论文标题
在旋转扩散的情况下，贝叶斯梯度传感
Bayesian gradient sensing in the presence of rotational diffusion
论文作者
论文摘要
生物细胞估计信号分子的浓度梯度，其精度不仅受到噪声的限制，而且还通过细胞自身的随机运动而受到限制。我们要求在存在运动和感应噪声的情况下梯度估计的理论限制。我们引入了平面中固定趋化剂的最小模型，但要经受旋转扩散，该模型使用贝叶斯估计来最佳地从嘈杂的浓度测量中推断出梯度方向。与通过时间比较进行的已知梯度传感的情况相反，我们表明，对于空间比较，梯度传感量表的最终精度不是旋转扩散时间，而是其平方根。为了达到这一精度，单个代理需要知道其自身的旋转扩散系数。该代理可以准确估计代理合奏中的预期变异性。但是，如果代理人没有解释其自身的运动噪声，则贝叶斯估计以特征性的方式失败。
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.
