This CAREER award supports theoretical and computational research, and associated education to investigate how protein molecules can tell the shape of a cell membrane. For cells to divide or make protrusions, proteins must end up in the right position in the cell – for instance, being localized to parts of the membrane that have particular curved shapes. Surprisingly, this can happen even when the scale over which the membranes curve is far larger, some thousand times larger, than the size of a protein. Because membranes are soft, and can change their shape easily, they also can be very rough, making the problem of the proteins "sensing" the cell’s shape even harder. This project is about understanding how shape sensing can happen. Two broad possibilities for how proteins end up in these spots are either: 1) each single protein can make incredibly precise measurements of the local membrane, allowing it to find the right shape, or 2) many proteins can work together cooperatively to measure the cell’s shape over a length scale of many protein sizes. How precisely must single proteins measure the membrane’s shape in order to locate where they go? Could proteins instead position themselves based on something correlated with shape but easier to measure? If proteins work together to sense the membrane’s shape, how much better will they be at finding their locations? The PI will use computer models, mathematical calculations, and experimental data shared from collaborators to address these questions. This research will also provide a way to better understand how patterning is coupled to surface shape in a broader context; similar problems show up both in biology and in the formation of patterns on other rough surfaces.
This project includes linking computational research to an education plan including development of a new class focused on the physics of cells, as well as mentoring and training students from high school to graduate school. People supported by this grant will work in collaboration with Baltimore City public high school students, with Baltimore City students within the group as well as group members working with the public high school. In particular, the PI and graduate student will work with local high school teachers to develop computational “labs†to teach high school students about the role of randomness in physics and biology and assist in presenting these labs.
This CAREER award supports theoretical and computational research, and associated education to investigate how nanometer-scale proteins sense micron-scale curvature of the cell membrane. Curvature sensing is a critical soft matter physics problem, but the guiding principles of how it operates at the micron scale are still unclear. The PI aims to develop useful predictive bounds showing the limiting factors in shape sensing. If successful, this research will lead to a broad understanding of how biochemical polarity and cell geometry are coupled by using both minimal phenomenological models and detailed reaction-diffusion approaches. Interesting connections at the interfaces of membrane dynamics, statistical sensing limits like the Berg-Purcell limit, and biochemical models, will be developed with the aim to gain new insight into of the organization and dynamics of cell membranes and related biological systems. This insight will be used in building predictive models.
This research will be pursued along two primary directions:
1. Determining the accuracy with which single proteins can sense membrane curvature. Even if a protein could perfectly measure the local membrane shape, it could not precisely determine the micron-scale membrane curvature, because thermal fluctuations create local curvature. Distinguishing curved membrane regions from flat is harder at larger radii of curvature – the signal-to-noise ratio decreases. Are proteins that sense micron-scale curvature near this basic physical limit? To determine this, the research group will apply estimation theory to continuum models of fluctuating membranes in and out of confinement. Protein binding may depend on proxies for membrane curvature, for example local defects in lipid packing. These effects, as well as lipid tilt and non-thermal origins of membrane fluctuations will also be studied. In combination, these models predict how curvature-dependent binding depends on membrane tension, bending modulus, the presence of a solid support, and lipid asymmetries between leaflets.
2. Emergent shape sensing by pattern formation on a fluctuating membrane. Initial simulations suggest simple bistable reactions on a membrane surface can reproduce the shape sensing observed in C. elegans embryos, where proteins localize to narrow ends of a cell. However, cell membranes are highly dynamic – fluctuating and undergoing lipid flow. Will these shape changes and flows prevent cells from sensing their own shape or help them? The PI will begin by developing and testing an energy landscape model of shape sensing, to describe how protein localization depends on membrane shape. Simulations of reaction-diffusion dynamics on fluctuating membranes will be carried out in order to determine when shape fluctuations can help or hinder shape sensing. These models will predict the extent to which the cell's patterning depends on membrane-cortex attachment, cytosol viscosity, and other factors known to modulate active membrane fluctuations.
This project includes linking computational research to an education plan including development of a new class focused on the physics of cells, as well as mentoring and training students from high school to graduate school. People supported by this grant will work in collaboration with Baltimore City public high school students, with Baltimore City students within the group as well as group members working with the public high school. In particular, the PI and graduate student will work with local high school teachers to develop computational “labs†to teach high school students about the role of randomness in physics and biology and assist in presenting these labs.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
