Composable Rate-Independent Computation in Continuous Chemical Reaction Networks

Biological regulatory networks depend upon chemical interactions to process information. Engineering such molecular computing systems is a major challenge for synthetic biology and related fields. The chemical reaction network (CRN) model idealizes chemical interactions, abstracting away specifics of the molecular implementation, and allowing rigorous reasoning about the computational power of chemical kinetics. Here we focus on function computation with CRNs, where we think of the initial concentrations of some species as the input and the eventual steady-state concentration of another species as the output. Specifically, we are concerned with CRNs that are rate-independent (the computation must be correct independent of the reaction rate law) and composable (\(f \circ g\) can be computed by concatenating the CRNs computing f and g). Rate independence and composability are important engineering desiderata, permitting implementations that violate mass-action kinetics, or even “well-mixedness”, and allowing the systematic construction of complex computation via modular design. We show that to construct composable rate-independent CRNs, it is necessary and sufficient to ensure that the output species of a module is not a reactant in any reaction within the module. We then exactly characterize the functions computable by such CRNs as superadditive, positive-continuous, and piecewise rational linear. Our results show that composability severely limits rate-independent computation unless more sophisticated input/output encodings are used.

[1]  Erik Winfree,et al.  DNA as a universal substrate for chemical kinetics , 2009, Proceedings of the National Academy of Sciences.

[2]  S. Ovchinnikov Max-Min Representation of Piecewise Linear Functions , 2000, math/0009026.

[3]  M. Feinberg,et al.  Dynamics of open chemical systems and the algebraic structure of the underlying reaction network , 1974 .

[4]  Luca Cardelli,et al.  Programmable chemical controllers made from DNA. , 2013, Nature nanotechnology.

[5]  David Eisenstat,et al.  The computational power of population protocols , 2006, Distributed Computing.

[6]  Luca Cardelli Strand Algebras for DNA Computing , 2009, DNA.

[7]  Erik Winfree,et al.  Enzyme-free nucleic acid dynamical systems , 2017, Science.

[8]  David K. Smith Theory of Linear and Integer Programming , 1987 .

[9]  David Doty,et al.  Composable computation in discrete chemical reaction networks , 2019, Distributed Computing.

[10]  Anne Condon,et al.  Composable Computation in Leaderless, Discrete Chemical Reaction Networks , 2020, DNA.

[11]  Marc D. Riedel,et al.  Computing Mathematical Functions using DNA via Fractional Coding , 2018, Scientific Reports.

[12]  Eduardo Sontag,et al.  Modular cell biology: retroactivity and insulation , 2008, Molecular systems biology.

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

[14]  François Fages,et al.  Strong Turing Completeness of Continuous Chemical Reaction Networks and Compilation of Mixed Analog-Digital Programs , 2017, CMSB.

[15]  Ho-Lin Chen,et al.  Rate-independent computation in continuous chemical reaction networks , 2014, ITCS.