English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Preprint

Non-asymptotic transients away from steady states determine cellular responsiveness to dynamic spatial-temporal signals

MPS-Authors
/persons/resource/persons238223

Nandan,  Akhilesh P.       
Lise Meitner Group Cellular Computations and Learning, Max Planck Institute for Neurobiology of Behavior – caesar, Max Planck Society;

/persons/resource/persons118347

Koseska,  Aneta       
Lise Meitner Group Cellular Computations and Learning, Max Planck Institute for Neurobiology of Behavior – caesar, Max Planck Society;

Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
Supplementary Material (public)
There is no public supplementary material available
Citation

Nandan, A. P., & Koseska, A. (2023). Non-asymptotic transients away from steady states determine cellular responsiveness to dynamic spatial-temporal signals. bioRxiv: the preprint server for biology, 526969. doi:10.1101/2023.02.03.526969.


Cite as: https://hdl.handle.net/21.11116/0000-000D-464C-0
Abstract
Majority of the theory on cell polarization and the understanding of cellular sensing and responsiveness to localized chemical cues has been based on the idea that non-polarized and polarized cell states can be represented by stable asymptotic switching between them. The existing model classes that describe the dynamics of signaling networks underlying polarization are formulated within the framework of autonomous systems. However these models do not simultaneously capture both, robust maintenance of polarized state longer than the signal duration, and retained responsiveness to novel signals. Using recent experimental evidence for criticality organization of biochemical networks, we challenge the current concepts and demonstrate that non-asymptotic signaling dynamics arising at criticality uniquely ensures optimal responsiveness to changing chemoattractant fields. We provide a framework to characterize non-asymptotic dynamics of system’s state trajectories through a non-autonomous treatment of the system, further emphasizing the importance of (long) transient dynamics, as well as the necessity to change the mathematical formalism when describing biological systems that operate in changing environments.