Embedded Files
How Cells Sense and Shape Curvature
Living cells are not simple bags of molecules. Their membranes bend and fold into a rich landscape of shapes, and cells actively read this geometry, using the curvature of a surface as a signal to decide where molecules should gather and when processes should begin. But this raises a puzzle of scale: how can a protein only nanometers in size perceive a gentle curve thousands of times larger than itself? We study this question through septins, a family of proteins that recognize curvature at the scale of the whole cell. Combining live microscopy with mathematical modeling, we found that the ability to sense curvature is not built into any single protein. Instead, it emerges collectively, from the way many septins assemble together on the membrane, step by step. Even more surprisingly, their sense of curvature is not fixed but shifts depending on the surrounding context. This reveals a flexible strategy by which cells can deploy the very same molecule for different jobs in different places, and it offers a broader lesson in how sophisticated behavior can arise from simple parts acting together.
Flow and Transport at Biological Interfaces
The surfaces that define a living cell are not solid walls. The membranes wrapping a cell and its compartments behave like thin, two-dimensional fluids, and the proteins and filaments embedded in them are constantly on the move: drifting, rotating, and being carried along by the flow. How things move through this crowded, curved, fluid sheet sets how proteins find one another and organize into the structures a cell needs. Yet the physics here departs sharply from the familiar rules that govern motion in open three-dimensional space. We build microhydrodynamics theory to predict how a rod-like protein or filament experiences drag as it moves within a membrane, accounting for the membrane's curvature, its two coupled layers, the friction from whatever supports it, and the surrounding fluids above and below. We found that curvature does not slow a filament uniformly: it reshapes the resistance to sliding lengthwise, moving sideways, and spinning each in its own distinct way, so that the geometry of a membrane leaves a directional fingerprint on how its contents move. Together, this work provides the basic rules of motion for the fluid, membrane-bound world in which much of life takes place.
Breaking Symmetry: How Life Builds Chirality Across Scales
Most animals look outwardly mirror-symmetric, yet inside, the heart, gut, and brain settle consistently to one side. This left-right bias begins astonishingly early, in the handedness of single cells, whose internal actin fibres spontaneously swirl with a preferred chirality. How does that microscopic handedness climb upward to organize a whole tissue? Here we confine cells to adhesive islands, forming simple two-dimensional microtissues, and study how their individual handedness becomes a collective pattern. By reading the chiral behavior of cells in different positions and shapes, we build models linking single-cell handedness to tissue-wide alignment. The finding is that order does not begin in the interior: it starts at the edge. Cells along the boundary tilt against the island's rim, and this alignment propagates inward, an outside-in rule that predicts the patterns real tissues form. Strikingly, the handedness of the whole is not dictated by the handedness of its parts alone. The size and shape of the tissue, even its aspect ratio, can strengthen, weaken, or redirect the collective chirality. Chirality, like so much in biology, is a law that emerges anew at each scale rather than one inherited intact from below.
Machinery of Cell Motion: Inferring Hidden Mechanics from Natural Fluctuations
When a cell crawls, it leads with a thin, restless sheet called the lamellipodium, a region that surges forward and pauses in constant negotiation with its surroundings. Inside lies a dense, self-building scaffold of actin filaments, branched by a molecular machine called Arp2/3, that pushes the membrane ahead. For decades this machinery was studied by poking it, disabling one protein at a time, but the parts are too tangled and redundant to reveal how they truly drive one another. Here the approach is inverted: rather than perturbing the cell, we listen to the natural, moment-to-moment jitter of its proteins in an undisturbed state. The central challenge is that such small fluctuations only ever expose the system's behavior close to steady-state, where everything looks linear. The contribution of this work is to show how a full nonlinear mechanistic model, complete with reaction rates, mechanical forces, and feedback loops, can be reconstructed from those linear measurements alone, by combining them with physical reasoning and parsimony. The recovered model reveals the lamellipodium as a damped oscillator, and shows that a system's deep, nonlinear rules can be read from the faint fluctuations it produces on its own.
Forces of Mitosis: How Cells Pull Themselves Apart
Every time a cell divides, it must split its chromosomes with near-perfect fidelity, a mistake here can be catastrophic. The machine that does this, the mitotic spindle, assembles itself in minutes from stiff protein filaments and molecular motors, then hauls two full sets of chromosomes to opposite ends of the cell. The forces driving this are formidable, yet they act deep inside a crowded, living cell where they cannot be measured directly. So how do we weigh forces we cannot touch? Here we watch, in three dimensions, the rapid dance of chromosomes and spindle poles during the spindle's most chaotic phase, its initial assembly, and infer the forces from the motion itself. Reconstructing this hidden mechanics reveals a clear hierarchy. Chromosomes ignore one another, feeling only the poles, which repel them when near and gently gather them when far. And the dominant push that drives the two poles apart comes not from the cell's outer cortex pulling outward, but from motors working between the poles, from within. The spindle, it turns out, largely builds itself apart.
Laws of Randomness: Machine Learning of Stochastic Dynamics
Page updated
Google Sites
Report abuse