Probability 1 Computation with Chemical Reaction Networks

  • Rachel Cummings
  • David Doty
  • David Soloveichik
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8727)


The computational power of stochastic chemical reaction networks (CRNs) varies significantly with the output convention and whether or not error is permitted. Focusing on probability 1 computation, we demonstrate a striking difference between stable computation that converges to a state where the output cannot change, and the notion of limit-stable computation where the output eventually stops changing with probability 1. While stable computation is known to be restricted to semilinear predicates (essentially piecewise linear), we show that limitstable computation encompasses the set of predicates in \(\Delta^0_2\) in the arithmetical hierarchy (a superset of Turing-computable). In finite time, our construction achieves an error-correction scheme for Turing universal computation. This work refines our understanding of the tradeoffs between error and computational power in CRNs.


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Rachel Cummings
    • 1
  • David Doty
    • 2
  • David Soloveichik
    • 3
  1. 1.Northwestern UniversityEvanstonUSA
  2. 2.California Institute of TechnologyPasadenaUSA
  3. 3.University of CaliforniaSan FranciscoUSA

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