We map a class of well-mixed stochastic models of biochemical feedback in steady state to the mean-field Ising model near the critical point. The mapping provides an effective temperature, magnetic field, order parameter, and heat capacity that can be extracted from biological data without fitting or knowledge of the underlying molecular details. We demonstrate this procedure on fluorescence data from mouse T cells, which reveals distinctions between how the cells respond to different drugs. We also show that the heat capacity allows inference of absolute molecule number from fluorescence intensity. We explain this result in terms of the underlying fluctuations and demonstrate the generality of our work.

Cells use biochemical networks to translate environmental information into intracellular responses. These responses can be highly dynamic, but how the information is encoded in these dynamics remains poorly understood. Here, we investigate the dynamic encoding of information in the ATP-induced calcium responses of fibroblast cells, using a vectorial, or multi-time-point, measure from information theory. We find that the amount of extracted information depends on physiological constraints such as the sampling rate and memory capacity of the downstream network, and it is affected differentially by intrinsic versus extrinsic noise. By comparing to a minimal physical model, we find, surprisingly, that the information is often insensitive to the detailed structure of the underlying dynamics, and instead the decoding mechanism acts as a simple lowpass filter. These results demonstrate the mechanisms and limitations of dynamic information storage in cells.

We present a detailed study of scattering by an amplitude-modulated potential barrier using three distinct physical frameworks: quantum, classical, and semiclassical. Classical physics gives bounds on the energy and momentum of the scattered particle, while also providing the foundation for semiclassical theory. We use the semiclassical approach to selectively add quantum-mechanical effects such as interference and diffraction. We find good agreement between the quantum and semiclassical momentum distributions. Our methods and results can be used to understand quantum and classical aspects of transport mechanisms involving time-varying potentials, such as quantum pumping.

Collective sensing by interacting cells is observed in a variety of biological systems, and yet, a quantitative understanding of how sensory information is collectively encoded is lacking. Here, we investigate the ATP-induced calcium dynamics of monolayers of fibroblast cells that communicate via gap junctions. Combining experiments and stochastic modeling, we find that increasing the ATP stimulus increases the propensity for calcium oscillations, despite large cell-to-cell variability. The model further predicts that the oscillation propensity increases with not only the stimulus, but also the cell density due to increased communication. Experiments confirm this prediction, showing that cell density modulates the collective sensory response. We further implicate cell-cell communication by coculturing the fibroblasts with cancer cells, which we show act as "defects" in the communication network, thereby reducing the oscillation propensity. These results suggest that multicellular networks sit at a point in parameter space where cell-cell communication has a significant effect on the sensory response, allowing cells to simultaneously respond to a sensory input and the presence of neighbors.cell-cell communication | calcium oscillations | gap junctions | cellular sensing | collective behavior D ecoding the cellular response to environmental perturbations, such as chemosensing, photosensing, and mechanosensing, has been of central importance in our understanding of living systems. To date, most studies of cellular sensation and response have focused on single isolated cells or population averages. An emerging picture from these studies is the set of design principles governing cellular signaling pathways: these pathways are organized into an intertwined, often redundant network with architecture that is closely related to the robustness of cellular information processing (1, 2). However, many examples suggest that collective sensing by many interacting cells may provide another dimension for the cells to process environmental cues (3). Examples, such as quorum sensing in bacterial colonies (4), olfaction in insects (5) and mammals (6), glucose response in the pancreatic islet (7), and the visual processing of retinal ganglion cells (8), suggest a fundamental need to revisit cellular information processing in the context of multicellular sensation and response, because even weak cell-to-cell interaction may have strong impact on the states of multicellular network dynamics (9). In particular, we seek to examine how the sensory response of cells in a population differs from that of isolated cells and whether we can tune between these two extremes by controlling the degree of cell-cell communication.Previously, we described the spatial-temporal dynamics of collective chemosensing of a mammalian cell model system (10, 11). In this system, high-density mouse fibroblast cells (NIH 3T3) form a monolayer that allows nearest neighbor communications through gap junctions (12). When extracellular ATP is delivered to the monol...