Effects of dose-response relationships on cellular information transmission
- 1University of Washington
Abstract
Cell signaling systems can transmit information with high precision, despite relying on complex reaction networks and inherently noisy biochemical reactions. To better understand how they achieve this precision, this work focuses on how cells can tune their quantitative steady-state dose-response relationships to improve signaling fidelity. Using a combination of analytic and numerical approaches, we quantify the maximum information that can be transmitted in single-step and multi-step signaling systems under several biologically realistic noise distributions: Gaussian, gamma, constant coeffcient of variation, and binomial. We find that signaling systems transmit more information if their dose-response curves have low baselines, large dynamic ranges, and curve shapes that are well matched to the natural distribution of input signals. For multi-step systems, linear transfer functions typically improve signaling fidelity for all noise levels, consistent with previous work on "dose-response alignment". We also find that switchlike transfer functions transmit a maximum of one bit and are robust to low levels of noise, but fail rapidly with high levels of noise. Simulations of toy signaling systems corroborate these results. Together, these findings provide insight into some cellular design principles that have arisen through evolution and that are widely observed in nature.
Related
- Estimating dose-response relationships for vitamin D with coronary heart disease, stroke, and all-cause mortality: observational and Mendelian randomisation analyses
- Soluble pathogenic tau transmission to astrocytes drives acute oxidative damage, cellular senescence, and neurovascular uncoupling in a model of Alzheimers tauopathy
- Social relationships promote access to food and information in wild jackdaws.
- Predictions for and lack of maximal information transmission in the neuromuscular junction
- Peer Transmission of Mental Health Information Increases ADHD Self-Diagnosis
BibTeX
@misc{andrews2026effects,
title = {{Effects of dose-response relationships on cellular information transmission}},
author = {S. S. Andrews and H. S. Wiley and R. Brent and H. M. Sauro},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.26.761777},
url = {https://www.biorxiv.org/content/10.64898/2026.10.26.761777v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Effects of dose-response relationships on cellular information transmission
AU - S. S. Andrews
AU - H. S. Wiley
AU - R. Brent
AU - H. M. Sauro
PY - 2026
DA - 2026/10/26
PB - bioRxiv
DO - 10.64898/2026.10.26.761777
UR - https://www.biorxiv.org/content/10.64898/2026.10.26.761777v1
AB - Cell signaling systems can transmit information with high precision, despite relying on complex reaction networks and inherently noisy biochemical reactions. To better understand how they achieve this precision, this work focuses on how cells can tune their quantitative steady-state dose-response relationships to improve signaling fidelity. Using a combination of analytic and numerical approaches, we quantify the maximum information that can be transmitted in single-step and multi-step signaling systems under several biologically realistic noise distributions: Gaussian, gamma, constant coeffcient of variation, and binomial. We find that signaling systems transmit more information if their dose-response curves have low baselines, large dynamic ranges, and curve shapes that are well matched to the natural distribution of input signals. For multi-step systems, linear transfer functions typically improve signaling fidelity for all noise levels, consistent with previous work on "dose-response alignment". We also find that switchlike transfer functions transmit a maximum of one bit and are robust to low levels of noise, but fail rapidly with high levels of noise. Simulations of toy signaling systems corroborate these results. Together, these findings provide insight into some cellular design principles that have arisen through evolution and that are widely observed in nature.
ER -