Information-geometric optimization of transient dynamics in gene regulatory networks
- 1School of Science and Technology, University of Camerino 1 , Camerino, Italy
- 2IIMAS, Universidad Nacional Autónoma de México 2 , Mexico City, Mexico
Abstract
The dynamics of gene regulatory networks (GRNs) is governed by the interaction between deterministic biochemical reactions and molecular noise. To understand how GRNs process information during cell state transitions, we study stochastic dynamics derived from a Boolean network model via its representation on the parameter space of Gaussian distributions, equipped with the Fisher information metric. Restricting our analysis to transitions towards a single attractor, this reformulation reveals that dynamics along the gradient flows of the Kullback–Leibler (KL) divergence requires isotropic decay rates across all nodes, and that the noise intensity quantitatively determines the potential differentiation between the initial and final states. Furthermore, we show that the Fisher geodesics, previously identified with minimal biological cost, would require the suppression of noise and lead to deterministic dynamics that lacks biological relevance. This suggests that some departure from minimal cost is necessary for cell differentiation. Our approach frames noise and decay rates as fundamental control parameters, providing a geometric principle for the analysis and design of synthetic networks.
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BibTeX
@article{bravetti2026informationgeometric,
title = {{Information-geometric optimization of transient dynamics in gene regulatory networks}},
author = {Alessandro Bravetti and Miguel Ángel García Ariza and Pablo Padilla and José Roberto Romero-Arias},
journal = {Royal Society Open Science},
year = {2026},
month = oct,
doi = {10.1098/rsos.261278},
url = {https://royalsocietypublishing.org/rsos/article/13/10/261278/483825/Information-geometric-optimization-of-transient},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Information-geometric optimization of transient dynamics in gene regulatory networks
AU - Alessandro Bravetti
AU - Miguel Ángel García Ariza
AU - Pablo Padilla
AU - José Roberto Romero-Arias
PY - 2026
DA - 2026/10/21
JO - Royal Society Open Science
DO - 10.1098/rsos.261278
UR - https://royalsocietypublishing.org/rsos/article/13/10/261278/483825/Information-geometric-optimization-of-transient
AB - The dynamics of gene regulatory networks (GRNs) is governed by the interaction between deterministic biochemical reactions and molecular noise. To understand how GRNs process information during cell state transitions, we study stochastic dynamics derived from a Boolean network model via its representation on the parameter space of Gaussian distributions, equipped with the Fisher information metric. Restricting our analysis to transitions towards a single attractor, this reformulation reveals that dynamics along the gradient flows of the Kullback–Leibler (KL) divergence requires isotropic decay rates across all nodes, and that the noise intensity quantitatively determines the potential differentiation between the initial and final states. Furthermore, we show that the Fisher geodesics, previously identified with minimal biological cost, would require the suppression of noise and lead to deterministic dynamics that lacks biological relevance. This suggests that some departure from minimal cost is necessary for cell differentiation. Our approach frames noise and decay rates as fundamental control parameters, providing a geometric principle for the analysis and design of synthetic networks.
SN - 2054-5703
ER -