Published October 2017 | Version public
Journal Article

Speed faults in computation by chemical reaction networks

  • 1. ROR icon National Taiwan University
  • 2. ROR icon California Institute of Technology
  • 3. ROR icon University of California, Davis
  • 4. ROR icon The University of Texas at Austin

Abstract

Chemical reaction networks (CRNs) formally model chemistry in a well-mixed solution. Assuming a fixed molecular population size and bimolecular reactions, CRNs are formally equivalent to population protocols, a model of distributed computing introduced by Angluin, Aspnes, Diamadi, Fischer, and Peralta (PODC 2004). The challenge of fast computation by CRNs (or population protocols) is to not rely on a bottleneck "slow" reaction that requires two molecules (agent states) to react (communicate), both of which are present in low (O(1)) counts. It is known that CRNs can be fast in expectation by avoiding slow reactions with high probability. However, states may be reachable from which the correct answer may only be computed by executing a slow reaction. We deem such an event a speed fault. We show that the predicates stably decidable by CRNs guaranteed to avoid speed faults are precisely the detection predicates: Boolean combinations of questions of the form "is a certain species present or not?". This implies, for instance, that no speed fault free CRN decides whether there are at least two molecules of a certain species—i.e., speed fault free CRNs "can't count".

Additional Information

© 2015 Springer-Verlag Berlin. Received: 5 February 2015. Accepted: 5 September 2015. Published online: 14 September 2015. A preliminary version of this article appeared as [10]; the current version has been significantly revised for clarity, and includes several omitted proofs. The first, third, and fourth authors were supported by the Molecular Programming Project under NSF Grants 0832824 and 1317694, the first author was supposed by NSC Grant number 101-2221-E-002-122-MY3, the second author was supported by NSF Grants CCF-1049899 and CCF-1217770, the third author was supported by a Computing Innovation Fellowship under NSF Grant 1019343, and NSF Grants CCF-1219274 and CCF-1162589, and the fourth author was supported by NIGMS Systems Biology Center grant P50 GM081879. We thank Damien Woods, Anne Condon, Chris Thachuk, Bonnie Kirkpatrick, Monir Hajiaghayi, and Ján Maňuch for useful discussions. We are also grateful to anonymous reviewers for pointing out a number of issues in the original version of this manuscript.

Additional details

Identifiers

Eprint ID
82165
DOI
10.1007/s00446-015-0255-6
Resolver ID
CaltechAUTHORS:20171006-113226137

Related works

Funding

NSF
CCF-0832824
NSF
CCF-1317694
National Science Council (Taipei)
101-2221-E-002-122-MY3
NSF
CCF-1049899
NSF
CCF-1217770
NSF
CNS-1019343
NSF
CCF-1219274
NSF
CCF-1162589
NIH
P50 GM081879

Dates

Created
2017-10-06
Created from EPrint's datestamp field
Updated
2021-11-15
Created from EPrint's last_modified field