FOM Institute AMOLF, Science Park 104, 1098 XG Amsterdam, Netherlands.
Phys Rev Lett. 2012 Nov 21;109(21):218103. doi: 10.1103/PhysRevLett.109.218103. Epub 2012 Nov 20.
Living cells often need to extract information from biochemical signals that are noisy. We study how accurately cells can measure chemical concentrations with signaling networks that are linear. For stationary signals of long duration, they can reach, but not beat, the Berg-Purcell limit, which relies on uniformly averaging in time the fluctuations in the input signal. For short times or nonstationary signals, however, they can beat the Berg-Purcell limit, by nonuniformly time averaging the input. We derive the optimal weighting function for time averaging and use it to provide the fundamental limit of measuring chemical concentrations with linear signaling networks.
活细胞经常需要从嘈杂的生化信号中提取信息。我们研究了线性信号网络如何准确地测量化学浓度。对于长时间的静态信号，它们可以达到，但不能超过 Berg-Purcell 极限，该极限依赖于对输入信号波动的时间均匀平均。然而，对于短时间或非稳态信号，通过对输入信号的非均匀时间平均，可以超过 Berg-Purcell 极限。我们推导出时间平均的最优加权函数，并使用它提供了用线性信号网络测量化学浓度的基本极限。
