In this section
@article{Ahmad:2025:10.1038/s41598-025-86854-7,
author = {Ahmad, Shaberi HS and Endres, RG and Matas-Gil, A and Kappassov, A},
doi = {10.1038/s41598-025-86854-7},
journal = {Scientific Reports},
title = {Optimal network sizes for most robust Turing patterns},
url = {http://dx.doi.org/10.1038/s41598-025-86854-7},
volume = {15},
year = {2025}
}
TY - JOUR
AB - Many cellular patterns exhibit a reaction-diffusion component, suggesting that Turing instability may contribute to pattern formation. However, biological gene-regulatory pathways are more complex than simple Turing activator-inhibitor models and generally do not require fine-tuning of parameters as dictated by the Turing conditions. To address these issues, we employ random matrix theory to analyze the Jacobian matrices of larger networks with robust statistical properties. Our analysis reveals that Turing patterns are more likely to occur by chance than previously thought and that the most robust Turing networks have an optimal size, consisting of only a handful of molecular species, thus significantly increasing their identifiability in biological systems. Broadly speaking, this optimal size emerges from a trade-off between the highest stability in small networks and the greatest instability with diffusion in large networks. Furthermore, we find that with multiple immobile nodes, differential diffusion ceases to be important for Turing patterns. Our findings may inform future synthetic biology approaches and provide insights into bridging the gap to complex developmental pathways.
AU - Ahmad,Shaberi HS
AU - Endres,RG
AU - Matas-Gil,A
AU - Kappassov,A
DO - 10.1038/s41598-025-86854-7
PY - 2025///
SN - 2045-2322
TI - Optimal network sizes for most robust Turing patterns
T2 - Scientific Reports
UR - http://dx.doi.org/10.1038/s41598-025-86854-7
VL - 15
ER -
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