Thermodynamically Driven Signal Amplification (2307.01550v1)
Abstract: The field of chemical computation attempts to model computational behavior that arises when molecules, typically nucleic acids, are mixed together. 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. 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. 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. 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).
- The value function of an integer program. Mathematical Programming, 23(1):237–273, Dec 1982. doi:10.1007/BF01583794.
- Programming substrate-independent kinetic barriers with thermodynamic binding networks. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 18(1):283–295, 2021. doi:10.1109/TCBB.2019.2959310.
- Computing properties of stable configurations of thermodynamic binding networks. Theoretical Computer Science, 785:17–29, 2019.
- Third-generation in situ hybridization chain reaction: Multiplexed, quantitative, sensitive, versatile, robust. Development, 145(12):dev165753, 2018.
- Thermodynamic binding networks. In Robert Brijder and Lulu Qian, editors, DNA Computing and Molecular Programming, pages 249–266, Cham, 2017. Springer International Publishing.
- 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://drops.dagstuhl.de/opus/volltexte/2021/14669, doi:10.4230/LIPIcs.DNA.27.2.
- A molecular multi-gene classifier for disease diagnostics. Nature chemistry, 10(7):746–754, 2018.
- Robust nucleation control via crisscross polymerization of highly coordinated DNA slats. Nature communications, 12(1):1741, 2021.
- Polymerase chain reaction. The Journal of Infectious Diseases, 158(6):1154–1157, 1988. URL: http://www.jstor.org/stable/30137034.
- Minimizing leakage in stacked strand exchange amplification circuits. ACS Synthetic Biology, 10(6):1277–1283, 2021. PMID: 34006090. arXiv:https://doi.org/10.1021/acssynbio.0c00615, doi:10.1021/acssynbio.0c00615.