旋转扩散条件下的贝叶斯梯度传感
Bayesian gradient sensing in the presence of rotational diffusion
摘要：
生物细胞估计信号分子的浓度梯度，其精度不仅受到感知噪声的限制，而且还受到细胞自身随机运动的限制。我们要求在运动和感知噪声的存在下的梯度估计的理论极限。我们引入了一个极小模型的一个固定的趋化剂在平面上旋转扩散，它使用贝叶斯估计，以最佳的推断从嘈杂的浓度测量的梯度方向。相反，已知的情况下，梯度传感的时间比较，我们表明，对于空间比较，梯度传感尺度的最终精度不与旋转扩散时间，但与其平方根。为了达到这种精度，单个代理需要知道自己的旋转扩散系数。该代理可以准确地估计代理集合内的预期可变性。然而，如果一个代理，不考虑自己的运动噪声，贝叶斯估计失败的特征。
英文摘要：
Biological cells estimate concentration gradients of signaling molecules with a precision that is limited not only by sensing noise, but additionally by the cell's own stochastic motion. We ask for the theoretical limits of gradient estimation in the presence of both motility and sensing noise. We introduce a minimal model of a stationary chemotactic agent in the plane subject to rotational diffusion, which uses Bayesian estimation to optimally infer a gradient direction from noisy concentration measurements. Contrary to the known case of gradient sensing by temporal comparison, we show that for spatial comparison, the ultimate precision of gradient sensing scales not with the rotational diffusion time, but with its square-root. To achieve this precision, an individual agent needs to know its own rotational diffusion coefficient. This agent can accurately estimate the expected variability within an ensemble of agents. If an agent, however, does not account for its own motility noise, Bayesian estimation fails in a characteristic manner.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
