Optimal control and design of cytoskeleton-based dynamic biomaterials

The ability of living systems to dynamically rearrange their constituents is one that we wish to endow in synthetic materials. While we have built active systems using reconstituted cytoskeletal components (such as microtubules, actin, motor proteins, and cross-linkers), there is no general framework for combining elements to achieve specific emergent spatiotemporal patterns. Using active nematics as an exemplar, I outline how the inverse problem toolset combined with a dynamical systems view of active matter enables both exogenous and endogenous material control. Inspired by new optogenetically enabled assays, I demonstrate how optimal control theory determines spatiotemporal light input that qualitatively changes the dynamics of an active nematic. Next, recognizing that the ultimate goal is to create autonomous materials, I present a framework for designing reaction-diffusion systems that couple to nematohydrodynamics to perform control endogenously. This framework uses a dynamical systems-centric machine learning technique to generate a set of partial differential equations that approximate the desired behavior. I end with a discussion about how the former, optimal control, might be applied to steer material flows in living systems.