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Modularity of Signal Propagation Networks in the Thermodynamic Binding Network Model

Authors And Kaan Ata Yilmaz , David Soloveichik

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LIPIcs.DNA.32.10.pdf
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Subject Classification

ACM Subject Classification
  • Theory of computation → Models of computation
Keywords
  • Thermodynamic Binding Networks
  • Chemical Reaction Networks
  • Modular Composition
  • Verification
  • Simulation

Metrics

Abstract

The Thermodynamic Binding Network (TBN) model provides an equilibrium-based abstraction for molecular systems, but proving correctness of large networks built from individually verified modules remains difficult because modules can interact through shared binding-site types. We develop a method for proving correctness of modular TBNs by reducing a composition of arbitrarily many modules to a fixed coarse-grained TBN, computing the Hilbert basis of the reduced system, and lifting the resulting entropy bounds and polymer characterizations back to the original network. We apply this method to reversible signal-propagation modules: For the previously studied module implementing A+B ⟷ C, we show that stable configurations of an arbitrary network of such modules have the intended local structure, implying that firing an individual module preserves global stability. Under certain restrictions on the reaction set, these networks faithfully simulate a class of reversible chemical reaction networks: every bounded-length CRN execution can be realized by a height-2 path between stable TBN configurations, and any two stable TBN configurations map to CRN states in the same stoichiometric compatibility class. We further show that the same proof strategy extends to a novel module implementing reactions with two reactants and products like A+B ⟷ C+D.

Cite As Get BibTex

And Kaan Ata Yilmaz and David Soloveichik. Modularity of Signal Propagation Networks in the Thermodynamic Binding Network Model. In 32nd International Conference on DNA Computing and Molecular Programming (DNA 32). Leibniz International Proceedings in Informatics (LIPIcs), Volume 387, pp. 10:1-10:23, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026) https://doi.org/10.4230/LIPIcs.DNA.32.10

Author Details

And Kaan Ata Yilmaz
  • Electrical and Computer Engineering, University of Texas at Austin, TX, USA
David Soloveichik
  • Electrical and Computer Engineering, University of Texas at Austin, TX, USA

Funding

Schmidt Sciences Polymath Award to D.S., NSF SemiSynBio III: GOALI grant 2227578.

References

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