Thermodynamically Driven Signal Amplification

Authors Joshua Petrack , David Soloveichik , David Doty



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Author Details

Joshua Petrack
  • University of California-Davis, CA, USA
David Soloveichik
  • University of Texas at Austin, TX, USA
David Doty
  • University of California-Davis, CA, USA

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Joshua Petrack, David Soloveichik, and David Doty. Thermodynamically Driven Signal Amplification. In 29th International Conference on DNA Computing and Molecular Programming (DNA 29). Leibniz International Proceedings in Informatics (LIPIcs), Volume 276, pp. 8:1-8:22, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023) https://doi.org/10.4230/LIPIcs.DNA.29.8

Abstract

The field of chemical computation attempts to model computational behavior that arises when molecules, typically nucleic acids, are mixed together. By modeling this physical phenomenon at different levels of specificity, different operative computational behavior is observed. Thermodynamic binding networks (TBNs) is a highly abstracted model that focuses on which molecules are bound to each other in a "thermodynamically stable" sense. Stability is measured based only on how many bonds are formed and how many total complexes are in a configuration, without focusing on how molecules are binding or how they became bound. By defocusing on kinetic processes, TBNs attempt to naturally model the long-term behavior of a mixture (i.e., its thermodynamic equilibrium). 
We study the problem of signal amplification: detecting a small quantity of some molecule and amplifying its signal to something more easily detectable. This problem has natural applications such as disease diagnosis. By focusing on thermodynamically favored outcomes, we seek to design chemical systems that perform the task of signal amplification robustly without relying on kinetic pathways that can be error prone and require highly controlled conditions (e.g., PCR amplification).
It might appear that a small change in concentrations can result in only small changes to the thermodynamic equilibrium of a molecular system. However, we show that it is possible to design a TBN that can "exponentially amplify" a signal represented by a single copy of a monomer called the analyte: this TBN has exactly one stable state before adding the analyte and exactly one stable state afterward, and those two states "look very different" from each other. In particular, their difference is exponential in the number of types of molecules and their sizes. The system can be programmed to any desired level of resilience to false positives and false negatives. To prove these results, we introduce new concepts to the TBN model, particularly the notions of a TBN’s entropy gap to describe how unlikely it is to be observed in an undesirable state, and feed-forward TBNs that have a strong upper bound on the number of polymers in a stable configuration.
We also show a corresponding negative result: a doubly exponential upper bound, meaning that there is no TBN that can amplify a signal by an amount more than doubly exponential in the number and sizes of different molecules that comprise it. We leave as an open question to close this gap by either proving an exponential upper bound, or giving a construction with a doubly-exponential difference between the stable configurations before and after the analyte is added.
Our work informs the fundamental question of how a thermodynamic equilibrium can change as a result of a small change to the system (adding a single molecule copy). While exponential amplification is traditionally viewed as inherently a non-equilibrium phenomenon, we find that in a strong sense exponential amplification can occur at thermodynamic equilibrium as well - where the "effect" (e.g., fluorescence) is exponential in types and complexity of the chemical components.

Subject Classification

ACM Subject Classification
  • Theory of computation → Models of computation
Keywords
  • Thermodynamic binding networks
  • signal amplification
  • integer programming

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References

  1. C. E. Blair and R. G. Jeroslow. The value function of an integer program. Mathematical Programming, 23(1):237-273, December 1982. URL: https://doi.org/10.1007/BF01583794.
  2. Keenan Breik, Cameron Chalk, David Doty, David Haley, and David Soloveichik. Programming substrate-independent kinetic barriers with thermodynamic binding networks. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 18(1):283-295, 2021. URL: https://doi.org/10.1109/TCBB.2019.2959310.
  3. Keenan Breik, Chris Thachuk, Marijn Heule, and David Soloveichik. Computing properties of stable configurations of thermodynamic binding networks. Theoretical Computer Science, 785:17-29, 2019. Google Scholar
  4. Harry MT Choi, Maayan Schwarzkopf, Mark E Fornace, Aneesh Acharya, Georgios Artavanis, Johannes Stegmaier, Alexandre Cunha, and Niles A Pierce. Third-generation in situ hybridization chain reaction: Multiplexed, quantitative, sensitive, versatile, robust. Development, 145(12):dev165753, 2018. Google Scholar
  5. David Doty, Trent A. Rogers, David Soloveichik, Chris Thachuk, and Damien Woods. Thermodynamic binding networks. In Robert Brijder and Lulu Qian, editors, DNA Computing and Molecular Programming, pages 249-266, Cham, 2017. Springer International Publishing. Google Scholar
  6. David Haley and David Doty. Computing properties of thermodynamic binding networks: An integer programming approach. In Matthew R. Lakin and Petr Šulc, editors, DNA 2021: Proceedings of the 27th International Meeting on DNA Computing and Molecular Programming, volume 205 of Leibniz International Proceedings in Informatics (LIPIcs), pages 2:1-2:16, Dagstuhl, Germany, 2021. Schloss Dagstuhl - Leibniz-Zentrum für Informatik. URL: https://doi.org/10.4230/LIPIcs.DNA.27.2.
  7. Randolph Lopez, Ruofan Wang, and Georg Seelig. A molecular multi-gene classifier for disease diagnostics. Nature chemistry, 10(7):746-754, 2018. Google Scholar
  8. Dionis Minev, Christopher M Wintersinger, Anastasia Ershova, and William M Shih. Robust nucleation control via crisscross polymerization of highly coordinated DNA slats. Nature communications, 12(1):1741, 2021. Google Scholar
  9. Gerald Schochetman, Chin-Yih Ou, and Wanda K. Jones. Polymerase chain reaction. The Journal of Infectious Diseases, 158(6):1154-1157, 1988. URL: http://www.jstor.org/stable/30137034.
  10. Erhu Xiong, Dongbao Yao, Andrew D. Ellington, and Sanchita Bhadra. Minimizing leakage in stacked strand exchange amplification circuits. ACS Synthetic Biology, 10(6):1277-1283, 2021. PMID: 34006090. URL: https://doi.org/10.1021/acssynbio.0c00615.
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