Pathway Reweighting in DNA Reaction Networks Enables Adaptation to Environmental Pressures
- 1Life-Like Materials and Systems, University of Mainz, Duesbergweg 10-14, Mainz, Germany, 55128
- 2Institute of Human Genetics, University Medical Center, Duesbergweg 6, Mainz, Germany, 55128
Abstract
Adaptive biochemical reaction networks must respond to environmental changes while maintaining function, yet synthetic networks typically rely on predefined pathways and stimulus–response relationships. Here, we introduce pathway reweighting as a strategy for adaptivity in DNA strand-displacement reaction networks. We design a splitting network in which two competing pathways connect a shared initial state to a common output. This allows environmental perturbations to redistribute reaction flux between the pathways while preserving overall network function. We showcase environmental sensitivity by integrating pHresponsive i-motif and ATP-binding aptamer motifs that enable selective and proportional adaptation. Combining these pressures demonstrates pathway reweighting under more complex, biologically relevant environmental conditions. Beyond proportional adaptation, we introduce a distinct, history-dependent mechanism based on an enzymatic feed-forward loop. The loop progressively modifies the Input strand, creating delayed pathway reweighting and encoding a memory of prior environmental exposure. A deep-learning reaction-network framework resolves the resulting interconnected dynamics and identifies changes in pathway utilization without requiring complete mechanistic parameterization. Our experimentalnumerical framework enables the rational design of synthetic CRNs whose adaptive behavior emerges from network organization rather than explicit reaction-by-reaction programming.
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BibTeX
@misc{duzs2026pathway,
title = {{Pathway Reweighting in DNA Reaction Networks Enables Adaptation to Environmental Pressures}},
author = {Brigitta Dúzs and Nicolò Alagna and Livia Lehmann and Alexander Haehnel and Susanne Gerber and Andreas Walther},
howpublished = {ChemRxiv},
year = {2026},
month = oct,
doi = {10.26434/chemrxiv.15010271/v1},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010271/v1},
note = {CC BY-NC-ND},
}
RIS
TY - UNPB
TI - Pathway Reweighting in DNA Reaction Networks Enables Adaptation to Environmental Pressures
AU - Brigitta Dúzs
AU - Nicolò Alagna
AU - Livia Lehmann
AU - Alexander Haehnel
AU - Susanne Gerber
AU - Andreas Walther
PY - 2026
DA - 2026/10/12
PB - ChemRxiv
DO - 10.26434/chemrxiv.15010271/v1
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15010271/v1
AB - Adaptive biochemical reaction networks must respond to environmental changes while maintaining function, yet synthetic networks typically rely on predefined pathways and stimulus–response relationships. Here, we introduce pathway reweighting as a strategy for adaptivity in DNA strand-displacement reaction networks. We design a splitting network in which two competing pathways connect a shared initial state to a common output. This allows environmental perturbations to redistribute reaction flux between the pathways while preserving overall network function. We showcase environmental sensitivity by integrating pHresponsive i-motif and ATP-binding aptamer motifs that enable selective and proportional adaptation. Combining these pressures demonstrates pathway reweighting under more complex, biologically relevant environmental conditions. Beyond proportional adaptation, we introduce a distinct, history-dependent mechanism based on an enzymatic feed-forward loop. The loop progressively modifies the Input strand, creating delayed pathway reweighting and encoding a memory of prior environmental exposure. A deep-learning reaction-network framework resolves the resulting interconnected dynamics and identifies changes in pathway utilization without requiring complete mechanistic parameterization. Our experimentalnumerical framework enables the rational design of synthetic CRNs whose adaptive behavior emerges from network organization rather than explicit reaction-by-reaction programming.
ER -