There will be a 5-day workshop on Programming with Chemical Reaction Networks: Mathematical Foundation at BIRS from Sunday, June 8 to Friday June 13, 2014 It’s being organized by

• Anne Condon (University of British Columbia)

• David Doty (California Institute of Technology)

• Chris Thachuk (University of Oxford).

BIRS is the Banff International Research Station, in the mountains west of Calgary, in Alberta, Canada.

### Description

Here’s the workshop proposal on the BIRS website. It’s a pretty interesting proposal, especially if you’ve already read Luca Cardelli’s description of computing with chemical reaction networks, at the end of our series of posts on chemical reaction networks. The references include a lot of cool papers, so I’ve created links to those to help you get ahold of them.

This workshop will explore three of the most important research themes concerning stochastic chemical reaction networks (CRNs). Below we motivate each theme and highlight key questions that the workshop will address. Our main objective is to bring together distinct research communities in order to consider new problems that could not be fully appreciated in isolation. It is also our aim to determine commonalities between different disciplines and bodies of research. For example, research into population protocols, vector addition systems, and Petri networks provide a rich body of theoretical results that may already address contemporary problems arising in the study of CRNs.

## Computational power of CRNs

Before designing robust and practical systems, it is useful to know the limits to computing with a chemical soup. Some interesting theoretical results are already known for stochastic chemical reaction networks. The computational power of CRNs depend upon a number of factors, including: (i) is the computation deterministic, or probabilistic, and (ii) does the CRN have an initial context — certain species, independent of the input, that are initially present in some exact, constant count.

In general, CRNs with a constant number of species (independent of the input length) are capable of Turing universal computation [17], if the input is represented by the exact (unary) count of one molecular species, some small probability of error is permitted and an initial context in the form of a single-copy leader molecule is used.

Could the same result hold in the absence of an initial context? In a surprising result based on the distributed computing model of population protocols, it has been shown that if a computation must be error-free, then deterministic computation with CRNs having an initial context is limited to computing semilinear predicates [1], later extended to functions outputting natural numbers encoded by molecular counts [5].

Furthermore, any semilinear predicate or function can be computed by that class of CRNs in expected time polylogarithmic in the input length. Building on this result, it was recently shown that by incurring an expected time linear in the input length, the same result holds for “leaderless” CRNs [8] — CRNs with no initial context. Can this result be improved to sub-linear expected time? Which class of functions can be computed deterministically by a CRN without an initial context in expected time polylogarithmic in the input length?

While (restricted) CRNs are Turing-universal, current results use space proportional to the computation time. Using a non-uniform construction, where the number of species is proportional to the input length and each initial species is present in some constant count, it is known that any S(n) space-bounded computation can be computed by a logically-reversible tagged CRN, within a reaction volume of size poly(S(n)) [18]. Tagged CRNs were introduced to model explicitly the fuel molecules in physical realizations of CRNs such as DNA strand displacement systems [6] that are necessary to supply matter and energy for implementing reactions such as X → X + Y that violate conservation of mass and/or energy.

Thus, for space-bounded computation, there exist CRNs that are time-efficient or are space-efficient. Does there exist time- and space-efficient CRNs to compute any space-bounded function?

## Designing and verifying robust CRNs

While CRNs provide a concise model of chemistry, their physical realizations are often more complicated and more granular. How can one be sure they accurately implement the intended network behaviour? Probabilistic model checking has already been employed to find and correct inconsistencies between CRNs and their DNA strand displacement system (DSD) implementations [9]. However, at present, model checking of arbitrary CRNs is only capable of verifying the correctness of very small systems. Indeed, verification of these types of systems is a difficult problem: probabilistic state reachability is undecidable [17, 20] and general state reachability is EXPSPACE-hard [4].

How can larger systems be verified? A deeper understanding of CRN behaviour may simplify the process of model checking. As a motivating example, there has been recent progress towards verifying that certain DSD implementations correctly simulate underlying CRNs [16, 7, 10]. This is an important step to ensuring correctness, prior to experiments. However, DSDs can also suffer from other errors when implementing CRNs, such as spurious hybridization or strand displacement. Can DSDs and more generally CRNs be designed to be robust to such predictable errors? Can error correcting codes and redundant circuit designs used in traditional computing be leveraged in these chemical computers? Many other problems arise when implementing CRNs. Currently, unique types of fuel molecules must be designed for every reaction type. This complicates the engineering process significantly. Can a universal type of fuel be designed to smartly implement any reaction?

## Energy efficient computing with CRNs

Rolf Landauer showed that logically irreversible computation — computation as modeled by a standard Turing machine — dissipates an amount of energy proportional to the number of bits of information lost, such as previous state information, and therefore cannot be energy efficient [11]. However, Charles Bennett showed that, in principle, energy efficient computation is possible, by proposing a universal Turing machine to perform logically-reversible computation and identified nucleic acids (RNA/DNA) as a potential medium to realize logically-reversible computation in a physical system [2].

There have been examples of logically-reversible DNA strand displacement systems — a physical realization of CRNs — that are, in theory, capable of complex computation [12, 19]. Are these systems energy efficient in a physical sense? How can this argument be made formally to satisfy both the computer science and the physics communities? Is a physical experiment feasible, or are these results merely theoretical footnotes?

## References

[1] D. Angluin, J. Aspnes, and D. Eisenstat. Stably computable predicates are semilinear. In PODC, pages 292–299, 2006.

[2] C. H. Bennett. Logical reversibility of computation. IBM Journal of Research and Development, 17 (6):525–532, 1973.

[3] L. Cardelli and A. Csikasz-Nagy. The cell cycle switch computes approximate majority. Scientific Reports, 2, 2012.

[4] E. Cardoza, R. Lipton, A. R. Meyer. Exponential space complete problems for Petri nets and commutative semigroups (Preliminary Report). Proceedings of the Eighth Annual ACM Symposium on Theory of Computing, pages 507–54, 1976.

[5] H. L. Chen, D. Doty, and D. Soloveichik. Deterministic function computation with chemical reaction networks. DNA Computing and Molecular Programming, pages 25–42, 2012.

[6] A. Condon, A. J. Hu, J. Manuch, and C. Thachuk. Less haste, less waste: on recycling and its limits in strand displacement systems. Journal of the Royal Society: Interface Focus, 2 (4):512–521, 2012.

[7] Q. Dong. A bisimulation approach to verification of molecular implementations of formal chemical reaction network. Master’s thesis. SUNY Stony Brook, 2012.

[8] D. Doty and M. Hajiaghayi. Leaderless deterministic chemical reaction networks. In Proceedings of the 19th International Meeting on DNA Computing and Molecular Programming, 2013.

[9] M. R. Lakin, D. Parker, L. Cardelli, M. Kwiatkowska, and A. Phillips. Design and analysis of DNA strand displacement devices using probabilistic model checking. Journal of The Royal Society Interface, 2012.

[10] M. R. Lakin, D. Stefanovic and A. Phillips. Modular Verification of Two-domain DNA Strand Displacement Networks via Serializability Analysis. In Proceedings of the 19th Annual conference on DNA computing, 2013.

[11] R. Landauer. Irreversibility and heat generation in the computing process. IBM Journal of research and development, 5 (3):183–191, 1961.

[12] L. Qian, D. Soloveichik, and E. Winfree. Efficient Turing-universal computation with DNA polymers (extended abstract) . In Proceedings of the 16th Annual conference on DNA computing, pages 123–140, 2010.

[13] L. Qian and E. Winfree. Scaling up digital circuit computation with DNA strand displacement cascades. Science, 332 (6034):1196–1201, 2011.

[14] L. Qian, E. Winfree, and J. Bruck. Neural network computation with DNA strand displacement cascades. Nature, 475 (7356):368–372, 2011.

[15] G. Seelig, D. Soloveichik, D.Y. Zhang, and E. Winfree. Enzyme-free nucleic acid logic circuits. Science, 314 (5805):1585–1588, 2006.

[16] S. W. Shin. Compiling and verifying DNA-based chemical reaction network implementations. Master’s thesis. California Insitute of Technology, 2011.

[17] D. Soloveichik, M. Cook, E. Winfree, and J. Bruck. Computation with finite stochastic chemical reaction networks. Natural Computing, 7 (4):615–633, 2008.

[18] C. Thachuk. Space and energy efficient molecular programming. PhD thesis, University of British Columbia, 2012.

[19] C. Thachuk and A. Condon. Space and energy efficient computation with DNA strand displacement systems. In Proceedings of the 18th Annual International Conference on DNA computing and Molecular Programming, 2012.

[20] G. Zavattaro and L. Cardelli. Termination Problems in Chemical Kinetics. In Proceedings of the 2008 Conference on Concurrency Theory, pages 477–491, 2008.

I’ve been invited to this workshop, and I’ll attend and try to write some blog articles about it. You can see a list of other participants here. I’ve already met one of the organizers, David Doty, when I gave a talk on chemical reaction networks at Erik Winfree’s group in Caltech. But I look forward to meeting everyone else!

I’m in Banff at the moment! It’s a wonderful place. There is a permanent Mathematics research station here, but also centres dedicated to fostering creativity in other areas, like art, music and literature.

Just watch out for the grizzlies :)

Cool! What are you doing there?

I hear you shouldn’t leave novels or other literature lying around outside—it attracts the grizzly bears.

Mostly trying to get over my jet-lag and flu, and taking in the beautiful scenery. But from this evening, I’ll be taking part in a workshop on Parameterized Morse Theory. I have to admit I don’t know much about Morse theory, but lots of my work is based on properties of topological manifolds, so hopefully I’ll learn a lot.

What’s a good simple example of a nontrivial computation that can be performed by a chemical reaction network?

I don’t know enough to answer your question well. I’ve mainly heard about general theorems saying that large classes of computations can be performed by chemical reaction networks. Here’s a brief summary written by Luca Cardelli for Part 26 of the network theory series:

I hope to learn more at this workshop—maybe that will help me answer your question.

I would like to nudge the conversation in the direction of understanding what biologically important chemical reactions actually do. They do complicated things, but it may not be wise to call these things ‘computations’, because what they do is often more ‘sloppy’ than what a digital computer does, yet still ‘successful’ according to some rather subtle criterion of ‘success’ that’s ultimately Darwinian. I don’t think anyone knows how to make this precise yet—not precise enough for theorems, anyway.

Needless to say, these thoughts are not new to me.

I’m at this workshop:

• Programming with Chemical Reaction Networks: Mathematical Foundations, Banff International Research Station, 8-13 June 2014.

A while ago I blogged about David Soloveichik’s talk at this workshop:

• Programming with Chemical Reaction Networks: Mathematical Foundations, Banff International Research Station, 8-13 June 2014.

Now the slides for his talk are available:

• David Soloveichik, U.C. San Francisco, The computational power of chemical reaction networks.

And now I’d like to tell you about three more talks!

There’s a workshop this summer:

• Dynamics, Thermodynamics and Information Processing in Chemical Networks, 13-16 June 2017, Complex Systems and Statistical Mechanics Group, University of Luxembourg. Organized by Massimiliano Esposito and Matteo Polettini.

They write, “The idea of the workshop is to bring in contact a small number of high-profile research groups working at the frontier between physics and biochemistry, with particular emphasis on the role of Chemical Networks.”

I’m looking forward to this, in part because there will be a mix of speakers I’ve met, speakers I know but haven’t met, and speakers I don’t know yet. I feel like reminiscing a bit, and I hope you’ll forgive me these reminiscences, since if you try the links you’ll get an introduction to the interface between computation and chemical reaction networks.