Beam finite-element model of a molecular motor for the simulation of active fibre networks

Modeling the dynamic growth and branching of actin filaments

As an essential component of the cytoskeleton, actin filaments play a key role in a variety of cellular physiological activities. To better understand the function of actin filaments, which are a special kind of polymer chain, researchers have started to focus on the Brownian dynamics of polymers. Currently, to study the dynamics of polymers, classical explicit bead-spring models and finite-element methods (FEMs) have both been broadly used. However, compared to bead-spring models, FEMs can address the mechanical properties of actin filaments and actin networks with more detail and better accuracy. However, current FEMs do not consider the dynamic assembly of actin into an actin filament network. Here, we extend the traditional FEM and present a new framework of the FEM based on the co-rotational grid method, which allows us to simulate the dynamic growth and branching of actin filaments. Several examples are studied. The proposed numerical model is capable of capturing the dynamic assembly of actin filaments.

Mechanical and Systems Biology of Cancer

Mechanics and biochemical signaling are both often deregulated in cancer, leading toincreased cell invasiveness, proliferation, and survival. The dynamics and interactions of cytoskeletal components control basic mechanical properties, such as cell tension, stiffness, and engagement with the extracellular environment, which can lead to extracellular matrix remodeling. Intracellular mechanics can alter signaling and transcription factors, impacting cell decision making. Additionally, signaling from soluble and mechanical factors in the extracellular environment, such as substrate stiffness and ligand density, can modulate cytoskeletal dynamics. Computational models closely integrated with experimental support, incorporating cancer-specific parameters, can provide quantitative assessments and serve as predictive tools toward dissecting the feedback between signaling and mechanics and across multiple scales and domains in tumor progression.