Optimal Cellular Prediction

ongoing

The ability to respond to changes in the environment is a defining property of life. Strikingly, experiments have vividly demonstrated that even single-celled organisms can leverage correlations in the environmental fluctuations to predict the future environment and mount a response ahead of time. Yet, how reliably single cells can do so and what the fitness benefit of prediction is, are wide-open questions. To predict the future environment, cells need to compress the past signal into the dynamics of the intracellular biochemical network from which the future input is inferred. Yet, storing information is costly while not all features of the past signal are equally informative on the future input signal. Using measures from information theory and ideas from statistical physics, we will study network motifs and environmental stimuli of increasing complexity to derive the fundamental limit to the prediction accuracy as set by the information on the past. We will determine how close biochemical networks can come to this bound, and how this depends on the topology of the network and the resources to build and operate it – protein copies, time, and energy. We will elucidate how the features of the past signal that are most informative about the future signal are encoded in these optimal networks, and how the cell decodes these. The studies on these minimal model systems will uncover general principles of cellular prediction. We will use our theoretical framework to set up experiments that allow us to test whether two specific biological systems – the E. coli chemotaxis system and the glucose sensing system of yeast – have implemented the uncovered design principles for optimal cellular prediction. We will measure how close these systems come to the fundamental bound on the prediction precision and how this constrains their fitness. We envision that this program will establish information transmission efficiency as a paradigm for understanding cellular function.