Kinetic TBNs is an interactive web simulator for designing Thermodynamic Binding Network systems and exploring theromodynamically-driven kinetics in such systems. Users can define monomers, provide start configurations, choose kinetic approaches, step through possible configurations, and inspect how reachability changes under different kinetic assumptions.
The simulator computes the energy of a configuration using the simplified TBN energy function:
Here, w is the bond strength set by the user, H(α) is the number of bonds in configuration α, and S(α) is the number of polymers in the configuration α [1].
Contributors
Divya Bajaj
Simulator design and implementation
Dr. Austin Luchsinger
Research advising and project direction
Research Group
Algorithmic Self-Assembly Research Group (ASARG)
University of Texas Rio Grande Valley
Implementation
At its core, the simulator uses a domain-specific language for designing TBN systems, including monomer sets, start configurations, and kinetic approaches. A backend built using Haskell parses and validates these descriptions, handles energy computation, transitions and kinetic semantics. The browser interface provides a visual way to run simulations and inspect configurations.
Performance Note
The simulator generally handles systems with large counts of the same monomer types well. Performance may degrade for systems with many unique monomer types, especially when a step produces many possible split or merge transitions.
Research Context
This project builds on the Thermodynamic Binding Network model and prior work formalizing TBN kinetics through merge and split transitions [2]. The simulator is intended to support exploration of kinetic reachability in TBNs.
References
[1] David Doty, Trent A. Rogers, David Soloveichik, Chris Thachuk, and Damien Woods. Thermodynamic binding networks. DNA Computing and Molecular Programming<, pages 249–266, Springer, 2017.
[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, 2019.