Lakshminarayanan Mahadevan

Spiders arrest their fall by abseiling on dragline silk, varying silk release rate to avoid excessive strains that could break the thread. To understand how this is achieved, we develop a minimal dynamical model of abseiling and formulate a time-optimal control problem subject to physical limits on silk strain and dragline spinning rate. We find that the optimal trajectory includes an interval over which the strain is constant, resulting in uniform deceleration consistent with published experiments. To test the generality of this strategy, we then turn to a distinct extreme motor behavior seen in jumping spiders: using a dragline anchored at the takeoff site to correct their orientation while jumping at speeds exceeding 100 body lengths per second. We show that the same constant-strain strategy suffices to counteract the initial body rotation and ensure an upright landing without significant loss of range. Together, our theoretical framework suggests how strain regulation may serve as a strategy for controlling extreme movements such as abseiling seen in many spiders and may be specialized to tasks such as aerial braking and reorientation in jumping spiders.

A simplified model of the heart's mitral valve shows that flow fluctuations can cause a soft valve to close at about one-tenth the pressure required under steady flow.

Interception of moving objects is a universal pattern of animal behavior with many industrial applications. Inspired by the behavior of the characid fish Brycon guatemalensis , which catches fruits falling onto the surface of a river, we study one-shot (i.e., open-loop) interceptions using both stochastic optimal control theory and reinforcement learning. In the first instance, an agent actuates a control (wait time before launch and heading angle) to meet its target, given a noisy estimate of the target initial state. In a minimal setting, where launch velocity and actuated control are constants, we show that the optimal wait time is attenuated by noise in both the target measurement and control actuation, as well as agent energy conservation. We then derive an upper bound for the heading angle variance which scales as the ratio of the non-vanishing optimal interception error to the initial planar separation. Complementing our control framework, our deep reinforcement learning approach shows that the neural network representing the fish first learns the value function for the interception, then learns how to aim, and finally learns the optimal wait time. Beyond the specific problem at hand, our results may also have applications to problems with little time for feedback or corrections such as perching, landing, and docking.

Many morphogenetic processes in living systems are driven by active stresses within epithelial sheets that undergo simultaneous changes in thickness, curvature, and internal order. Existing theoretical descriptions of active nematics have largely focused on effectively two-dimensional layers of fixed thickness or prescribed geometry, limiting their ability to capture the coupled thickness-shape dynamics that are central to epithelial morphogenesis. Here, starting from a three-dimensional active nematohydrodynamic description, we derive a hierarchy of low-dimensional continuum models for thin active layers with finite thickness that can deform, flow, and reorient. Using an asymptotic long-wavelength theory, we show how activity couples nematic order to thickness variations and surface geometry in both flat and curved films. In flat layers, allowing thickness to evolve qualitatively modifies classical bend and splay instabilities, producing activity-driven modes that are not directionally constrained. In curved geometries, such as cylindrical films relevant to tubular epithelia and cnidarian body columns, thickness and shape instabilities become intrinsically coupled. Notably, we find that in isotropic phases both extensile and contractile activity can induce nematic order when thickness and shape are dynamic-contrasting sharply with active nematics on fixed substrates. Our results suggest experimentally testable mechanisms by which active stresses in epithelia can drive invagination, thickening, and curvature generation during development. More broadly, this framework provides a minimal physical theory linking active nematic order, thickness regulation, and morphogenesis in living tissues.

We introduce an additive approach for the design of a class of transformable structures based on two-bar linkages (“scissor mechanisms”) joined at vertices to form a two-dimensional mesh which we call a pantograph lattice. Our approach shows how these lattices unfold from a one-dimensional collapsed state to two-dimensional surfaces of single and double curvature. We provide an algorithm for growing pantograph structures that allows us to explore the full space of possible mechanisms, and we use it to computationally design and physically assemble a series of examples of varying complexity. We finally demonstrate a streamlined method for automated fabrication of pantograph lattices using multimaterial 3D printing.

Diseases of the (mitral and tricuspid) atrioventricular valves (AVV), which regulate inflow from the atria to the ventricles, can result in severe obstruction to inflow (stenosis) or valvular leakage (regurgitation), requiring surgical intervention. In patients with small annulus diameters ( < 19 mm), valve replacement is a clinical challenge limited by prosthesis size constraints, lack of growth potential, suboptimal durability, and elevated thrombosis and bleeding risk. While living valve transplantation (LVT) has re-opened the possibility of using allogeneic valve tissue capable of growth and remodeling, translating this to the AVV has been challenging given the anatomical complexity of the sub-valvular apparatus. Here, we propose a strategy using a replacement bi-leaflet cylindrical valve fabricated from donor AVV tissue and artificial chordae, with a geometry designed to mimic the native AVV and engineered to satisfy predefined clinical targets. Pulse duplicator experiments allowed characterization of valve dynamics in terms of clinically important attributes framed as dimensionless parameters. A multi-objective optimization allowed us to identify an optimal design which we implemented in porcine AVV replacements (n=6). Our results demonstrated favorable hemodynamics with minimal regurgitation and stenosis, suggesting a promising method for patient-optimized valve replacements.

Epithelial cortinoids, fluid-filled shells formed from induced pluripotent stem cells (iPSCs), must accommodate large deformations during growth and morphogenesis. Using inflation–deflation assays and high-resolution imaging, we find that these fluid-filled shells are weakly pressurized and achieve extreme deformability through reversible soft modes of deformation accommodated by the cytoskeleton. We show that cytoskeletal elements such as actin localized along lateral cell edges undergo tilt and bend instabilities that buffer mechanical load by decoupling apico–basal stretching from lateral extension. These reversible instabilities act as elastic safety valves, permitting large shape changes without loss of epithelial hydraulic and topological integrity. A minimal theoretical and computational model demonstrates how tilt and bend reduce effective resistance to radial thinning and explains the observed pressure–strain softening. Thus, iPSC shells exploit reversible cytoskeletal instabilities as mechanical buffers, enabling robust tolerance of large deformations in developing epithelia.

T-cells use molecular reactions with nonequilibrium error correction, i.e., proofreading, to discriminate between nearly identical antigens with high specificity and sensitivity. These receptor binding events are known to be force sensitive, yet traditional schemes of proofreading focus on reaction kinetics alone and do not consider the role of force dependent catch/slip bond behavior or interactions with mechanically engaged coreceptors such as adhesion molecules. To address this, we propose a minimal framework for proofreading of ligand discrimination by T-cell receptors (TCRs) that uses endogenous TCR mechanosensation and substrate-mediated mechanical interactions with adhesive proteins (load sharing) to improve recognition fidelity. We leverage the catch bond behavior of cognate antigens to delay decision making and amplify TCR signaling while discarding noncognate slip bond ligands in the presence of a force. By integrating our model with existing structural and molecular data, we show that substrate mechanics regulates the transmission of active cytoskeletal forces through a molecular clutch and controls the energization of bound TCRs needed for optimal proofreading. Our work demonstrates how mechanical forces and substrate properties can augment kinetic proofreading in T-cells, suggesting biomaterial design strategies for immunotherapies that tune the mechanical microenvironment of T-cells to achieve high fidelity TCR-ligand discrimination, antigen recognition, and activation.

Natural filaments, such as proteins, plant tendrils, octopus tentacles, and elephant trunks, can transform into arbitrary three-dimensional shapes that carry out vital functions. Their shape-morphing behavior arises from intricate patterning of active and passive regions, which are difficult to replicate in synthetic matter. Here, we introduce a filament-centric strategy for programmable shape morphing in which intrinsic curvature and twist are directly encoded within multimaterial elastomeric filaments during fabrication. By harnessing rotational multimaterial 3D printing, we directly prescribe the filament’s natural curvature–twist field κ(s) through controlled material distribution and helical liquid crystal mesogen alignment. When heated above their nematic-to-isotropic transition temperature ( T _NI ), the helically aligned liquid crystal elastomer regions contract along their local director field, while passive regions remain essentially unchanged. This approach enables independent control of bending and torsion at every cross-section along the filament centerline: the principal natural curvatures of the filament along two orthogonal axes as well as the local twist. Next, we printed architected lattices composed of unit cells formed by sinusoidal filaments that either reversibly contract, expand, or exhibit out-of-plane deformations. Discrete elastic rod simulations of Janus filaments with different natural curvatures and twist, which are interconnected within the printed lattices, allow accurate prediction of their observed shape-morphing behavior. By integrating active–passive elastomers, additive manufacturing, and computational modeling, we have created shape-morphing matter with complex programmable responses for applications that rely on adaptive, robotic, or deployable architectures.

The collective construction of complex architectures by social insects involves the use of decentralized spatiotemporal signals in a dynamical environment that changes the behavior of the insects and is changed by them. Inspired by this original example of “stigmergy,” we show that a robot collective can nucleate, construct, and deconstruct aggregates via a trapping instability by simply altering their local transport behavior. We quantify these observations in terms of a two-dimensional phase space that encompasses agent-agent interaction and the agent-environment interaction (collection and deposition). Our results show how coordinated function can emerge spontaneously through simple local rules for sensing and action without the need for global representation, planning, or optimization/control algorithms, in sharp contrast with the traditional views of complex task execution. More broadly, our study shows that embodied collectives can leverage the environment as both a communication channel and a spatiotemporal memory, coupling organismal behavior to environmental remodeling leading to “exbodied intelligence.”