Title: Trade-offs between cost and information in cellular prediction

Speaker: Pieter Rein ten Wolde (AMOLF)

Abstract:

Living cells can leverage correlations in environmental fluctuations to predict the future environment and mount a response ahead of time. To this end, cells need to encode the past signal into the output of the intracellular network from which the future input is predicted. Yet, storing information is costly, requiring protein copies and energy (chemical power). Moreover, not all features of the past signal are equally informative on the future input signal. Here, we show that cellular networks can reach the fundamental bound on the predictive information as set by the information extracted from the past signal. However, the bits of past information that are most informative about the future signal are also prohibitively costly. As a result, the optimal system that maximizes the predictive information for a given resource cost is, in general, not at the information bound. Applying our theory to the chemotaxis network of E.coli reveals that its response function is optimal for predicting future concentration changes over a broad range of background concentrations, and that the system has been tailored to predicting these changes in shallow gradients.