ABSTRACT
Contractile cytoskeletal networks drive cell shape changes in many contexts including cytokinesis, the physical division of one cell into two. The primary source of force generation in contractile networks is non-muscle myosin II (NMMII), a molecular motor that assembles into bipolar ensembles that bind, slide, and crosslink actin filaments (F-actin). The multivalence of NMMII ensembles and their many roles have confounded the resolution of crucial questions including how the motors in NMMII ensembles impact each other’s binding, how the number of NMMII subunits affects network dynamics, and what affects the relative contribution of ensembles’ crosslinking versus motoring activities. Since measurements of ensembles are difficult, modeling of actomyosin networks has aided in discovering the complex behaviors of NMMII ensembles. Myosin ensembles have been modeled via several strategies with variable discretization/coarse-graining and unbinding dynamics, and while most result in global contractile behaviors, it remains unclear which strategies most accurately depict cellular activity. Here, we used an agent-based platform, Cytosim, to model NMMII ensembles via several strategies. Comparing the effects of bond type, we found that for discretized ensembles, only catch-slip motors exhibited processive translocation on immobilized F-actin. Conversely, all unbinding dynamics estimations allowed coarse-grained ensembles to translocate, though catch motors were the slowest and least processive. On simulated contractile rings, all motor types drove constriction, though again, only catch-slip NMMII motors generated effective contractile forces when fully discretized. In addition, rings fragmentation or coiled in all cases except those of fully discretized catch-slip motors. Changes in network connectivity not only affect contractile speed, as previously reported, but also seem to affect the amount of ring fragmentation and coiling. Together our results support the importance of modeling strategy chosen for NMMII ensembles, in terms of both discretization of motor units and bond type, for accurately estimating connectivity and providing more efficient contractility.
STATEMENT OF SIGNIFICANCE Agent-based simulations of contractile networks have provided many insights into the mechanics of actomyosin contractility, such as that which drives cytokinesis, the final step of cell division. Past attempts to predict the mechanics and dynamics of non-muscle contractility have lacked a consensus on how to best model the non-muscle myosin II motor ensembles. These highly complex ensembles of approximately 32 motors are responsible for driving contractility. Here, we comprehensively explore different methods for modeling non-muscle myosin II ensembles individually and within the context of contractile rings. We show that different levels of ensemble coarse-graining and different motor bonding behaviors under load can both have profound effects on contractile network dynamics.