Bayes-Optimal Chemotaxis
Bayes-Optimal Chemotaxis
复制标题
贝叶斯-最佳趋化性
作者：
D. Mortimer;P. Dayan;K. Burrage;G. Goodhill
趋化性在许多生物过程中起着至关重要的作用，包括神经系统发育。然而，基本的物理约束限制了诸如细胞或生长锥的小传感装置检测外部化学梯度的能力。其中之一是受体结合的随机性，导致细胞受体阵列中的结合模式不断波动。这类似于大脑在系统层面上经常遇到的感觉信息的不确定性。在这里，我们推导出分析贝叶斯最优策略相结合的信息从一个和两个维度的受体的空间阵列，以确定梯度方向。我们还展示了如何从一个以上的受体物种的信息可以最佳地整合，推导出梯度形状是最佳的引导细胞或生长锥在最长的距离，并说明极化细胞的行为可能会出现适应缓慢变化的环境。我们的结果一起提供了封闭形式的预测在很宽的梯度条件下的趋化性能的变化。
Chemotaxis plays a crucial role in many biological processes, including nervous system development. However, fundamental physical constraints limit the ability of a small sensing device such as a cell or growth cone to detect an external chemical gradient. One of these is the stochastic nature of receptor binding, leading to a constantly fluctuating binding pattern across the cell's array of receptors. This is analogous to the uncertainty in sensory information often encountered by the brain at the systems level. Here we derive analytically the Bayes-optimal strategy for combining information from a spatial array of receptors in both one and two dimensions to determine gradient direction. We also show how information from more than one receptor species can be optimally integrated, derive the gradient shapes that are optimal for guiding cells or growth cones over the longest possible distances, and illustrate that polarized cell behavior might arise as an adaptation to slowly varying environments. Together our results provide closed-form predictions for variations in chemotactic performance over a wide range of gradient conditions.
DOI：
10.1073/pnas.0601909103
发表时间：
2006-08-01
影响因子：
11.1
作者：
Samadani, Azadeh;Mettetal, Jerome;van Oudenaarden, Alexander
通讯作者：
van Oudenaarden, Alexander
DOI：
10.1073/pnas.0504321102
发表时间：
2005-07-19
影响因子：
11.1
作者：
Bialek, W;Setayeshgar, S
通讯作者：
Setayeshgar, S