Periodic Reporting for period 1 - OCP (Optimal Cellular Prediction)
Reporting period: 2021-04-01 to 2022-09-30
Living cells need to respond to changes in their environment. Yet, mounting a response takes time.Remarkably recent experiments have shown that living cells can detect correlations in the environmental fluctuations to anticipate changes in the environment and mount a response ahead of time. How living cells can predict future signals, how reliably they can do so, and what the fitness benefits of anticipation are, are question that are not understood. In this program, theory and experiments will be employed to address these questions.
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.
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.