Timing in chemical reaction networks

Chemical reaction networks (CRNs) formally model chemistry in a well-mixed solution. CRNs are widely used to describe information processing occurring in natural cellular regulatory networks, and with upcoming advances in synthetic biology, CRNs are a promising programming language for the design of artificial molecular control circuitry. Due to a formal equivalence between CRNs and a model of distributed computing known as population protocols, results transfer readily between the two models. We show that if a CRN respects finite density (at most O(n) additional molecules can be produced from n initial molecules), then starting from any dense initial configuration (all molecular species initially present have initial count Omega(n), where n is the initial molecular count and volume), then every producible species is produced in constant time with high probability. This implies that no CRN obeying the stated constraints can function as a timer, able to produce a molecule, but doing so only after a time that is an unbounded function of the input size. This has consequences regarding an open question of Angluin, Aspnes, and Eisenstat concerning the ability of population protocols to perform fast, reliable leader election and to simulate arbitrary algorithms from a uniform initial state.

[1]  Ján Manuch,et al.  Reachability Bounds for Chemical Reaction Networks and Strand Displacement Systems , 2012, DNA.

[2]  Jehoshua Bruck,et al.  Programmability of Chemical Reaction Networks , 2009, Algorithmic Bioprocesses.

[3]  D. Gillespie Exact Stochastic Simulation of Coupled Chemical Reactions , 1977 .

[4]  David Eisenstat,et al.  Stably computable predicates are semilinear , 2006, PODC '06.

[5]  Luca Cardelli,et al.  The Cell Cycle Switch Computes Approximate Majority , 2012, Scientific Reports.

[6]  David Soloveichik,et al.  Robust Stochastic Chemical Reaction Networks and Bounded Tau-Leaping , 2008, J. Comput. Biol..

[7]  A Hjelmfelt,et al.  Chemical implementation of neural networks and Turing machines. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[8]  Keshab K. Parhi,et al.  Digital Signal Processing With Molecular Reactions , 2012, IEEE Design & Test of Computers.

[9]  Massimo Franceschetti,et al.  Closing the Gap in the Capacity of Wireless Networks Via Percolation Theory , 2007, IEEE Transactions on Information Theory.

[10]  A. Condon,et al.  Less haste, less waste: on recycling and its limits in strand displacement systems , 2011, Interface Focus.

[11]  Ho-Lin Chen,et al.  Deterministic function computation with chemical reaction networks , 2012, Natural Computing.

[12]  Matthew Cook,et al.  Computation with finite stochastic chemical reaction networks , 2008, Natural Computing.

[13]  T. Kurtz The Relationship between Stochastic and Deterministic Models for Chemical Reactions , 1972 .

[14]  Dana Angluin,et al.  Urn Automata , 2003 .

[15]  Noam Nisan,et al.  Hardness vs Randomness , 1994, J. Comput. Syst. Sci..

[16]  James Aspnes,et al.  An Introduction to Population Protocols , 2007, Bull. EATCS.

[17]  M. Magnasco CHEMICAL KINETICS IS TURING UNIVERSAL , 1997 .

[18]  Luca Cardelli,et al.  Termination Problems in Chemical Kinetics , 2008, CONCUR.

[19]  G. Seelig,et al.  DNA as a universal substrate for chemical kinetics , 2010, Proceedings of the National Academy of Sciences.

[20]  David Eisenstat,et al.  Fast computation by population protocols with a leader , 2006, Distributed Computing.

[21]  N. Wormald Differential Equations for Random Processes and Random Graphs , 1995 .

[22]  Luca Cardelli,et al.  Strand algebras for DNA computing , 2009, Natural Computing.

[23]  Anne Condon,et al.  Space and Energy Efficient Computation with DNA Strand Displacement Systems , 2012, DNA.

[24]  Michael J. Fischer,et al.  Computation in networks of passively mobile finite-state sensors , 2004, PODC '04.

[25]  J. Kingman Random Processes , 2019, Nature.