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Optimal Prediction by Cellular Signaling Networks
2015
Physical Review Letters
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
doi:10.1103/physrevlett.115.258103
pmid:26722947
fatcat:5kpmnfklq5fnjbki2knqeapqoq