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Thermodynamic Binding Networks Capture Equilibrium Monomer-Polymer Thermodynamics

Authors And Kaan Ata Yilmaz , David Soloveichik , Austin Luchsinger

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LIPIcs.DNA.32.9.pdf
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Subject Classification

ACM Subject Classification
  • Theory of computation → Models of computation
Keywords
  • Thermodynamic Binding Networks
  • Thermodynamic Affinity Networks
  • Equilibrium Thermodynamics

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Abstract

Thermodynamic Binding Networks (TBNs) are a minimal model for molecular thermodynamics. Despite their simplicity, the stable behavior of these systems can be unexpectedly nontrivial. To better understand the expressive power of the model, we compare TBNs with Thermodynamic Affinity Networks (TANs), a partition-scoring formalism in which polymer-level favorability is specified directly, rather than derived from domain-level interactions. We show that these two models are thermodynamically equivalent at the level of stable configurations: every TBN can be represented directly by a TAN, and every TAN can be indirectly realized by a TBN whose stable configurations map exactly to the stable configurations of the TAN. The key idea in the latter direction is to use auxiliary monomers that allow the system to convert polymer-level free-energy rewards specified by the TAN into entropic gains from separate complexes in the TBN. This correspondence shows that the local rules of TBNs do not limit their equilibrium expressive power nearly as much as one might expect. In this minimum-free-energy sense of equilibrium, TBNs capture the coarse-grained monomer-polymer thermodynamics formalized by TANs.

Cite As Get BibTex

And Kaan Ata Yilmaz, David Soloveichik, and Austin Luchsinger. Thermodynamic Binding Networks Capture Equilibrium Monomer-Polymer Thermodynamics. In 32nd International Conference on DNA Computing and Molecular Programming (DNA 32). Leibniz International Proceedings in Informatics (LIPIcs), Volume 387, pp. 9:1-9:20, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2026) https://doi.org/10.4230/LIPIcs.DNA.32.9

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
Austin Luchsinger
  • Computer Science, University of Texas Rio Grande Valley, Edinburg, TX, USA

Funding

Schmidt Sciences Polymath Award to D.S., DOE grant DE-SC0024248, NSF SemiSynBio III: GOALI grant 2227578.

References

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