Abstract
This paper answers an open question of Chen et al. (DNA 2012: proceedings of the 18th international meeting on DNA computing and molecular programming, vol 7433 of lecture notes in computer science. Springer, Berlin, pp 25–42, 2012), who showed that a function \(f:\mathbb {N}^k\rightarrow \mathbb {N}^l\) is deterministically computable by a stochastic chemical reaction network (CRN) if and only if the graph of \(f\) is a semilinear subset of \(\mathbb {N}^{k+l}\). That construction crucially used “leaders”: the ability to start in an initial configuration with constant but non-zero counts of species other than the \(k\) species \(X_1,\ldots ,X_k\) representing the input to the function \(f\). The authors asked whether deterministic CRNs without a leader retain the same power. We answer this question affirmatively, showing that every semilinear function is deterministically computable by a CRN whose initial configuration contains only the input species \(X_1,\ldots ,X_k\), and zero counts of every other species, so long as \(f({\bf 0})={\bf 0}\). We show that this CRN completes in expected time \(O(n)\), where \(n\) is the total number of input molecules. This time bound is slower than the \(O(\log ^5 n)\) achieved in Chen et al. (2012), but faster than the \(O(n \log n)\) achieved by the direct construction of Chen et al. (2012).
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Notes
Semilinear sets are defined formally in Sect. 2. Informally, they are finite unions of “periodic” sets, where the definition of “periodic” is extended in a natural way to multi-dimensional spaces such as \(\mathbb {N}^k\).
It is easy to see that no leaderless CRN could reach an output stable state with positive count of output species \(Y\) from an initial state with no molecules, since it would need to contain reaction(s) of the form \(\emptyset \rightarrow A\) for some species \(A\) from which (unbounded counts of) \(Y\) could be produced.
Those authors use the term “stably compute”, but we reserve the term “compute” to apply to the computation of non-Boolean functions. Also, we omit discussion of the definition of stable computation used in the population protocols literature, which employs a notion of “fair” executions; the definitions are proven equivalent in Chen et al. (2012).
One possibility is to have a “dynamically” growing volume as in Soloveichik et al. (2008).
Its output species could potentially be reactants so long as they are catalytic, meaning that the stoichiometry of the species as a product is at least as great as its stoichiometry as a reactant, e.g. if \(Y\) is the output species, \(X + Y \rightarrow Z + Y\) or \(A + Y \rightarrow Y + Y\).
By \({\bf y}_P = O(f({\bf x}))\), we mean that there is a constant \(c\) such that \(y_P \le c f({\bf x})\) for all \({\bf x}\in \mathbb {N}^k\).
Abbreviations
- CRD:
-
Chemical reaction decider
- CRN:
-
Chemical reaction network
- CRC:
-
Chemical reaction computer
References
Angluin D, Aspnes J, Diamadi Z, Fischer M, Peralta R (2006) Computation in networks of passively mobile finite-state sensors. Distrib Comput 18:235–253. Preliminary version appeared in PODC 2004
Angluin D, Aspnes J, Eisenstat D (2006) Stably computable predicates are semilinear. In: PODC 2006: Proceedings of the twenty-fifth annual ACM symposium on Principles of distributed computing. New York, NY, USA, pp 292–299. ACM Press
Angluin D, Aspnes J, Eisenstat D (2008) Fast computation by population protocols with a leader. Distrib Comput 21(3):183–199. Preliminary version appeared in DISC 2006
Chen HL, Doty D, Soloveichik D (2012) Deterministic function computation with chemical reaction networks. In: DNA 2012: Proceedings of the 18th international meeting on DNA computing and molecular programming, vol 7433 of Lecture Notes in Computer Science, Springer, pp 25–42
Chen HL, Doty D, Soloveichik D (2014) Rate-independent computation in mass-action chemical reaction networks. In ITCS 2014: Proceedings of the 5th conference on innovations in theoretical computer science, pp 313–326
Condon A, Hu A, Manuch J, Thachuk C (2012a) Less haste, less waste: on recycling and its limits in strand displacement systems. J R Soc Interface 2:512–521. Preliminary version appeared in, DNA 2011
Condon A, Kirkpatrick B, Manuch J (2012b) Reachability bounds for chemical reaction networks and strand displacement systems. In DNA 2012: 18th international meeting on DNA computing and molecular programming, vol 7433, Springer, pp 43–57
Cook M, Soloveichik D, Winfree E, Bruck J (2009) Programmability of chemical reaction networks. In: Condon A, Harel D, Kok JN, Salomaa A, Winfree E (eds) Algorithmic bioprocesses. Springer, Berlin Heidelberg, pp 543–584
Doty D (2014) Timing in chemical reaction networks. In: SODA 2014: Proceedings of the 25th Annual ACM-SIAM symposium on discrete algorithms. pp 772–784
Gillespie DT (1977) Exact stochastic simulation of coupled chemical reactions. J Phys Chem 81(25):2340–2361
Ginsburg S, Spanier EH (1966) Semigroups, Presburger formulas, and languages. Pac J Math 16(2):285–296
Hjelmfelt A, Weinberger ED, Ross J (1991) Chemical implementation of neural networks and turing machines. Proc Natl Acad Sci USA 88(24):10983–10987
Jiang H, Riedel M, Parhi K (2012) Digital signal processing with molecular reactions. IEEE Design Test Comp 29(3):21–31
Lipton RJ (1976) The reachability problem requires exponential space. Technical report. Yale University
Magnasco MO (1997) Chemical kinetics is Turing universal. Phys Rev Lett 78(6):1190–1193
Soloveichik D, Cook M, Winfree E, Bruck J (2008) Computation with finite stochastic chemical reaction networks. Natural Computing 7(4):615–633
Soloveichik D, Seelig G, Winfree E (2010) DNA as a universal substrate for chemical kinetics. Proc Natl Acad Sci USA 107(12):5393. Preliminary version appeared in DNA 2008
Thachuk C, Condon A (2012) Space and energy efficient computation with DNA strand displacement systems. In: DNA 2012: Proceedings of the 18th international meeting on DNA computing and molecular programming, pp 135–149
Zavattaro G, Cardelli L (2008) Termination problems in chemical kinetics. CONCUR 2008-Concurrency Theory, pp 477–491
Acknowledgments
We are indebted to Anne Condon for helpful discussions and suggestions. David Doty was supported by the Molecular Programming Project under NSF Grant 0832824 and by NSF Grants CCF-1219274 and CCF-1162589.
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Doty, D., Hajiaghayi, M. Leaderless deterministic chemical reaction networks. Nat Comput 14, 213–223 (2015). https://doi.org/10.1007/s11047-014-9435-8
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DOI: https://doi.org/10.1007/s11047-014-9435-8