Becker Nils B, Mugler Andrew, Ten Wolde Pieter Rein
Bioquant, Universtität Heidelberg, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany.
Department of Physics, Purdue University, West Lafayette, Indiana 47907, USA.
Phys Rev Lett. 2015 Dec 18;115(25):258103. doi: 10.1103/PhysRevLett.115.258103. Epub 2015 Dec 17.
Living cells can enhance their fitness by anticipating environmental change. We study how accurately linear signaling networks in cells can predict future signals. We find that maximal predictive power results from a combination of input-noise suppression, linear extrapolation, and selective readout of correlated past signal values. Single-layer networks generate exponential response kernels, which suffice to predict Markovian signals optimally. Multilayer networks allow oscillatory kernels that can optimally predict non-Markovian signals. At low noise, these kernels exploit the signal derivative for extrapolation, while at high noise, they capitalize on signal values in the past that are strongly correlated with the future signal. We show how the common motifs of negative feedback and incoherent feed-forward can implement these optimal response functions. Simulations reveal that E. coli can reliably predict concentration changes for chemotaxis, and that the integration time of its response kernel arises from a trade-off between rapid response and noise suppression.
活细胞可以通过预测环境变化来提高自身的适应性。我们研究细胞中的线性信号网络能够多准确地预测未来信号。我们发现，最大预测能力源于输入噪声抑制、线性外推以及对相关过去信号值的选择性读出相结合。单层网络生成指数响应核，这足以最优地预测马尔可夫信号。多层网络允许存在振荡核，其能够最优地预测非马尔可夫信号。在低噪声情况下，这些核利用信号导数进行外推，而在高噪声情况下，它们利用与未来信号高度相关的过去信号值。我们展示了负反馈和非相干前馈的常见基序如何实现这些最优响应函数。模拟结果表明，大肠杆菌能够可靠地预测趋化作用中的浓度变化，并且其响应核的积分时间源于快速响应与噪声抑制之间的权衡。
