Organization at criticality enables processing of time‐varying signals by receptor networks

Abstract

How cells utilize surface receptors for chemoreception is a recurrent question spanning between physics and biology over the past few decades. However, the dynamical mechanism for processing time‐varying signals is still unclear. Using dynamical systems formalism to describe criticality in non‐equilibrium systems, we propose generic principle for temporal information processing through phase space trajectories using dynamic transient memory. In contrast to short‐term memory, dynamic memory generated via “ghost” attractor enables signal integration depending on stimulus history and thereby uniquely promotes integrating and interpreting complex temporal growth factor signals. We argue that this is a generic feature of receptor networks, the first layer of the cell that senses the changing environment. Using the experimentally established epidermal growth factor sensing system, we propose how recycling could provide self‐organized maintenance of the critical receptor concentration at the plasma membrane through a simple, fluctuation‐sensing mechanism. Processing of non‐stationary signals, a feature previously attributed only to neural networks, thus uniquely emerges for receptor networks organized at criticality.

Cells use molecular working memory to navigate in changing chemoattractant fields

“…Necessary for manifestation of the ‘ghost’ state is organization at criticality, before the . We have previously examined both theoretically and experimentally, the response of receptor networks under uniform growth factor stimulation and determined that the concentration of receptors on the cell membrane regulate the organization of the system at criticality ( Stanoev et al, 2018 ; Stanoev et al, 2020 ). The features of both bifurcations, cell polarization under spatial cues and a transient memory of this polarization in absence of the cue, will be unified for a sub-critical , as it is stabilized via s. We thus propose that organization at criticality - in the vicinity of a (gray shaded area in Figure 1—figure supplement 1A ; details discussed in Materials and methods), renders a minimal mechanism for cellular responsiveness in changing environments.…”

“…The memory in polarized signaling was also reflected on the level of the cell morphology, as shown by the difference of normalized cell protrusion area in the front and the back of the cell over time ( Figure 1G ). Plotting the trajectory that describes the change of the state of the system over time (state-space trajectory, Figure 1F bottom) shows that the temporal memory in EGFR phosphorylation polarization is established due to transient trapping of the signaling state trajectory in state-space, a property of the metastable ‘ghost’ state ( Stanoev et al, 2020 ; Strogatz, 2018 ) through which the system is maintained away from the steady state. The simulations show that there are two characteristic time-scales present in the system: slow evolution of the system’s dynamics in the ‘ghost’ state due to the trapping, and fast transitions between the steady states ( Figure 1—video 1 ).…”

Cells use molecular working memory to navigate in changing chemoattractant fields

“…In our previous work we identified that when a saddle-node bifurcation ( SN ) and thereby a steady-state is lost in a dynamical transition, i.e. upon signal removal, a remnant or a dynamical ”ghost” of the stable attractor serves as a mechanism for sensing timevarying growth factors in biochemical receptor networks (Stanoev et al, 2018; Stanoev et al, 2020). Necessary for manifestation of the ”ghost” state is organization at criticality, which in the networks we previously examined was determined by the concentration of receptors on the cell membrane.…”

“…Plotting the trajectory that describes the change of the state of the system over time (state-space trajectory, Figure 1F bottom, movie S1) shows that the temporal memory in EGFR phosphorylation polarization is established due to transient trapping of the signaling state trajectory in state-space. This is typical for the emergence of metastable "ghost" states (Stanoev et al, 2020;Strogatz, 2018), indicating that the system is maintained away from steady-states. The trapping in the dynamically-metastable memory state does not hinder sensing of and adapting to subsequent signals.…”

“…These models rely on computations with stable states, where switching from the attractor of basal-to the attractor of polarized-signaling activity enables noise-robust sensing, or establishing a long-term memory of previous signal localization. However, they are less 2 suited for real-time computation of signals that vary in time and space, since the stable attractors completely hinder or at least significantly delay the responsiveness to newly encountered signals (Stanoev et al, 2020). Thus, the mechanism that underlies robust cellular navigation in changing chemical fields has remained unknown.…”