so , we chose Hill coefficients n = 3 in the rates of GTPase activation , AR , Aρ ., We then assume that ECM signaling both couples the lamellipods and provides the requisite slow negative feedback ., Here we consider the case that GTPases are abundant , so that the levels of inactive Rac and Rho ( RI , ρI ) are constant ., We first characterize the GTPase dynamics with bR , ρ as parameters ., Subsequently , we include ECM signaling dynamics and determine how the feedback drives the dynamics in the ( bR , bρ ) parameter plane ., Isolated from the ECM influence , each lamellipod is independent so we only consider the properties of GTPase signaling in one ., This mutually antagonistic GTPase submodel is the well-known “toggle switch” 50 that has a bistable regime , as shown in the ( bR , bρ ) plane of Fig 4a ., ECM signaling affects the Rac / Rho system only as an input to bρ ., A linear dependence of bρ on Ek failed to produce an oscillatory parameter regime , so we used a nonlinear Hill type dependence with basal and saturating components ., Furthermore , for GTPase influence on ECM signaling we use Hill functions for the influence of Rho ( in LE ) and Rac ( in BE ) on protrusion and contraction ., We set AE = 0 in this model for simplicity ., ( Nonzero AE can lead to compounded ECM bistability that we here do not consider . ), Given the structure of the bρ − bR parameter plane and the fact that ECM signaling variables only influence bρ , we can view oscillations as periodic cycles of contraction and protrusion forming a trajectory along one of horizontal dashed lines in Fig 4a ., This idea guides our parametrization of the model ., We select a value of bR that admits a bistable range of bρ in Fig 4a ., Next we choose maximal and minimal values of the function bρ ( EK ) that extend beyond the borders of the bistable range ., This choice means that the system transitions from the high Rac / low Rho state to the low Rac / high Rho state over each of the cycles of its oscillation ., With this parametrization , we find oscillatory dynamics , as shown in Fig 4b ., We now consider the two-lamellipod system with the above GTPase module in each lamellipod; we challenge the full model with experimental observations ., Since each lamellipod has a unique copy of the Rac-Rho module , ECM signaling provides the only coupling between the two lamellipods ., First , we observed that inhibition of ROCK ( reduction of γρ in Fig 4b ) suppress oscillations ., However the resulting stationary state is non-polar , in contrast to experimentally observed increase in the fraction of polarized cells ( O1 ) ., We adjusted the coupling strength ( lc ) to ensure that this disagreement was not merely due to insufficient coupling between the two lamellipods ., While an oscillatory regime persists , the discrepancy with ( O1 ) is not resolved: the system oscillates , but inhibiting ROCK gives rise to a non-polarized stationary state , contrary to experimental observations ., Yet another problematic feature of the model is its undue sensitivity to the strength of Rac activation ( bR ) ., This is evident from a comparison of the dashed lines in Fig 4a ., A small change in bR ( vertical shift ) dramatically increases the range of bistability ( horizontal span ) , and hence the range of values of bρ to be traversed in driving oscillations ., This degree of sensitivity seems inconsistent with biological behavior ., It is possible that an alternate formulation of the model ( different kinetic terms or different parametrization ) might fix the discrepancies noted above , so we avoid ruling out this scenario altogether ., In our hands , this model variant failed ., However a simple augmentation , described below , addresses all deficiencies , and leads to the final result ., In our third and final step , we add a small but significant feature to the bistable GTPase model to arrive at a working variant that accounts for all observations ., Keeping all equations of Model 2 , we merely drop the assumption of unlimited Rac and Rho ., We now require that the total amount of each GTPase be conserved in the cell ., This new feature has two consequences ., First , lamellipods now compete not only for growth , but also for limited pools of Rac and Rho ., This , along with rapid diffusion of inactive GTPases across the cell 30 , 31 , 51 provides an additional global coupling of the two lamellipods ., This seemingly minor revision produces novel behavior ., We proceed as before , first analyzing the GTPase signaling system on its own ., With conservation , the bR − bρ plane has changed from its previous version ( Fig 4a for Model 2 ) to Fig 5a ., For appropriate values of bR , there is a significant bistable regime in bρ ., Indeed , we find three regimes of behavior as the contractile strength in lamellipod k , bρ ( Ek ) , varies: a bistable regime where polarity in either direction is possible , a regime where lamellipod j “wins” ( Ej > Ek , left of the bistable regime ) , and a regime where lamellipod k “wins” ( right of the bistable regime ) ., Only polarity in a single direction is possible on either side of the bistable regime ., As in Model 2 , we view oscillations in the full model as cycles of lamellipodial protrusion and contraction that modify bρ ( Ek ) over time , and result in transitions between the three polarity states ., To parameterize the model , we repeat the process previously described ( choose a value of bR consistent with bistability , then choose the dependence of bρ on ECM signaling so as to traverse that entire bistable regime . ) We couple the GTPase system with ECM equations as before ., We then check for agreement with observations ( O1–O3 ) ., As shown in Fig 5, ( e ) and 5, ( f ) , the model produces both polarized and oscillatory solutions ., To check consistency with experiments , we mapped the dynamics of this model with respect to both ROCK mediated contraction and PI3K mediated protrusion ( Fig 5c ) ., Inhibiting ROCK ( Fig 5b , decreasing γρ ) results in a transition from oscillations to polarized states , consistent with ( O1 ) ., PI3K upregulation promotes oscillations ( increasing γR , Fig 5c ) , characteristic of the more invasive cell line 1205Lu ( consistent with O2 ) ., Finally , increased fibronectin density ( increased γE , Fig 5d ) also promotes oscillations , consistent with ( O3 ) ., We conclude that this Hybrid Model can account for polarity and oscillations , and that it is consistent with the three primary experimental observations ( O1–3 ) ., Finally , Model 3 can recapitulate such observations with more reasonable timescales for GTPase and ECM dynamics than were required for Model variant 1b ., It is apparent that Model 3 contains two forms of lamellipodial coupling: direct ( mechanical ) competition and competition for the limited pools of inactive Rac and Rho ., While the former is certain to be an important coupling in some contexts or conditions 52 , we find that it is dispensable in this model ( e . g , see lc = 0 in Fig 5c ) ., We comment about the effect of such coupling in the Discussion ., In the context of this final model , we also tested the effect of ECM activation of Rac ( in addition to the already assumed effect on Rho activation ) ., As shown in Fig 5d ( dashed curves ) , the essential bifurcation structure is preserved when this modification is incorporated ( details in the S1 Text , and implications in the Discussion ) ., To summarize , Model 1b was capable of accounting for all observations , but required conservation of GTPase to do so ., This model was however rejected due to unreasonable time scales needed to give rise to oscillations ., Model 2 could account for oscillations with appropriate timescales , but it appears to be highly sensitive to parameters and , in our hands , inconsistent with experimental observations ., Model 3 , which combines the central features of Models 1b and 2 , has the right mix of timescales , and agrees with experimental observations ., In that final Hybrid Model , ECM based coupling ( lc ) due to mechanical tension or competition for other resources is not essential , but its inclusion makes oscillations more prevalent ( Fig 5b and 5e ) ., Furthermore , in this Hybrid Model , we identify two possible negative feedback motifs , shown in Fig 2b ., These appear to work cooperatively in promoting oscillations ., As we have argued , feedbacks are tuned so that ECM signaling spans a range large enough that bρ ( Ek ) traverses the entire bistable regime ( Fig 5a ) ., This is a requirement for the relaxation oscillations schematically depicted in Fig 2c ., Within an appropriate set of model parameters , either feedback could , in principle , accomplish this ., Hence , if Feedback 1 is sufficiently strong , Feedback 2 is superfluous and vice versa ., Alternatively , if neither suffices on its own , the combination of both could be sufficient to give rise to oscillations ., Heterogeneity among these parameters could thus be responsible for the fact that in ROCK inhibition experiments ( where Feedback 1 is essentially removed ) , most but not all cells transition to the persistent polarity phenotype ., The Hybrid Model ( Model 3 ) is consistent with observations O1–O3 ., We can now challenge it with several further experimental tests ., In particular , we make two predictions ., Migrating cells can exhibit a variety of behaviors ., These behaviors can be modulated by the cell’s internal state , its interactions with the environment , or mutations such as those leading to cancer progression ., In most cases , the details of mechanisms underlying a specific behavior , or leading to transitions from one phenotype to another are unknown or poorly understood ., Moreover , even in cases where one or more defective proteins or genes are known , the complexity of signaling networks make it difficult to untangle the consequences ., Hence , using indirect observations of cell migration phenotypes to elucidate the properties of underlying signaling modules and feedbacks are , as argued here , a useful exercise ., Using a sequence of models and experimental observations ( O1–O3 ) we tested several plausible hypotheses for melanoma cell migration phenotypes observed in 11 ., By so doing , we found that GTPase dynamics are fundamental to providing, 1 ) bistability associated with polarity and, 2 ) coupling between competing lamellipods to select a single “front” and “rear” ., ( This coupling is responsible for the antiphase lamellipodial oscillations ) ., Further , slow feedback between GTPase and ECM signaling resulting from contraction and protrusion generate oscillations similar those observed experimentally ., The single successful model , Hybrid Model ( Model 3 ) , is essentially a relaxation oscillator ., Mutual inhibition between the limited pools of Rac and Rho , sets up a primary competition between lamellipods that produces a bistable system with polarized states pointing in opposite directions ., Interactions between GTPase dynamics and ECM signaling provide the negative feedback required to flip this system between the two polarity states , generating oscillations for appropriate parameters ., Results of Model 3 are consistent with observations ( O1–O3 ) , and lead to predictions ( P1–P2 ) , that are also confirmed by experimental observations 11 ., In 11 , it is further shown that the fraction of cells exhibiting each of these behaviors can be quantitatively linked to heterogeneity in the ranges of parameters representing the cell populations in the model’s parameter space ., In our models , we assumed that the dominant effect of ECM signaling input is to activate Rho , rather than Rac ., In reality , both GTPases are likely activated to some extent in a cell and environment-dependent manner 41 , 42 ., We can incorporate ECM activation of Rac by amending the term AR so that its magnitude is dependent on ECM signaling ( Ek ) ., Doing so results in a shift in the borders of regimes we have indicated in Fig 5d ( dashed versus solid borders , see S1 Text for more details ) ., So long as Rho activation is the dominant effect , this hardly changes the qualitative results ., As the strength of feedback onto Rac strengthens , however , the size of the oscillatory regime is reduced ., Thus if feedback onto Rac dominates , loss of oscillations would be predicted ., This is to be expected based on the structure of these interactions ., Where ECM → Rho mediates a negative feedback , ECM → Rac mediates a positive feedback , which would be expected to suppress oscillatory behavior ., Thus while the ECM likely mediates multiple signaling feedbacks , this modeling suggest feedback onto Rho is most consistent with observations ., We have argued that conservation laws ( fixed total amount of Rac and fixed total amount of Rho ) in the cell plays an important role in the competition between lamellipods ., Such conservation laws are also found to be important in a number of other settings ., Fully spatial ( PDE ) modeling of GTPase function has shown that conservation significantly alters signaling dynamics 27 , 31 , 54 ., In 55 , it was shown that stochastically initiated hot spots of PI3K appeared to be globally coupled , potentially through a shared and conserved cytoplasmic pool of a signaling regulator ., Conservation of MIN proteins , which set up a standing wave oscillation during bacterial cell division , has been shown to give rise to a new type of Turing instability 56 ., Finally , interactions between conserved GTPase and negative regulation from F-actin in a PDE model was shown to give rise to a new type of conservative excitable dynamics 46 , 47 , which have been linked to the propagation of actin waves 57 ., These results provide interesting insights into the biology of invasive cancer cells ( in melanoma in particular ) , and shed light onto the mechanisms underlying the extracellular matrix-induced polarization and migration of normal cells ., First , they illustrate that diverse polarity and migration patterns can be captured within the same modeling framework , laying the foundation for a better understanding of seemingly unrelated and diverse behaviors previously reported ., Second , our results present a mathematical and computational platform that distills the critical aspects and molecular regulators in a complex signaling cascade; this platform could be used to identify promising single molecule and molecular network targets for possible clinical intervention .
Protrusion and retraction of lamellipodia are common features of eukaryotic cell motility ., As a cell migrates through its extracellular matrix ( ECM ) , lamellipod growth increases cell-ECM contact area and enhances engagement of integrin receptors , locally amplifying ECM input to internal signaling cascades ., In contrast , contraction of lamellipodia results in reduced integrin engagement that dampens the level of ECM-induced signaling ., These changes in cell shape are both influenced by , and feed back onto ECM signaling ., Motivated by experimental observations on melanoma cells lines ( 1205Lu and SBcl2 ) migrating on fibronectin ( FN ) coated topographic substrates ( anisotropic post-density arrays ) , we probe this interplay between intracellular and ECM signaling ., Experimentally , cells exhibited one of three lamellipodial dynamics: persistently polarized , random , or oscillatory , with competing lamellipodia oscillating out of phase ( Park et al . , 2017 ) ., Pharmacological treatments , changes in FN density , and substrate topography all affected the fraction of cells exhibiting these behaviours ., We use these observations as constraints to test a sequence of hypotheses for how intracellular ( GTPase ) and ECM signaling jointly regulate lamellipodial dynamics ., The models encoding these hypotheses are predicated on mutually antagonistic Rac-Rho signaling , Rac-mediated protrusion ( via activation of Arp2/3 actin nucleation ) and Rho-mediated contraction ( via ROCK phosphorylation of myosin light chain ) , which are coupled to ECM signaling that is modulated by protrusion/contraction ., By testing each model against experimental observations , we identify how the signaling layers interact to generate the diverse range of cell behaviors , and how various molecular perturbations and changes in ECM signaling modulate the fraction of cells exhibiting each ., We identify several factors that play distinct but critical roles in generating the observed dynamic: ( 1 ) competition between lamellipodia for shared pools of Rac and Rho , ( 2 ) activation of RhoA by ECM signaling , and ( 3 ) feedback from lamellipodial growth or contraction to cell-ECM contact area and therefore to the ECM signaling level .
Cells crawling through tissues migrate inside a complex fibrous environment called the extracellular matrix ( ECM ) , which provides signals regulating motility ., Here we investigate one such well-known pathway , involving mutually antagonistic signalling molecules ( small GTPases Rac and Rho ) that control the protrusion and contraction of the cell edges ( lamellipodia ) ., Invasive melanoma cells were observed migrating on surfaces with topography ( array of posts ) , coated with adhesive molecules ( fibronectin , FN ) by Park et al . , 2017 ., Several distinct qualitative behaviors they observed included persistent polarity , oscillation between the cell front and back , and random dynamics ., To gain insight into the link between intracellular and ECM signaling , we compared experimental observations to a sequence of mathematical models encoding distinct hypotheses ., The successful model required several critical factors ., ( 1 ) Competition of lamellipodia for limited pools of GTPases ., ( 2 ) Protrusion / contraction of lamellipodia influence ECM signaling ., ( 3 ) ECM-mediated activation of Rho ., A model combining these elements explains all three cellular behaviors and correctly predicts the results of experimental perturbations ., This study yields new insight into how the dynamic interactions between intracellular signaling and the cell’s environment influence cell behavior .
cell physiology, cell motility, engineering and technology, enzymes, signal processing, biological cultures, enzymology, cell polarity, developmental biology, gtpase signaling, cell cultures, melanoma cells, cellular structures and organelles, research and analysis methods, extracellular matrix signaling, proteins, extracellular matrix, guanosine triphosphatase, biochemistry, signal transduction, hydrolases, cell biology, cell migration, biology and life sciences, cultured tumor cells, cell signaling