Intracellular networks of entangled or cross-linked polymers, such as the actin and microtubule cytoskeleton, interact with and respond to mechanical cues from the environment, influencing how cells grow and divide, how stem cells differentiate into specific cell types, and how cancer cells proliferate, among other important molecular and cellular phenomena. In many cases, the structure of these networks is dynamically altered by the mechanical feedback of lipid membranes and cytoplasmic flows. However, the majority of current modeling and computational approaches focus on computational tractability at the expense of these mechanical feedbacks. A central theme of this project is that the development of physically realistic models of intracellular polymer networks (and other biological matter) requires a computational approach that efficiently integrates the mechanics of the network with the hydrodynamics of intracellular fluid flows and the mechanical interactions with biological membranes. Such integrative computational efforts are key to building a better understanding of intracellular phenomena. In particular, they hold the potential to help diagnose and treat cytoskeleton-related diseases and guide the development of stem cell techniques. The proposed work will help with the scientific and professional development of both graduate students and undergraduate students. It will also integrate excellent educational opportunities including the development of open education resources; promoting computer-coding literacy as a means to investigate physical phenomena through the development of an educational website with specialized computer coding activities pertaining to the proposed project; conducting boot camps to train undergraduate students on "Mesoscale computational modeling of intracellular soft matter" and; working with the Science Centre at Brown University to engage high school students from underrepresented communities in scientific computing and the physics of soft matter.
The PI proposes a synergistic combination of approaches consisting of dissipative particle dynamics (DPD) simulations and stochastic homogenization methods. DPD can accurately model the molecular-scale motion of polymers and fluids without the computational burden that normally makes alternative simulations (e.g., molecular dynamics) unviable. Stochastic homogenization methods, on the other hand, provide the means of efficiently extracting bulk mechanical properties from the mesoscale DPD description. Specific aims of the project include (i) the development of specialized particle-based methods for investigating the mechanical interactions between cytoplasmic flows and intracellular matter, (ii) a computational and analytic investigation of stochastic homogenization approaches to developing mesoscopic mechanical descriptions of intracellular matter, (iii) applications of the developed algorithms to the computational modeling of specific examples of biological matter, such as the actin cytoskeleton and DNA macromolecules, and (iv) the clustering and classification of simulation data in order to characterize the dynamics of biological matter in the systems under investigation. Given the recent interest in intracellular polymer networks (e.g., the actin cytoskeleton) as therapeutic targets in a broad array of pathological conditions, including cancer, neurodegenerative diseases, and kidney disease, the proposed computational approach is expected to advance significant biomedical applications, especially therapeutic perturbations of cytoskeletal dynamics.
