Integrated Calendar

Mechanical cues from the extracellular microenvironment play a central role in regulating the structure, function and fate of living cells. Nevertheless, the precise nature of the mechanisms and processes underlying this crucial cellular mechanosensitivity remains a fundamental open problem. Here we provide a novel framework for addressing cellular sensitivity and response to external forces by experimentally and theoretically studying one of its most striking manifestations – cell reorientation to a uniform angle in response to cyclic stretching. We first report on extensive new experiments of adherent cell reorientation under cyclic stretching and show that they cannot be quantitatively explained by existing models. We then propose a new theory which focuses on the cell’s stored elastic energy, corresponding to a 2D anisotropic linear elastic material. The variation of this energy with the cell’s orientation is shown to drive the dissipative reorientation process. Our theory is in excellent quantitative agreement with the complete temporal reorientation dynamics of individual cells, measured over a wide range of experimental conditions, thus elucidating a basic aspect of mechanosensitivity. It, moreover offers new venues for predicting and controlling cell behavior in response to mechanical cues from the microenvironment.

Since the discovery of place cells in the hippocampus, a variety of experimental observations have pointed to the complexity of hippocampal circuit dynamics and their importance in memory related tasks. During spatial navigation, place cell activity predicts the upcoming animal location within the short time scale of individual cycles of theta oscillations. Sudden changes of the spatial context are followed by a bistability between population coding of past and current context, paced by the theta rhythm. During immobility, brief sequences of place cell activation encode spatial trajectories, which have been linked to learning in spatial memory tasks and goal-directed navigation. Finally, when the animal is engaged in a delayed memory task, hippocampal cells fire at specific time intervals within the delay period and the activity of a population of cells is predictive of the behavioral outcome. I will present a unified attractor network model that accounts for this wide range of experimental observations. A critical component of the model is the use of realistic synapses that exhibit short-term plasticity driven by presynaptic activity. Complexity in the network dynamics emerges due to the effect of history dependent synaptic states on the network activity. Model predictions, possible extensions of the model and its relationship to dynamics observed in other cortical areas will be discussed.