UBC Theses and Dissertations
Error-free stable computation with polymer-supplemented chemical reaction networks Tai, Allison Yeu Yang
When disallowing error, traditional chemical reaction networks are very limited in computational power: Angluin et al. and Chen et al. showed that only semilinear predicates and functions are stably computable by CRNs. Qian et al. and others have shown that polymer-supplemented chemical reaction networks are capable of Turing-universal computation. However, their model requires that inputs are loaded onto the polymers at protocol initialization, in contrast with the traditional convention that inputs are represented by counts of molecules in solution. Here, we show that polymer-supplemented chemical reaction networks can stably simulate Turing-universal computations even with solution-based inputs. However, such simulations use a unique ''leader'' polymer per input type and thus involve many slow bottleneck reactions. We further refine the polymer-supplemented chemical reaction network model to allow for clone polymers, that is, multiple functionally-identical copies of a polymer, and provide an illustrative example of how bottleneck reactions can be reduced in this new model.
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