Resource Convertibility (Part 3)

13 April, 2015

guest post by Tobias Fritz

In Part 1 and Part 2, we learnt about ordered commutative monoids and how they formalize theories of resource convertibility and combinability. In this post, I would like to say a bit about the applications that have been explored so far. First, the study of resource theories has become a popular subject in quantum information theory, and many of the ideas in my paper actually originate there. I’ll list some references at the end. So I hope that the toolbox of ordered commutative monoids will turn out to be useful for this. But here I would like to talk about an example application that is much easier to understand, but no less difficult to analyze: graph theory and the resource theory of zero-error communication.

A graph consists of a bunch of nodes connected by a bunch of edges, for example like this:

This particular graph is the pentagon graph or 5-cycle. To give it some resource-theoretic interpretation, think of it as the distinguishability graph of a communication channel, where the nodes are the symbols that can be sent across the channel, and two symbols share an edge if and only if they can be unambiguously decoded. For example, the pentagon graph roughly corresponds to the distinguishability graph of my handwriting, when restricted to five letters only:

So my ‘w’ is distinguishable from my ‘u’, but it may be confused for my ‘m’. In order to communicate unambiguously, it looks like I should restrict myself to using only two of those letters in writing, since any third of them may be mistaken for one of the other three. But alternatively, I could use a block code to create context around each letter which allows for perfect disambiguation. This is what happens in practice: I write in natural language, where an entire word is usually not ambiguous.

One can now also consider graph homomorphisms, which are maps like this:

The numbers on the nodes indicate where each node on the left gets mapped to. Formally, a graph homomorphism is a function taking nodes to nodes such that adjacent nodes get mapped to adjacent nodes. If a homomorphism G\to H exists between graphs G and H, then we also write H\geq G; in terms of communication channels, we can interpret this as saying that H simulates G, since the homomorphism provides a map between the symbols which preserves distinguishability. A ‘code’ for a communication channel is then just a homomorphism from the complete graph in which all nodes share an edge to the graph which describes the channel. With this ordering structure, the collection of all finite graphs forms an ordered set. This ordered set has an intricate structure which is intimately related to some big open problems in graph theory.

We can also combine two communication channels to form a compound one. Going back to the handwriting example, we can consider the new channel in which the symbols are pairs of letters. Two such pairs are distinguishable if and only if either the first letters of each pair are distinguishable or the second letters are,

(a,b) \sim (a',b') \:\Leftrightarrow\: a\sim a' \:\lor\: b\sim b'

When generalized to arbitrary graphs, this yields the definition of disjunctive product of graphs. It is not hard to show that this equips the ordered set of graphs with a binary operation compatible with the ordering, so that we obtain an ordered commutative monoid denoted Grph. It mathematically formalizes the resource theory of zero-error communication.

Using the toolbox of ordered commutative monoids combined with some concrete computations on graphs, one can show that Grph is not cancellative: if K_{11} is the complete graph on 11 nodes, then 3C_5\not\geq K_{11}, but there exists a graph G such that

3 C_5 + G \geq K_{11} + G

The graph G turns out to have 136 nodes. This result seems to be new. But if you happen to have seen something like this before, please let me know!

Last time, we also talked about rates of conversion. In Grph, it turns out that some of these correspond to famous graph invariants! For example, the rate of conversion from a graph G to the single-edge graph K_2 is Shannon capacity \Theta(\overline{G}), where \overline{G} is the complement graph. This is of no surprise since \Theta was originally defined by Shannon with precisely this rate in mind, although he did not use the language of ordered commutative monoids. In any case, the Shannon capacity \Theta(\overline{G}) is a graph invariant notorious for its complexity: it is not known whether there exists an algorithm to compute it! But an application of the Rate Theorem from Part 2 gives us a formula for the Shannon capacity:

\Theta(\overline{G}) = \inf_f f(G)

where f ranges over all graph invariants which are monotone under graph homomorphisms, multiplicative under disjunctive product, and normalized such that f(K_2) = 2. Unfortunately, this formula still not produce an algorithm for computing \Theta. But it nonconstructively proves the existence of many new graph invariants f which approximate the Shannon capacity from above.

Although my story ends here, I also feel that the whole project has barely started. There are lots of directions to explore! For example, it would be great to fit Shannon’s noisy channel coding theorem into this framework, but this has turned out be technically challenging. If you happen to be familiar with rate-distortion theory and you want to help out, please get in touch!

References

Here is a haphazard selection of references on resource theories in quantum information theory and related fields:

• Igor Devetak, Aram Harrow and Andreas Winter, A resource framework for quantum Shannon theory.

• Gilad Gour, Markus P. Müller, Varun Narasimhachar, Robert W. Spekkens and Nicole Yunger Halpern, The resource theory of informational nonequilibrium in thermodynamics.

• Fernando G.S.L. Brandão, Michał Horodecki, Nelly Huei Ying Ng, Jonathan Oppenheim and Stephanie Wehner, The second laws of quantum thermodynamics.

• Iman Marvian and Robert W. Spekkens, The theory of manipulations of pure state asymmetry: basic tools and equivalence classes of states under symmetric operations.

• Elliott H. Lieb and Jakob Yngvason, The physics and mathematics of the second law of thermodynamics.


Resource Convertibility (Part 2)

10 April, 2015

guest post by Tobias Fritz

In Part 1, I introduced ordered commutative monoids as a mathematical formalization of resources and their convertibility. Today I’m going to say something about what to do with this formalization. Let’s start with a quick recap!

Definition: An ordered commutative monoid is a set A equipped with a binary relation \geq, a binary operation +, and a distinguished element 0 such that the following hold:

+ and 0 equip A with the structure of a commutative monoid;

\geq equips A with the structure of a partially ordered set;

• addition is monotone: if x\geq y, then also x + z \geq y + z.

Recall also that we think of the x,y\in A as resource objects such that x+y represents the object consisting of x and y together, and x\geq y means that the resource object x can be converted into y.

When confronted with an abstract definition like this, many people ask: so what is it useful for? The answer to this is twofold: first, it provides a language which we can use to guide our thoughts in any application context. Second, the definition itself is just the very start: we can now also prove theorems about ordered commutative monoids, which can be instantiated in any particular application context. So the theory of ordered commutative monoids will provide a useful toolbox for talking about concrete resource theories and studying them. In the remainder of this post, I’d like to say a bit about what this toolbox contains. For more, you’ll have to read the paper!

To start, let’s consider catalysis as one of the resource-theoretic phenomena neatly captured by ordered commutative monoids. Catalysis is the phenomenon that certain conversions become possible only due to the presence of a catalyst, which is an additional resource object which does not get consumed in the process of the conversion. For example, we have

\text{timber + nails}\not\geq \text{table},

\text{timber + nails + saw + hammer} \geq \text{table + saw + hammer}

because making a table from timber and nails requires a saw and a hammer as tools. So in this example, ‘saw + hammer’ is a catalyst for the conversion of ‘timber + nails’ into ‘table’. In mathematical language, catalysis occurs precisely when the ordered commutative monoid is not cancellative, which means that x + z\geq y + z sometimes holds even though x\geq y does not. So, the notion of catalysis perfectly matches up with a very natural and familiar notion from algebra.

One can continue along these lines and study those ordered commutative monoids which are cancellative. It turns out that every ordered commutative monoid can be made cancellative in a universal way; in the resource-theoretic interpretation, this boils down to replacing the convertibility relation by catalytic convertibility, in which x is declared to be convertible into y as soon as there exists a catalyst which achieves this conversion. Making an ordered commutative monoid cancellative like this is a kind of ‘regularization': it leads to a mathematically more well-behaved structure. As it turns out, there are several additional steps of regularization that can be performed, and all of these are both mathematically natural and have an appealing resource-theoretic interpretation. These regularizations successively take us from the world of ordered commutative monoids to the realm of linear algebra and functional analysis, where powerful theorems are available. For now, let me not go into the details, but only try to summarize one of the consequences of this development. This requires a bit of preparation.

In many situations, it is not just of interest to convert a single copy of some resource object x into a single copy of some y; instead, one may be interested in converting many copies of x into many copies of y all together, and thereby maximizing (or minimizing) the ratio of the resulting number of y‘s compared to the number of x‘s that get consumed. This ratio is measured by the maximal rate:

\displaystyle{ R_{\mathrm{max}}(x\to y) = \sup \left\{ \frac{m}{n} \:|\: nx \geq my \right\} }

Here, m and n are natural numbers, and nx stands for the n-fold sum x+\cdots+x, and similarly for my. So this maximal rate quantifies how many y’ s we can get out of one copy of x, when working in a ‘mass production’ setting. There is also a notion of regularized rate, which has a slightly more complicated definition that I don’t want to spell out here, but is similar in spirit. The toolbox of ordered commutative monoids now provides the following result:

Rate Theorem: If x\geq 0 and y\geq 0 in an ordered commutative monoid A which satisfies a mild technical assumption, then the maximal regularized rate from x to y can be computed like this:

\displaystyle{ R_{\mathrm{max}}^{\mathrm{reg}}(x\to y) = \inf_f \frac{f(y)}{f(x)} }

where f ranges over all functionals on A with f(y)\neq 0.

Wait a minute, what’s a ‘functional’? It’s defined to be a map f:A\to\mathbb{R} which is monotone,

x\geq y \:\Rightarrow\: f(x)\geq f(y)

and additive,

f(x+y) = f(x) + f(y)

In economic terms, we can think of a functional as a consistent assignment of prices to all resource objects. If x is at least as useful as y, then the price of x should be at least as high as the price of y; and the price of two objects together should be the sum of their individual prices. So the f in the rate formula above ranges over all ‘markets’ on which resource objects can be ‘traded’ at consistent prices. The term ‘functional’ is supposed to hint at a relation to functional analysis. In fact, the proof of the theorem crucially relies on the Hahn–Banach Theorem.

The mild technical mentioned in the Rate Theorem is that the ordered commutative monoid needs to have a generating pair. This turns out to hold in the applications that I have considered so far, and I hope that it will turn out to hold in most others as well. For the full gory details, see the paper.

So this provides some idea of what kinds of gadgets one can find in the toolbox of ordered commutative monoids. Next time, I’ll show some applications to graph theory and zero-error communication and say a bit about where this project might be going next.

Resource convertibility: part 3.


Resource Convertibility (Part 1)

7 April, 2015

guest post by Tobias Fritz

Hi! I am Tobias Fritz, a mathematician at the Perimeter Institute for Theoretical Physics in Waterloo, Canada. I like to work on all sorts of mathematical structures which pop up in probability theory, information theory, and other sorts of applied math. Today I would like to tell you about my latest paper:

The mathematical structure of theories of resource convertibility, I.

It should be of interest to Azimuth readers as it forms part of what John likes to call ‘green mathematics’. So let’s get started!

Resources and their management are an essential part of our everyday life. We deal with the management of time or money pretty much every day. We also consume natural resources in order to afford food and amenities for (some of) the 7 billion people on our planet. Many of the objects that we deal with in science and engineering can be considered as resources. For example, a communication channel is a resource for sending information from one party to another. But for now, let’s stick with a toy example: timber and nails constitute a resource for making a table. In mathematical notation, this looks like so:

\mathrm{timber} + \mathrm{nails} \geq \mathrm{table}

We interpret this inequality as saying that “given timber and nails, we can make a table”. I like to write it as an inequality like this, which I think of as stating that having timber and nails is at least as good as having a table, because the timber and nails can always be turned into a table whenever one needs a table.

To be more precise, we should also take into account that making the table requires some tools. These tools do not get consumed in the process, so we also get them back out:

\text{timber} + \text{nails} + \text{saw} + \text{hammer} \geq \text{table} + \text{hammer} + \text{saw}

Notice that this kind of equation is analogous to a chemical reaction equation like this:

2 \mathrm{H}_2 + \mathrm{O}_2 \geq \mathrm{H}_2\mathrm{O}

So given a hydrogen molecules and an oxygen molecule, we can let them react such as to form a molecule of water. In chemistry, this kind of equation would usually be written with an arrow ‘\rightarrow’ instead of an ordering symbol ‘\geq’ , but here we interpret the equation slightly differently. As with the timber and the nails and nails above, the inequality says that if we have two hydrogen atoms and an oxygen atom, then we can let them react to a molecule of water, but we don’t have to. In this sense, having two hydrogen atoms and an oxygen atom is at least as good as having a molecule of water.

So what’s going on here, mathematically? In all of the above equations, we have a bunch of stuff on each side and an inequality ‘\geq’ in between. The stuff on each side consists of a bunch of objects tacked together via ‘+’ . With respect to these two pieces of structure, the collection of all our resource objects forms an ordered commutative monoid:

Definition: An ordered commutative monoid is a set A equipped with a binary relation \geq, a binary operation +, and a distinguished element 0 such that the following hold:

+ and 0 equip A with the structure of a commutative monoid;

\geq equips A with the structure of a partially ordered set;

• addition is monotone: if x\geq y, then also x + z \geq y + z.

Here, the third axiom is the most important, since it tells us how the additive structure interacts with the ordering structure.

Ordered commutative monoids are the mathematical formalization of resource convertibility and combinability as follows. The elements x,y\in A are the resource objects, corresponding to the ‘collections of stuff’ in our earlier examples, such as x = \text{timber} + \text{nails} or y = 2 \text{H}_2 + \text{O}_2. Then the addition operation simply joins up collections of stuff into bigger collections of stuff. The ordering relation \geq is what formalizes resource convertibility, as in the examples above. The third axiom states that if we can convert x into y, then we can also convert x together with z into y together with z for any z, for example by doing nothing to z.

A mathematically minded reader might object that requiring A to form a partially ordered set under \geq is too strong a requirement, since it requires two resource objects to be equal as soon as they are mutually interconvertible: x \geq y and y \geq x implies x = y. However, I think that this is not an essential restriction, because we can regard this implication as the definition of equality: ‘x = y’ is just a shorthand notation for ‘x\geq y and y\geq x’ which formalizes the perfect interconvertibility of resource objects.

We could now go back to the original examples and try to model carpentry and chemistry in terms of ordered commutative monoids. But as a mathematician, I needed to start out with something mathematically precise and rigorous as a testing ground for the formalism. This helps ensure that the mathematics is sensible and useful before diving into real-world applications. So, the main example in my paper is the ordered commutative monoid of graphs, which has a resource-theoretic interpretation in terms of zero-error information theory. As graph theory is a difficult and traditional subject, this application constitutes the perfect training camp for the mathematics of ordered commutative monoids. I will get to this in Part 3.

In Part 2, I will say something about what one can do with ordered commutative monoids. In the meantime, I’d be curious to know what you think about what I’ve said so far!

Resource convertibility: part 2.

Resource convertibility: part 3.


Trends in Reaction Network Theory

27 January, 2015

For those who have been following the posts on reaction networks, this workshop should be interesting! I hope to see you there.

Workshop on Mathematical Trends in Reaction Network Theory, 1-3 July 2015, Department of Mathematical Sciences, University of Copenhagen. Organized by Elisenda Feliu and Carsten Wiuf.

Description

This workshop focuses on current and new trends in mathematical reaction network theory, which we consider broadly to be the theory describing the behaviour of systems of (bio)chemical reactions. In recent years, new interesting approaches using theory from dynamical systems, stochastics, algebra and beyond, have appeared. We aim to provide a forum for discussion of these new approaches and to bring together researchers from different communities.

Structure

The workshop starts in the morning of Wednesday, July 1st, and finishes at lunchtime on Friday, July 3rd. In the morning there will be invited talks, followed by contributed talks in the afternoon. There will be a reception and poster session Wednesday in the afternoon, and a conference dinner Thursday. For those participants staying Friday afternoon, a sightseeing event will be arranged.

Organization

The workshop is organized by the research group on Mathematics of Reaction Networks at the Department of Mathematical Sciences, University of Copenhagen. The event is sponsored by the Danish Research Council, the Department of Mathematical Sciences and the Dynamical Systems Interdisciplinary Network, which is part of the UCPH Excellence Programme for Interdisciplinary Research.

Confirmed invited speakers

Nikki Meskhat (North Carolina State University, US)

Alan D. Rendall (Johannes Gutenberg Universität Mainz, Germany)

• János Tóth (Budapest University of Technology and Economics, Hungary)

Sebastian Walcher (RWTH Aachen, Germany)

Gheorghe Craciun (University of Wisconsin, Madison, US)

David Doty (California Institute of Technology, US)

>

Manoj Gopalkrishnan (Tata Institute of Fundamental Research, India)

Michal Komorowski (Institute of Fundamental Technological Research, Polish Academy of Sciences, Poland)

John Baez (University of California, Riverside, US)

Important dates

Abstract submission for posters and contributed talks: March 15, 2015.

Notification of acceptance: March 26, 2015.

Registration deadline: May 15, 2015.

Conference: July 1-3, 2015.

The organizers

The organizers are Elisenda Feliu and Carsten Wiuf at the Department of Mathematical Sciences of the University of Copenhagen.

They’ve written some interesting papers on reaction networks, including some that discuss chemical reactions with more than one stationary state. This is a highly nonlinear regime that’s very important in biology:

• Elisenda Feliu and Carsten Wiuf, A computational method to preclude multistationarity in networks of interacting species, Bioinformatics 29 (2013), 2327-2334.

Motivation. Modeling and analysis of complex systems are important aspects of understanding systemic behavior. In the lack of detailed knowledge about a system, we often choose modeling equations out of convenience and search the (high-dimensional) parameter space randomly to learn about model properties. Qualitative modeling sidesteps the issue of choosing specific modeling equations and frees the inference from specific properties of the equations. We consider classes of ordinary differential equation (ODE) models arising from interactions of species/entities, such as (bio)chemical reaction networks or ecosystems. A class is defined by imposing mild assumptions on the interaction rates. In this framework, we investigate whether there can be multiple positive steady states in some ODE models in a given class.

Results. We have developed and implemented a method to decide whether any ODE model in a given class cannot have multiple steady states. The method runs efficiently on models of moderate size. We tested the method on a large set of models for gene silencing by sRNA interference and on two publicly available databases of biological models, KEGG and Biomodels. We recommend that this method is used as (i) a pre-screening step for selecting an appropriate model and (ii) for investigating the robustness of non-existence of multiple steady state for a given ODE model with respect to variation in interaction rates.

Availability and Implementation. Scripts and examples in Maple are available in the Supplementary Information.

• Elisenda Feliu, Injectivity, multiple zeros, and multistationarity in reaction networks, Proceedings of the Royal Society A.

Abstract. Polynomial dynamical systems are widely used to model and study real phenomena. In biochemistry, they are the preferred choice for modelling the concentration of chemical species in reaction networks with mass-action kinetics. These systems are typically parameterised by many (unknown) parameters. A goal is to understand how properties of the dynamical systems depend on the parameters. Qualitative properties relating to the behaviour of a dynamical system are locally inferred from the system at steady state. Here we focus on steady states that are the positive solutions to a parameterised system of generalised polynomial equations. In recent years, methods from computational algebra have been developed to understand these solutions, but our knowledge is limited: for example, we cannot efficiently decide how many positive solutions the system has as a function of the parameters. Even deciding whether there is one or more solutions is non-trivial. We present a new method, based on so-called injectivity, to preclude or assert that multiple positive solutions exist. The results apply to generalised polynomials and variables can be restricted to the linear, parameter-independent first integrals of the dynamical system. The method has been tested in a wide range of systems.

You can see more of their papers on their webpages.


Chemical Reaction Network Talks

26 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!

The first two are about ways one chemical reaction network can simulate another. This is important for a couple of reasons. First, in biology, a bunch of different chemical reactions can ‘accomplish the same task’—and we’d like to make this idea precise. That’s what Luca Cardelli spoke about. Second, people trying to do computation with chemistry are starting to simulate quite general reactions using DNA! That’s what Sheung Woo Shin spoke about.

Luca Cardelli

Luca Cardelli was at Oxford when I was visiting this spring, but unfortunately I didn’t meet him! He works for Microsoft on the interface of biology and computation. At Banff, he talked about ways one chemical reaction network can simulate another. His slides are here:

• Luca Cardelli, Morphisms of reaction networks.

He has a paper that gives a more detailed explanation of these ideas:

• Luca Cardelli, Morphisms of reaction networks that couple structure to function.

Here is my own disorganized explanation… with lots of informative but confusing digressions. A population protocol is a chemical reaction with only 2-in, 2-out reactions. For example, this paper presents a population protocol that does ‘approximate majority detection':

• Dana Angluin, James Aspnes, and David Eisenstat, A simple population protocol for fast robust approximate majority, Distributed Computing 21 (2008), 87–102.

What’s the idea? We start with two kinds of molecules, say x’s and y’s, and we want to see which one is in the majority, so we run these chemical reactions:

x + y \to x + b

x + y \to y + b

x + b \to 2x

y + b \to 2y

See? All the reactions have 2 molecules going in and 2 going out. The b molecules act as ‘undecided voters’ who become either an x or a y, depending on who they meet first.

If we start with about n molecules, in O(n \log n) time these reactions are very likely to convert all x’s and y’s to whatever kind of molecule was in the majority initially… at least if the gap in the number of x’s and y’s is big enough.

Here’s another population protocol that also does the job:

x + y \to 2b

x + b \to 2x

y + b \to 2y

And here’s a proof that one of these algorithms actually works—most of the time, when the initial difference in populations is big enough:

• Etienne Perron, Dinkar Vasudevan, and Milan Vojonvic, Using three states for binary consensus on complete graphs, Technical Report, MSR-TR-2008-114, Microsoft, September 2008.

If we use a discrete-time formalism to describe the dynamics, the proof seems to get harder. See the paper by Angluin, Aspnes, and Eisenstat for the only known proof!

Anyway, Luca Cardelli is interested in chemical reaction networks actually found in biology. This approximate majority algorithm is seen quite clearly in a certain biological system: a certain ‘epigenetic switch’. However, it is usually ‘obfuscated’ or ‘encrypted': hidden in a bigger, more complicated chemical reaction network. For example, see:

• Luca Cardelli and Attila Csikász-Nagy, The cell cycle switch is approximate majority obfuscated, Scientific Reports 2 (2012).

This got him interested in developing a theory of morphisms between reaction networks, which could answer questions like: When can one CRN emulate another? But these questions turn out to be much easier if we use the rate equation than with the master equation. So, he asks: when can one CRN give the same rate equation as another?

He found a sufficient condition that’s ‘purely syntactic': you can tell if it holds by looking at the reaction networks, regardless of the rate constants.

Here’s the idea. We say one network emulates another if for any rate constants of the second, we can find rate constants for the first that makes its rate equation have solutions exactly mimicking that of the second, but where several species in the first correspond to one in the second.

For this to make sense, we assume there is a map sending:

• species to species
• reactions to reactions

In a chemical reaction network homomorphism, the map on reactions is determined by the map on species in the obvious way. For example, if species A is sent to f(A) and species B is sent to f(B) then the reaction

2A + B \to 3B

is sent to the reaction

2f(A) + f(B) \to 3 f(B)

In this situation, to make the first network emulate the second, we need to set equal the initial concentrations of all species in the inverse image of a given species.

A reactant homomorphism from one chemical reaction network to another is more general: it sends species to species, and for any reaction in the first chemical reaction network with input

A + B + C \cdots

there’s a reaction in the second with input

f(A) + f(B) + f(C) + \cdots

(Reactant is another name for input.)

A stoichiomorphism is a kind of morphism that takes rate constants into account. See Cardelli’s paper for the definition.

The main theorem: given a stoichiomorphism from one chemical reaction network to another that’s also a reactant homomorphism, then the first emulates the second.

For a better explanation, read his paper! Here’s a cool picture from his paper showing a bunch of chemical reaction networks including the approximate majority network (labelled AM), many of which show up in biology, and morphisms between them:


Click to enlarge! These chemical reaction networks are drawn in a special style: as influence networks, consisting of ‘gates’ where process activates or deactivates another. Each gate is a chemical reaction network of a certain form, schematically like this:

\mathrm{off} \leftrightarrow \mathrm{intermediate} \leftrightarrow \mathrm{on}

where the forward reactions are catalyzed by one chemical and the reverse reactions are catalyzed by another. A gate is like a switch that can be turned on or off.

While listening to this talk, I thought the way in which one CRN emulates another in Cardelli’s formalism looks awfully similar to the way one dynamical system emulates another in Eugene Lerman’s formalism:

• Eugene Lerman, Networks of dynamical systems, Azimuth, 18 March 2014.

The following picture from Cardelli’s paper shows that one of his morphisms of reaction networks is like ‘covering map’. This reminds me a lot of what’s happening in Lerman’s work.


Again, click to enlarge!

Seung Woo Shin

Seung Woo Shin was actually Brendan Fong’s roommate at the National University of Singapore while Brendan was working with me on chemical reaction networks. Apparently they never talked about their work!

Shin spoke about some other concepts of ‘morphism’ between chemical reaction networks. These other concepts do not involve reaction rates, just which chemicals can turn into which. You can see his slides here:

• Seung Woo Shin, Verifying CRN implementations.

and read his thesis for more details:

• Seung Woo Shin, Compiling and verifying DNA-based chemical reaction network implementations, Masters thesis, Caltech, 2012.

Abstract: One goal of molecular programming and synthetic biology is to build chemical circuits that can control chemical processes at the molecular level. Remarkably, it has been shown that synthesized DNA molecules can be used to construct complex chemical circuits that operate without any enzyme or cellular component. However, designing DNA molecules at the individual nucleotide base level is often difficult and laborious, and thus chemical reaction networks (CRNs) have been proposed as a higher-level programming language. So far, several general-purpose schemes have been described for designing synthetic DNA molecules that simulate the behavior of arbitrary CRNs, and many more are being actively investigated.

Here, we solve two problems related to this topic. First, we present a general-purpose CRN-to-DNA compiler that can apply user-defined compilation schemes for translating formal CRNs to domain-level specifications for DNA molecules. In doing so, we develop a language in which such schemes can be concisely and precisely described. This compiler can greatly reduce the amount of tedious manual labor faced by researchers working in the field. Second, we present a general method for the formal verification of the correctness of such compilation. We first show that this problem reduces to testing a notion of behavioral equivalence between two CRNs, and then we construct a mathematical formalism in which that notion can be precisely defined. Finally, we provide algorithms for testing that notion. This verification process can be thought of as an equivalent of model checking in molecular computation, and we hope that the generality of our verification techniques will eventually allow us to apply them not only to DNA-based CRN implementations but to a wider class of molecular programs.

His thesis built on this earlier paper:

• David Soloveichik, Georg Seelig and Erik Winfree, DNA as a universal substrate for chemical kinetics, Proceedings of the National Academy of Sciences (2010).

I think this work is fascinating and deeply related to category theory, so I talked to Shin and Winfree about it, and this is what we came up with:

CRN equivalences: progress report.

This is one of several reports on progress people at the workshop made on various open problems.

David Anderson

Brendan Fong and I wrote about David Anderson’s work in Part 9 of the network theory series. It’s so impressive that I expected him to be older… older than me, I guess. He’s not!

In his tutorial, he gave an overview of chemical reaction networks with an emphasis on the deficiency zero theorem. Since many people were puzzled by the ‘deficiency’ concept, they asked lots of questions. But I’ve already explained that idea in Part 21. So, I’ll just mention a couple of cool theorems he told us about!

Theorem (Horn and Jackson). If a reaction network has a complex balanced equilibrium, then:

1. It has no equilibria that are not complex balanced.

2. The reaction network must be weakly reversible.

3. Every stochiometric compatibility class contains precisely one complex balanced equilibrium.

I should have known this, since this work is classic. But I don’t think I knew that the existence of one complex balanced equilibrium implied all this stuff!

He also mentioned this paper:

• Guy Shinar and Martin Feinberg, Structural sources of robustness in biochemical reaction networks, Science (2010).

which contains this amazing theorem:

Theorem (Shinar and Feinberg). Suppose there is a chemical reaction network such that:

1. its deficiency equals one;

2. it has a positive steady state;

3. it has two “non-terminal complexes” that differ only in one species S. (“Non-terminal” is a concept that’s easier to explain with a picture of a reaction network).

Then the species S is absolutely robust: with any initial conditions, the rate equation will approach an equilibrium where the concentration of S approaches a specific fixed value c, independent of the initial conditions!

However, things work very differently if we treat the system stochastically, using the master equation:

• David F. Anderson, German A. Enciso and Matthew D. Johnston, Stochastic analysis of biochemical reaction networks with absolute concentration robustness.

More

A lot more happened at this workshop! There was a huge amount of discussion of the leader election problem, which is about how to cook up chemical reactions that create a ‘leader': a single molecule of some sort.

Leader election: the problem, and references.

Leader election: progress report.

As I explained before, David Soloveichik talked about various forms of digital computation with chemical reaction networks. David Doty talked about the very important flip side of the coin: analog computation.

• David Doty, Rate-independent computation by real-valued chemistry.

There were also great talks by Lulu Qian and Erik Winfree, which I won’t try to summarize. Qian does a lot of work in the lab making things actually happen, so if you’re a practical sort this is the talk to look at:

• Lulu Qian, Implementing complex CRNs with modular DNA components.

All in all, a very stimulating workshop. The diversity of things one can ask about chemical reaction networks is quite exciting!


The Computational Power of Chemical Reaction Networks

10 June, 2014

I’m at this workshop:

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

Luca Cardelli wrote about computation with chemical reactions in Part 26 of the network theory series here on this blog. So, it’s nice to meet him and many other researchers, learn more, and try to solve some problems together!

The first tutorial was this:

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

David works at the Center for Systems and Synthetic Biology, and their website says:

David did his graduate work with Erik Winfree at Caltech, focusing on algorithmic self-assembly and on synthetic networks of nucleic-acid interactions based on strand displacement cascades. He is interested in “molecular programming”: the systematic design of complex molecular systems based on the principles of computer science and distributed computing. More generally, he is trying to create a theoretical foundation of chemical computation applicable to both synthetic and natural systems.

According to his webpage, Soloveichik’s research interests are:

Wet-lab: the rational design of molecular interactions for synthetic biology, nanotechnology, and bioengineering. The goal is to engineer autonomous molecular systems that can sense, compute, and perform various actions. Using nucleic-acid “strand displacement cascades” as the molecular primitive, we are able to attain freedom of design that hasn’t been previously possible.

Theory: The theoretical foundation of chemical computation. Once we have a way to program molecular interactions, what programming language shall we use? How molecules can process information and carry out computation is still not well-understood; however, a formal connection to models of concurrent computation may allow systematic and scalable design, rigorous analysis and verification. Further, computational principles may elucidate the design of biological regulatory networks.

Here are my notes on his tutorial.

Motivation

We’ve got people here from different backgrounds:

• computational complexity theory
• wetlab / experimental science
• pure and applied mathematics
• software verification

CRNs (chemical reaction networks) show up in:

• chemistry
• population biology
• sensor networks
• math:
    ○ vector addition systems
    ○ Petri nets
    ○ commutative semigroups
    ○ bounded context-free languages
    ○ uniform recurrence equations

Why use them for computation? People want to go beyond the von Neumann architecture for computation. People also want to understand how cells process information. However, with a few exceptions, the computational perspective in this talk has not yet proved relevant in biology. So, there is a lot left to learn.

The model

The model of computation here will be the master equation for a chemical reaction network… since this has been explained starting Part 4 of the network theory series, I won’t review it!

Can all chemical reaction networks, even those without any conservation laws, be realized by actual chemical systems?

Though this is a subtle question, one answer is “yes, using strand displacement cascades”. This is a trick for getting DNA to simulate other chemical reactions. It’s been carried out in the lab! See this paper and many subsequent ones:

• Soloveichik, Seelig and Winfree, DNA as a universal substrate for chemical kinetics.

Abstract: Molecular programming aims to systematically engineer molecular and chemical systems of autonomous function and ever-increasing complexity. A key goal is to develop embedded control circuitry within a chemical system to direct molecular events. Here we show that systems of DNA molecules can be constructed that closely approximate the dynamic behavior of arbitrary systems of coupled chemical reactions. By using strand displacement reactions as a primitive, we construct reaction cascades with effectively unimolecular and bimolecular kinetics. Our construction allows individual reactions to be coupled in arbitrary ways such that reactants can participate in multiple reactions simultaneously, reproducing the desired dynamical properties. Thus arbitrary systems of chemical equations can be compiled into real chemical systems. We illustrate our method on the Lotka–Volterra oscillator, a limit-cycle oscillator, a chaotic system, and systems implementing feedback digital logic and algorithmic behavior.

However, even working with the master equation for a CRN, there are various things we might mean by having it compute something:

• uniform vs non-uniform: is a single CRN supposed to handle all inputs, or do we allow adding extra reactions for larger inputs? It’s a bit like Turing machines vs Boolean circuits.

• deterministic vs probabilistic: is the correct output guaranteed or merely likely?

• halting vs stabilizing: does the CRN ‘know’ when it has finished, or not? In the ‘halting’ case the CRN irreversibly produces some molecules that signal that the computation is done. In the ‘stabilizing’ case, it eventually stabilizes to the right answer, but we may not know how long to wait.

These distinctions dramatically affect the computational power. In the case of uniform computation:

• deterministic and halting: this has finite computational power.

• deterministic and stabilizing: this can decide semilinear predicates.

• probabilistic and halting: this is Turing-universal.

• probabilistic and stabilizing: this can decide \Delta_2^0 predicates, which are more general than computable ones. (Indeed, if we use Turing machines but don’t require them to signal when they’ve halted, the resulting infinitely long computations can ‘compute’ stuff that’s not computable in the usual sense.)

Deterministic stabilizing computations

Let’s look at the deterministic stabilizing computations in a bit more detail. We’ll look at decision problems. We have a subset S \subseteq \mathbb{N}^d, and we want to answer this question: is the vector X \in \mathbb{N}^d in the set S?

To do this, we represent the vector as a bunch of molecules: X_1 of the first kind, X_2 of the second kind, and so on. We call this an input. We may also include a fixed collection of additional molecules in our input, to help the reactions run.

Then we choose a chemical reaction network, and we let it run on our input. The answer to our question will be encoded in some molecules called Y and N. If X is in S, we want our chemical reaction to produce Y molecules. If it’s not, we want our reaction to produce N’s.

To make this more precise, we need to define what counts as an output. If we’ve got a bunch of molecules that

• contains Y but not N: then the output is YES.

• contains N but not Y: then the output is NO.

Otherwise the output is undefined.

Output-stable states are states with YES or NO output such that all states reachable from them via our chemical reactions give the same output. We say an output-stable-state is correct if this output is the correct answer to the question: is X in S.

Our chemical reaction network gives a deterministic stabilizing computation if for any input, and choosing any state reachable from that input, we can do further chemical reactions to reach a correct output-stable state.

In other words: starting from our input, and letting the chemical reactions <run any way they want, we will eventually stabilize at an output that gives the right answer to the question “is X in S?”

Examples

This sounds a bit complicated, but it’s really not. Let’s look at some examples!

Example 1. Suppose you want to check two numbers and see if one is greater than or equal to another. Here

S = \{(X_1,X_2) : X_2 \ge X_1 \}

How can you decide if a pair of numbers (X_1,X_2) is in this set?

You start with X_1 molecules of type A, X_2 molecules of type B, and one molecule of type Y. Then you use a chemical reaction network with these reactions:

A + N \to Y
B + Y \to N

If you let these reactions run, the Y switches to a N each time the reactions destroy an A. But the N switches back to a Y each time the reactions destroy a B.

When no more reactions are possible, we are left with either one Y or one N, which is the correct answer to your question!

Example 2. Suppose you want to check two numbers and see if one is equal to another. Here

S = \{(X_1,X_2) : X_2 = X_1 \}

How can you decide if a pair of numbers (X_1,X_2) is in here?

This is a bit harder! As before, you start with X_1 molecules of type A, X_2 molecules of type B, and one molecule of type Y. Then you use a chemical reaction network with these reactions:

A + B \to Y
Y + N \to Y
A + Y \to A + N
B + Y \to B + N

The first reaction lets an A and a B cancel out, producing a Y. If you only run this reaction, you’ll eventually be left with either a bunch of A\mathrm{s} or a bunch of B\mathrm{s} or nothing but Y\mathrm{s}.

If you have Y\mathrm{s}, your numbers were equal. The other reactions deal with the cases where you have A\mathrm{s} or B\mathrm{s} left over. But the key thing to check is that no matter what order we run the reactions, we’ll eventually get the right answer! In the end, you’ll have either Y\mathrm{s} or N\mathrm{s}, not both, and this will provide the yes-or-no answer to the question of whether X_1 = X_2.

What deterministic stabilizing computations can do

We’ve looked at some examples of deterministic stabilizing computations. The big question is: what kind of questions can they answer?

More precisely, for what subsets A \subseteq \mathbb{N}^d can we build a deterministic stabilizing computation that ends with output YES if the input X lies in A and with output NO otherwise?

The answer is: the ‘semilinear’ subsets!

• Dana Angluin, James Aspnes and David Eistenstat, Stably computable predicates are semilinear.

A set S \subseteq \mathbb{N}^d is linear if it’s of the form

\{u_0 + n_1 u_1 + \cdots + n_p u_p : n_i \in \mathbb{N}  \}

for some fixed vectors of natural numbers u_i \in \mathbb{N}^d.

A set S \subseteq \mathbb{N}^d semilinear if it’s a finite union of linear sets.

How did Angluin, Aspnes and Eisenstat prove their theorem? Apparently the easy part is showing that membership in any semilinear set can be decided by a chemical reaction network. David sketched the proof of the converse. I won’t go into it, but it used a very nice fact:

Dickson’s Lemma. Any subset of \mathbb{N}^d has a finite set of minimal elements, where we define x \le y if x_i \le y_i for all i.

For example, the region above and to the right of the hyperbola here has five minimal elements:

If you know some algebra, Dickson’s lemma should remind you of the Hilbert basis theorem, saying (for example) that every ideal in a ring of multivariable polynomials over a field is finitely generated. And in fact, Paul Gordan used Dickson’s Lemma in 1899 to help give a proof of Hilbert’s basis theorem.

It’s very neat to see how this lemma applies to chemical reaction networks! You can see how it works in Angluin, Aspnes and Eistenstat’s paper. But they call it “Higman’s lemma” for some reason.

References

Here are some of David Soloveichik’s recent talks:

• An introduction to strand displacement cascades for the Foresight Institute Conference (Palo Alto, Jan 2013): An artificial “biochemistry” with DNA.

• Paper presented at DNA Computing and Molecular Programming 18 (Aarhus, Denmark, Aug 2012): Deterministic function computation with chemical reaction networks.

• Tutorial talk for DNA Computing and Molecular Programming 17 (Pasadena, Aug 2011): The programming language of chemical kinetics, and how to discipline your DNA molecules using strand displacement cascades.

• High-level introduction to algorithmic self-assembly and stochastic chemical reaction networks as computer-theoretic models: Computer-theoretic abstractions for molecular programming.

• On algorithmic behavior in chemical reaction networks and implementing arbitrary chemical reaction networks with DNA: programming well-mixed chemical kinetics.


Programming with Chemical Reaction Networks

23 March, 2014

 

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.


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