Theory of biochemical information processing with transients

Abstract

Cells in tissues and organisms operate in dynamic environments, continuously sensing and responding to time-varying chemical signals. In order to accurately interpret the complex information from their environment, biochemical networks in single cells actively process these extracellular signals in real-time. The current concept of biochemical computations places a strong focus on attractor based information processing in cells. Recent studies however have shown that cells generate completely opposite phenotypic responses depending upon frequency of the growth factor, independent of growth factor identity. This breaks down the steady-state description of biochemical information processing. Therefore, we propose to describe biochemical networks embedded in non-stationary environments as non-autonomous systems whose solutions are the dynamic input-dependent trajectories. We show that memory arising through metastable states will enable the system to integrate time-varying signals such that, inputs resulting in different phenotypic responses will be uniquely encoded in phase-space trajectories. The extracellular information of different phenotypes is spread throughout the large signaling networks and represented by characteristically different classes of phase-space trajectories. This encoded information will further be decoded downstream by early response genes (ERG) in real-time, where we show that the feed-forward structure of ERG is sufficient for this task.