Background The dynamics of the actomyosin machinery is at the core of many important biological processes. Several relevant cellular responses such as the rhythmic compression of the cell cortex are governed, at a mesoscopic level, by the nonlinear interaction between actin monomers, actin crosslinkers, and myosin motors. Coarse-grained models are an optimal tool to study actomyosin systems, since they can include processes that occur at long time and space scales, while maintaining the most relevant features of the molecular interactions. Results Here, we present a coarse-grained model of a two-dimensional actomyosin cortex, adjacent to a three-dimensional cytoplasm. Our simplified model incorporates only well-characterized interactions between actin monomers, actin crosslinkers and myosin, and it is able to reproduce many of the most important aspects of actin filament and actomyosin network formation, such as dynamics of polymerization and depolymerization, treadmilling, network formation, and the autonomous oscillatory dynamics of actomyosin. Conclusions We believe that the present model can be used to study the in vivo response of actomyosin networks to changes in key parameters of the system, such as alterations in the attachment of actin filaments to the cell cortex.

The dynamics of the actomyosin machinery is at the core of many important biological processes. Several relevant cellular responses such as the rhythmic compression of the cell cortex are governed, at a mesoscopic level, by the nonlinear interaction between actin monomers, actin crosslinkers and myosin motors. Coarse grained models are an optimal tool to study actomyosin systems, since they can include processes that occur at long time and space scales, while maintaining the most relevant features of the molecular interactions. Here, we present a coarse grained model of a two-dimensional actomyosin cortex, adjacent to a three-dimensional cytoplasm. Our simplified model incorporates only well characterized interactions between actin monomers, actin cross- linkers and myosin, and it is able to reproduce many of the most important aspects of actin filament and actomyosin network formation, such as dynamics of polymerization and depolymerization, treadmilling, network formation and the autonomous oscilla- tory dynamics of actomyosin. Furthermore, the model can be used to predict the in vivo response of actomyosin networks to changes in key parameters of the system, such as alterations in the anchor of actin filaments to the cell cortex.

Autonomous oscillatory dynamics are ubiquitous at every level in Biology. At the cellular level, one of the most relevant and well characterized examples of periodic behavior is the cyclic assembly and disassembly of actomyosin networks. In Drosophila, these oscillations induce the robust contraction and expansion of individual cells required for correct dorsal closure, while in the follicular epithelium that surrounds the germline, periodic contractions of the basal actomyosin network are required for proper elongation of the egg chamber. While some studies suggest that actomyosin oscillations are driven by upstream signaling or mechanochemical features, we have recently proposed that they arise as a systems property from the competition between two well characterized features of the actomyosin machinery: 1) cooperative assembly of actin networks mediated by Actin crosslinker proteins and 2) tension-induced disassembly of actin networks mediated by myosin motors. Here, we perform experiments in amnioserosa and in the follicle cells of drosophila and simulations using a coarse-grained model of the actomyosin cortex to characterize the properties of the oscillations and how they depend on different features of the system. We also compare model and experiments to study the dynamics of actomyosin flows and the effect of mechanical coupling between cells in the tissue. In conclusion, our model is a powerful tool to study key features of actomyosin oscillations, from the effect of the individual components to network properties and finally supra-cellular organization of the oscillations at the tissue level.