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Abstract Understanding the behaviors of contractile actomyosin systems requires precise spatiotemporal control of filamentous myosin activity. Here, we develop a tool for optical control of contractility by extending the MyLOV family of gearshifting motors to create engineered filamentous myosins that change velocity in response to blue light. We characterize these minifilaments usingin vitrosingle-molecule tracking assays, contractility assays in reconstituted actin networks, and imaging of contractile phenotypes inDrosophilaS2 cells. The minifilaments change speed and/or direction when illuminated, display speeds that fall within and beyond the relevant physiological range, and display high processivities. Additionally, minifilament-driven contraction rates increase in blue light bothin vitroand in S2 cells. Finally, we develop an alternative design for minifilaments that only interact processively with actin in blue light. Engineered minifilaments can be used to dissect behaviors such as self-organization and mechanotransduction in contractile systems bothin vitroand in cells and tissues. 

In active materials, motor proteins produce activity while also modulating elasticity. 

Hydrodynamic theories effectively describe many-body systems out of equilibrium in terms of a few macroscopic parameters. However, such parameters are difficult to determine from microscopic information. Seldom is this challenge more apparent than in active matter, where the hydrodynamic parameters are in fact fields that encode the distribution of energy-injecting microscopic components. Here, we use active nematics to demonstrate that neural networks can map out the spatiotemporal variation of multiple hydrodynamic parameters and forecast the chaotic dynamics of these systems. We analyze biofilament/molecular-motor experiments with microtubule/kinesin and actin/myosin complexes as computer vision problems. Our algorithms can determine how activity and elastic moduli change as a function of space and time, as well as adenosine triphosphate (ATP) or motor concentration. The only input needed is the orientation of the biofilaments and not the coupled velocity field which is harder to access in experiments. We can also forecast the evolution of these chaotic many-body systems solely from image sequences of their past using a combination of autoencoders and recurrent neural networks with residual architecture. In realistic experimental setups for which the initial conditions are not perfectly known, our physics-inspired machine-learning algorithms can surpass deterministic simulations. Our study paves the way for artificial-intelligence characterization and control of coupled chaotic fields in diverse physical and biological systems, even in the absence of knowledge of the underlying dynamics.