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Published in: Natural Computing 1/2020

08-08-2019

Approximate majority analyses using tri-molecular chemical reaction networks

Authors: Anne Condon, Monir Hajiaghayi, David Kirkpatrick, Ján Maňuch

Published in: Natural Computing | Issue 1/2020

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Abstract

Approximate Majority is a well-studied problem in the context of chemical reaction networks (CRNs) and their close relatives, population protocols: Given a mixture of two types of species with an initial gap between their counts, a CRN computation must reach consensus on the majority species. Angluin, Aspnes, and Eisenstat proposed a simple population protocol for Approximate Majority and proved correctness and \(O(\log n)\) time efficiency with high probability, given an initial gap of size \(\omega (\sqrt{n}\log n)\) when the total molecular count in the mixture is n. Motivated by their intriguing but complex proof, we provide a new analysis of several CRNs for Approximate Majority, starting with a very simple tri-molecular protocol with just two reactions and two species. We obtain simple analyses of three bi-molecular protocols, including that of Angluin et al., by showing how they emulate the tri-molecular protocol. Our results improve on those of Angluin et al. in that they hold even with an initial gap of \(\varOmega (\sqrt{n \log n})\). We prove that our tri-molecular CRN is robust even when there is some uncertainty in the reaction rates, when some molecules are Byzantine (i.e., adversarial), or when activation of molecules is triggered by epidemic. We also analyse a natural variant of our tri-molecular protocol for the more general problem of multi-valued consensus. Our analysis approach, which leverages the simplicity of a tri-molecular CRN to ultimately reason about these many variants, may be useful in analysing other CRNs too.

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Footnotes
1
Here is the calculation for the probability conversion.
$$\begin{aligned} \rho _{r}(c)&= k'_{r} . \left[ \prod _{i=1}^m (x_i !/(x_i - s_i)!)\right]/v^{o-1} \\&= k'_{r} . \left[ \prod _{i=1}^m s_i!\right] . \left[ \prod _{i=1}^m \left( {\begin{array}{c}x_i\\ s_i\end{array}}\right) \right]/v^{o-1} \\&= \left[ \left( {\begin{array}{c}n\\ o\end{array}}\right) /v^{o-1} \right] k'_{r} \left[ \prod _{i=1}^m s_i!\right] . \left[ \prod _{i=1}^m \left( {\begin{array}{c}x_i\\ s_i\end{array}}\right) \right]/\left( {\begin{array}{c}n\\ o\end{array}}\right) \\&= \left[ \left( {\begin{array}{c}n\\ o\end{array}}\right) /v^{o-1} \right] k_{r} \left[ \prod _{i=1}^m \left( {\begin{array}{c}x_i\\ s_i\end{array}}\right) \right]/\left( {\begin{array}{c}n\\ o\end{array}}\right) , \end{aligned}$$
where
$$\begin{aligned} k_{r} = k'_{r} \left[ \prod _{i=1}^m s_i!\right] . \end{aligned}$$
(1)
We can interpret the last of these expressions for \(\rho _{r}(c)\) as the product of three terms. The first term, namely \(\left( {\begin{array}{c}n\\ o\end{array}}\right) /v^{o-1}\), corresponds to the (normalized) average rate of an interaction of order \(o\). The last term, namely \([\prod _{i=1}^m \left( {\begin{array}{c}x_i\\ s_i\end{array}}\right) ]/\left( {\begin{array}{c}n\\ o\end{array}}\right) \), is the probability that the reaction of order \(o\) has exactly the reactants of \(r\). The middle term \(k_{r}\) depends on the \(s_i\)’s, but could also model situations where different types of interactions have different rates, e.g., if some molecular species are larger than others. Normalizing the \(k_{r}\)’s by \(\sum k_{r}\) yields rate constants for our model.
 
2
Here, we tacitly assume that \(n=(2k)^2\), for some integer k. Extending the argument to general n, though notationally cumbersome, is straightforward.
 
3
A similar, though more involved argument, can be used to show the same result with relaxed X-consensus defined as an X-population of size \(n - (1+\epsilon )z\), for any \(\epsilon > 0\).
 
Literature
go back to reference Alistarh D, Aspnes J, Eisenstat D, Gelashvili R, Rivest RL (2017) Time-space trade-offs in population protocols. In: Proceedings of the twenty-eighth annual ACM-SIAM symposium on discrete algorithms. pp 2560–2579 Alistarh D, Aspnes J, Eisenstat D, Gelashvili R, Rivest RL (2017) Time-space trade-offs in population protocols. In: Proceedings of the twenty-eighth annual ACM-SIAM symposium on discrete algorithms. pp 2560–2579
go back to reference Angluin D, Aspnes J, Diamadi Z, Fischer MJ, Peralta R (2006a) Computation in networks of passively mobile finite-state sensors. Distrib Comput 18(4):235–253CrossRef Angluin D, Aspnes J, Diamadi Z, Fischer MJ, Peralta R (2006a) Computation in networks of passively mobile finite-state sensors. Distrib Comput 18(4):235–253CrossRef
go back to reference Angluin D, Aspnes J, Eisenstat D (2006b) Fast computation by population protocols with a leader. In: Dolev S (ed) Distributed computing (DISC), vol 4167. Lecture notes in computer science. Springer, Berlin, pp 61–75CrossRef Angluin D, Aspnes J, Eisenstat D (2006b) Fast computation by population protocols with a leader. In: Dolev S (ed) Distributed computing (DISC), vol 4167. Lecture notes in computer science. Springer, Berlin, pp 61–75CrossRef
go back to reference Angluin D, Aspnes J, Eisenstat D (2008) A simple population protocol for fast robust approximate majority. Distrib Comput 21(2):87–102CrossRef Angluin D, Aspnes J, Eisenstat D (2008) A simple population protocol for fast robust approximate majority. Distrib Comput 21(2):87–102CrossRef
go back to reference Becchetti L, Clementi A, Natale E, Pasquale F, Silvestri R, Trevisan L (2016) Simple dynamics for plurality consensus. Distrib Comput 30:1–14MathSciNetMATH Becchetti L, Clementi A, Natale E, Pasquale F, Silvestri R, Trevisan L (2016) Simple dynamics for plurality consensus. Distrib Comput 30:1–14MathSciNetMATH
go back to reference Becchetti L, Clementi AEF, Natale E, Pasquale F, Trevisan L (2016) Stabilizing consensus with many opinions. In: Proceedings of the twenty-seventh annual ACM-SIAM symposium on discrete algorithms. pp 620–635 Becchetti L, Clementi AEF, Natale E, Pasquale F, Trevisan L (2016) Stabilizing consensus with many opinions. In: Proceedings of the twenty-seventh annual ACM-SIAM symposium on discrete algorithms. pp 620–635
go back to reference Cardelli L, Csikász-Nagy A (2012) The cell cycle switch computes approximate majority. Nat Sci Rep 2:656CrossRef Cardelli L, Csikász-Nagy A (2012) The cell cycle switch computes approximate majority. Nat Sci Rep 2:656CrossRef
go back to reference Cardelli L, Kwiatkowska M, Laurenti L (2016) Programming discrete distributions with chemical reaction networks. In: Rondelez Y, Woods D (eds) DNA computing and molecular programming, vol 9818. Lecture notes in computer science. Springer, Cham, pp 35–51CrossRef Cardelli L, Kwiatkowska M, Laurenti L (2016) Programming discrete distributions with chemical reaction networks. In: Rondelez Y, Woods D (eds) DNA computing and molecular programming, vol 9818. Lecture notes in computer science. Springer, Cham, pp 35–51CrossRef
go back to reference Chen Y-J, Dalchau N, Srinivas N, Phillips A, Cardelli L, Soloveichik D, Seelig G (2013) Programmable chemical controllers made from DNA. Nat Nanotechnol 8(10):755–762CrossRef Chen Y-J, Dalchau N, Srinivas N, Phillips A, Cardelli L, Soloveichik D, Seelig G (2013) Programmable chemical controllers made from DNA. Nat Nanotechnol 8(10):755–762CrossRef
go back to reference Chernoff H (1952) A measure of asymptotic efficiency for tests of a hypothesis based on the sum of observations. Ann Math Stat 23:493–507MathSciNetCrossRef Chernoff H (1952) A measure of asymptotic efficiency for tests of a hypothesis based on the sum of observations. Ann Math Stat 23:493–507MathSciNetCrossRef
go back to reference Condon A, Hajiaghayi M, Kirkpatrick D, Manuch J (2017) Simplifying analyses of chemical reaction networks for approximate majority. In: 23rd international conference on DNA computing and molecular programming (Lecture notes in computer science), vol 10467. Springer-Verlag, pp 189–209 Condon A, Hajiaghayi M, Kirkpatrick D, Manuch J (2017) Simplifying analyses of chemical reaction networks for approximate majority. In: 23rd international conference on DNA computing and molecular programming (Lecture notes in computer science), vol 10467. Springer-Verlag, pp 189–209
go back to reference 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, pp 543–584CrossRef 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, pp 543–584CrossRef
go back to reference Doerr B, Goldberg LA, Minder L, Sauerwald T, Scheideler C (2011) Stabilizing consensus with the power of two choices. In: Proceedings of the twenty-third annual ACM symposium on parallelism in algorithms and architectures, SPAA ’11. New York, NY, USA, ACM, pp 149–158 Doerr B, Goldberg LA, Minder L, Sauerwald T, Scheideler C (2011) Stabilizing consensus with the power of two choices. In: Proceedings of the twenty-third annual ACM symposium on parallelism in algorithms and architectures, SPAA ’11. New York, NY, USA, ACM, pp 149–158
go back to reference Feller W (1968) An introduction to probability theory and its applications, vol 1, 3rd edn. Wiley, New YorkMATH Feller W (1968) An introduction to probability theory and its applications, vol 1, 3rd edn. Wiley, New YorkMATH
go back to reference Gillespie DT (1977) Exact stochastic simulation of coupled chemical reactions. J Phys Chem 81:2340–2361CrossRef Gillespie DT (1977) Exact stochastic simulation of coupled chemical reactions. J Phys Chem 81:2340–2361CrossRef
go back to reference Mertzios GB, Nikoletseas SE, Raptopoulos CL, Spirakis PG (2017) Determining majority in networks with local interactions and very small local memory. Distrib Comput 30(1):1–16MathSciNetCrossRef Mertzios GB, Nikoletseas SE, Raptopoulos CL, Spirakis PG (2017) Determining majority in networks with local interactions and very small local memory. Distrib Comput 30(1):1–16MathSciNetCrossRef
go back to reference Perron E, Vasudevan D, Vojnovic M (2009) Using three states for binary consensus on complete graphs. In: Proceedings of the 28th IEEE conference on computer communications (INFOCOM). pp 2527–2535 Perron E, Vasudevan D, Vojnovic M (2009) Using three states for binary consensus on complete graphs. In: Proceedings of the 28th IEEE conference on computer communications (INFOCOM). pp 2527–2535
go back to reference Soloveichik D, Cook M, Winfree E, Bruck J (2008) Computation with finite stochastic chemical reaction networks. Nat Comput 7:615–633MathSciNetCrossRef Soloveichik D, Cook M, Winfree E, Bruck J (2008) Computation with finite stochastic chemical reaction networks. Nat Comput 7:615–633MathSciNetCrossRef
go back to reference Soloveichik D, Seelig G, Winfree E (2010) DNA as a universal substrate for chemical kinetics. PNAS 107(12):5393–5398CrossRef Soloveichik D, Seelig G, Winfree E (2010) DNA as a universal substrate for chemical kinetics. PNAS 107(12):5393–5398CrossRef
go back to reference van Kampen N (1997) Stochastic processes in physics and chemistry (revised edition) van Kampen N (1997) Stochastic processes in physics and chemistry (revised edition)
Metadata
Title
Approximate majority analyses using tri-molecular chemical reaction networks
Authors
Anne Condon
Monir Hajiaghayi
David Kirkpatrick
Ján Maňuch
Publication date
08-08-2019
Publisher
Springer Netherlands
Published in
Natural Computing / Issue 1/2020
Print ISSN: 1567-7818
Electronic ISSN: 1572-9796
DOI
https://doi.org/10.1007/s11047-019-09756-4

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