A Networked World (Part 3)

guest post by David Spivak

• Part 1: The problem.

• Part 2: Creating a knowledge network.

From parts to wholes

Remember where we were. Ologs, linguistically-enhanced sketches, just weren’t doing justice to the idea that each step in a recipe is itself a recipe. But the idea seemed ripe for mathematical formulation.

Thus, I returned to a question I’d wondered about in the very beginning: how is macro-understanding built from micro-understanding? How can multiple individual humans come together, like cells in a multicellular organism, to make a whole that is itself a surviving decision-maker?

There were, and continue to be, a lot of “open-to-Spivak” questions one can ask: How are stories about events built from sub-stories about sub-events? How is macro-economics built from micro-economics? Are large-scale phenomena always based on, and relatable to, interactions between smaller-scale phenomena? For example, I still want to understand, in very basic terms, how classical (large-scale) phenomena are a manifestation of quantum phenomena.

Neuroscience professor Michael Gazzaniga has a similar question: How does cognition arise from the interaction of tiny event-noticers, and how does society emerge and effect individual brains? As put it in the last paragraph of his book Who’s In Charge, we are in need of a language by which to understand the interfaces of “our layered hierarchical existence”, because doing so “holds the answer to our quest for understanding mind/brain relationships.” He goes on:

Understanding how to develop a vocabulary for those layered interactions, for me, constitutes the scientific problem of this century.

I tend to be infatuated with this same kind of idea: cognition emerging from interactions between sub-cognitive pieces. This is what got me interested in what I now call “operadic modularity”. Luckily again, my Office of Naval Research hero (now at the Air Force Office of Scientific Research) granted me a chance to study it.

The idea is this: modularity is about arranging many modules into a single whole, which is another module, usable as part of a larger system. Each system of modules is a module of its own, and we see the nesting phenomenon. Operads can model the language of nestable interface arrangements, and their algebras can model how interfaces are filled in with the required sorts of modules.

Here, by operad, I mean symmetric colored operad, or symmetric multicategory. Operads are like categories—they have objects, morphisms, identities, and a unital and associative composition formula—the difference is that the domain of a morphism is a finite set of objects (in an operad) rather than a single object (as in a category). So morphisms in an operad are written like we call such a morphism n-ary.

An early example, formulated operadically by Peter May (the inventor of operads) is called the little 2-cubes operad, denoted It has only one object, say a square ⬜, and an n-ary morphism

⬜ ,…, ⬜ ⬜

is any arrangement of non-overlapping squares in a larger square. These arrangements clearly display a nesting property.

Another source of examples comes from the fact that every monoidal category has an underlying operad with

(Either was symmetric monoidal to begin with or you can add in symmetries, roughly by multiplying each hom-set by ) The operad underlying the cartesian monoidal category of sets is an example I’ll use later.

If you want to think about operads as modeling modularity—building one thing out of many—the first trick is to imagine the codomain object as the exterior and all the domain objects as sitting inside it, on the interior. May’s little 2-cubes operad gives the idea: squares in a square. From now on, if I speak of many little objects arranged inside one big object, I always mean it this way: the interior objects constitute the domain, the exterior object is the codomain, and the arrangement itself is the morphism. These arrangements can be nested inside one another, corresponding to composition in the operad.

What are other types of nested phenomena, which we might be able to think about operadically? How about circles wired together in a larger circle? An object in this operad is a circle with some number of wires sticking out; let’s call it a ported-circle. A morphism from n-many ported-circles to one ported-circle is any connection pattern involving—i.e., wiring together of—the ports. This description can be interpreted in a few different ways; I usually mean the underlying operad of the monoidal category of “sets and cospans under disjoint union”, but the “spaghetti and meatballs operad” of circular planar arc diagrams is another good interpretation.

Once you have an operad you have a kind of calculus for nestable arrangements. As I’ve been saying, I often think of the morphisms in an operad in terms of pictures, such as wiring diagrams or squares-in-a-square. If you say you want these pictures to “mean something”, you’re probably looking for an algebra This operad functor which acts like a lax functor between monoidal categories, would tell you the set of fillers or fills that can be placed into each object in the picture.

I often think of the operad as a picture language, and the -algebra its intended semantics. Not only does such a set-valued functor on give you a set of fills for each object , it would also give you a formula for producing a large-scale fill (element of ) from any arrangement of small-scale fills (element of ).

For example, given a pointed space , you can ask for the set of based 2-spheres

⬜

in it. Here, is any element of ⬜ Think of a based sphere in as a continuous map from the filled-in square to that sends the boundary of the square to the basepoint of Given n spheres in an arrangement of non-overlapping squares in a square prescribes a new based sphere ⬜ The idea is that you send all the unused space in the big exterior square to the basepoint of , and follow the instructions when you get to the th little square inside. Thus any “2-fold loop space” gives an algebra of May’s little 2-cubes operad.

So recently, I’ve been thinking a lot about operadic modularity, i.e., cases in which a thing can be built out of a bunch of simpler things. Note that not all cases of “nesting” have such a clear picture language. For example, context-free grammars are modular: you build [postal-address] out of [name-part], [street-address] and [zip-part], you build each of these, e.g., [name-part], in any of several ways (there is an optional suffix part and the option to abbreviate your first name using an initial). The point is, you build things out of smaller parts, nested inside still smaller parts. Seeing context-free grammars as free operads is one of the things Hermida, Makkai, and Power explained in their paper on higher dimensional multigraphs.

The operadic notion of modularity can also be applied to building hierarchical protein materials. Like context-free grammars, the operad of such materials doesn’t come with a nice picture language. However, it can be formalized as an operad nonetheless. That is, there is a grammar of actions that one can apply to a bunch of polypeptides, actions such as “attach”, “overlay”, “rigidMotion”, “helix”, “makeArray”. From these you can build proteins that are quite complex from a simple vocabulary of 20 amino acids. I’ve joined forces with Tristan Giesa and Ravi Jagadeesan to make such a program. The software package, called Matriarch, for “Materi-als Arch-itecture”, should be available soon as an open source Python library.

There are lots of operads whose morphisms look like string diagrams of various sorts. These operads, which generalize a set-theoretic version of May’s topological little 2-cubes, have clear picture languages. The algebras on such “visualizable” operads can model things like databases and dynamical systems. Over the past year or so, I’ve been writing a series of “worked example” papers, such as those above, in which I explain various picture languages and semantics for them.

I find that operads provide a nice language in which to discuss string diagrams of various sorts. String-diagrammatic languages exist for many different “doctrines”, such as categories, categories without identities, monoidal categories, cartesian monoidal categories, traced monoidal categories, operads, etc. For example, Dylan Rupel and I realized that traced monoidal categories are (well, if you have enough equipment and an expert like Patrick Schultz around) algebras on the operad of oriented 1-cobordisms. It seems to me that the other doctrines above are similarly associated to operads that are “nearby” Cob, e.g., sub-operads of Cob, operads under Cob, etc. Maps between these various operads should induce known adjunctions between the corresponding doctrines.

That brings us to present day. There will be a workshop in Turin in a couple of months, and I think it’ll be a lot of fun:

• Categorical Foundations of Network Theory, May 25-28, ISI Foundation, Turin, Italy.

I’m looking forward to hearing from John Baez, Jacob Biamonte, Eugene Lerman, Tobias Fritz and others, about what they’ve been thinking about recently. I think we’ll find interesting common ground. If there’s interest, I’d be happy to talk about categorical models for how information is communicated throughout a network, and whether this gives any insight that can lead to better decision-making by the larger whole.

A Networked World (Part 2)

guest post by David Spivak

• Part 1: The problem.

Creating a knowledge network

In 2007, I asked myself: as mathematically as possible, what can formally ground meaningful information, including both its successful communication and its role in decision-making? I believed that category theory could be useful in formalizing the type of object that we call information, and the type of relationship that we call communication.

Over the next few years, I worked on this project. I tried to understand what information is, how it is stored, and how it can be transferred between entities that think differently. Since databases store information, I wanted to understand databases category-theoretically. I eventually decided that databases are basically just categories corresponding to a collection of meaningful concepts and connections between them, and that these categories are equipped with functors Such a functor assigns to each meaningful concept a set of examples and connects them as dictated by the morphisms of I later found out that this “databases as categories” idea was not original; it is due to Rosebrugh and others. My view on the subject has matured a bit since then, but I still like this basic conception of databases.

If we model a person’s knowledge as a database (interconnected tables of examples of things and relationships), then the network of knowledgeable humans could be conceptualized as a simplicial complex equipped with a sheaf of databases. Here, a vertex represents an individual, with her database of knowledge. An edge represents a pair of individuals and a common ground database relating their individual databases. For example, you and your brother have a database of concepts and examples from your history. The common-ground database is like the intersection of the two databases, but it could be smaller (if the people don’t yet know they agree on something). In a simplicial complex, there are not only vertices and edges, but also triangles (and so on). These would represent databases held in common between three or more people.

I wanted “regular people” to actually make such a knowledge network, i.e., to share their ideas in the form of categories and link them together with functors. Of course, most people don’t know categories and functors, so I thought I’d make things easier for them by equipping categories with linguistic structures: text boxes for objects, labeled arrows for morphisms. For example, “a person has a mother” would be a morphism from the “person” object, to the “mother” object. I called such a linguistic category an olog, playing on the word blog. The idea (originally inspired during a conversation with my friend Ralph Hutchison) was that I wanted people, especially scientists, to blog their ontologies, i.e., to write “onto-logs” like others make web-logs.

Ologs codify knowledge. They are like concept webs, except with more rules that allow them to simultaneously serve as database schemas. By introducing ologs, I hoped I could get real people to upload their ideas into what is now called the cloud, and make the necessary knowledge network. I tried to write my papers to engage an audience of intelligent lay-people rather than for an audience of mathematicians. It was a risk, but to me it was the only honest approach to the larger endeavor.

(For students who might want to try going out on a limb like this, you should know that I was offered zero jobs after my first postdoc at University of Oregon. The risk was indeed risky, and one has to be ok with that. I personally happened to be the beneficiary of good luck and was offered a grant, out of the clear blue sky, by a former PhD in algebraic geometry, who worked at the Office of Naval Research at the time. That, plus the helping hands of Haynes Miller and many other brilliant and wonderful people, can explain how I lived to tell the tale.)

So here’s how the simplicial complex of ologs would ideally help humanity steer. Suppose we say that in order for one person to learn from another, the two need to find a common language and align some ideas. This kind of (usually tacit) agreement on, or alignment of, an initial common-ground vocabulary and concept-set is important to get their communication onto a proper footing.

For two vertices in such a simplicial network, the richer their common-ground olog (i.e., the database corresponding to the edge between them) is, the more quickly and accurately the vertices can share new ideas. As ideas are shared over a simplex, all participating databases can be updated, hence making the communication between them richer. In around 2010, Mathieu Anel and I worked out a formal way this might occur; however, we have not yet written it up. The basic idea can be found here.

In this setup, the simplicial complex of human knowledge should grow organically. Scientists, business people, and other people might find benefit in ologging their ideas and conceptions, and using them to learn from their peers. I imagined a network organizing itself, where simplices of like-minded people could share information with neighboring groups across common faces.

I later wrote a book called Category Theory for the Sciences, available free online, to help scientists learn how category theory could apply to familiar situations like taxonomies, graphs, and symmetries. Category theory, simply explained, becomes a wonderful key to the whole world of pure mathematics. It’s the closest thing we have to a universal language of thought, and therefore an appropriate language for forming connections.

My working hypothesis for the knowledge network was this. The information held by people whose worldview is more true—more accurate—would have better predictive power, i.e., better results. This is by definition: I define ones knowledge to be accurate as the extent to which, when he uses this knowledge to direct his actions, he has good luck handling his worldly affairs. As Louis Pasteur said, “Luck favors the prepared mind.” It follows that if someone has a track record of success, others will value finding broad connections into his olog. However, to link up with someone you must find a part of your olog that aligns with his—a functorial connection—and you can only receive meaningful information from him to the extent that you’ve found such common ground.

Thus, people who like to live in fiction worlds would find it difficult to connect, except to other like-minded “Obama’s a Christian”-type people. To the extent you are imbedded in a fictional—less accurate, less predictive—part of the network, you will find it difficult to establish functorial connections to regions of more accurate knowledge, and therefore you can’t benefit from the predictive and conceptual value of this knowledge.

In other words, people would be naturally inclined to try to align their understanding with people that are better informed. I felt hope that this kind of idea could lead to a system in which honesty and accuracy were naturally rewarded. At the very least, those who used it could share information much more effectively than they do now. This was my plan; I just had to make it real.

I had a fun idea for publicizing ologs. The year was in 2008, and I remember thinking it would be fantastic if I could olog the political platform and worldview of Barack Obama and of Sarah Palin. I wished I could sit down with them and other politicians and help them write ologs about what they believed and wanted for the country. I imagined that some politicians might have ologs that look like a bunch of disconnected text boxes—like a brain with neurons but no synapses—a collection of talking points but no real substantive ideas.

Anyway, there I was, trying to understand everything this way: all information was categories (or perhaps sketches) and presheaves. I would work with interested people from any academic discipline, such as materials science, to make ologs about whatever information they wanted to record category-theoretically. Ologs weren’t a theory of everything, but instead, as Jack Morava put it, a theory of anything.

One day I was working on a categorical sketch to model processes within processes, but somehow it really wasn’t working properly. The idea was simple: each step in a recipe is a mini-recipe of its own. Like chopping the carrots means getting out a knife and cutting board, putting a carrot on there, and bringing the knife down successively along it. You can keep zooming into any of these and see it as its own process. So there is some kind of nested, fractal-like behavior here. The olog I made could model the idea of steps in a recipe, but I found it difficult to encode the fact that each step was itself a recipe.

This nesting thing seemed like an idea that mathematics should treat beautifully, and ologs weren’t doing it justice. It was then when I finally admitted that there might be other fish in the mathematical sea.

• Part 3: From parts to wholes.

A Networked World (Part 1)

guest post by David Spivak

The problem

The idea that’s haunted me, and motivated me, for the past seven years or so came to me while reading a book called The Moment of Complexity: our Emerging Network Culture, by Mark C. Taylor. It was a fascinating book about how our world is becoming increasingly networked—wired up and connected—and that this is leading to a dramatic increase in complexity. I’m not sure if it was stated explicitly there, but I got the idea that with the advent of the World Wide Web in 1991, a new neural network had been born. The lights had been turned on, and planet earth now had a brain.

I wondered how far this idea could be pushed. Is the world alive, is it a single living thing? If it is, in the sense I meant, then its primary job is to survive, and to survive it’ll have to make decisions. So there I was in my living room thinking, “oh my god, we’ve got to steer this thing!”

Taylor pointed out that as complexity increases, it’ll become harder to make sense of what’s going on in the world. That seemed to me like a big problem on the horizon, because in order to make good decisions, we need to have a good grasp on what’s occurring. I became obsessed with the idea of helping my species through this time of unprecedented complexity. I wanted to understand what was needed in order to help humanity make good decisions.

What seemed important as a first step is that we humans need to unify our understanding—to come to agreement—on matters of fact. For example, humanity still doesn’t know whether global warming is happening. Sure almost all credible scientists have agreed that it is happening, but does that steer money into programs that will slow it or mitigate its effects? This isn’t an issue of what course to take to solve a given problem; it’s about whether the problem even exists! It’s like when people were talking about Obama being a Muslim, born in Kenya, etc., and some people were denying it, saying he was born in Hawaii. If that’s true, why did he repeatedly refuse to show his birth certificate?

It is important, as a first step, to improve the extent to which we agree on the most obvious facts. This kind of “sanity check” is a necessary foundation for discussions about what course we should take. If we want to steer the ship, we have to make committed choices, like “we’re turning left now,” and we need to do so as a group. That is, there needs to be some amount of agreement about the way we should steer, so we’re not fighting ourselves.

Luckily there are a many cases of a group that needs to, and is able to, steer itself as a whole. For example as a human, my neural brain works with my cells to steer my body. Similarly, corporations steer themselves based on boards of directors, and based on flows of information, which run bureaucratically and/or informally between different parts of the company. Note that in neither case is there any suggestion that each part—cell, employee, or corporate entity—is “rational”; they’re all just doing their thing. What we do see in these cases is that the group members work together in a context where information and internal agreement is valued and often attained.

It seemed to me that intelligent, group-directed steering is possible. It does occur. But what’s the mechanism by which it happens, and how can we think about it? I figured that the way we steer, i.e., make decisions, is by using information.

I should be clear: whenever I say information, I never mean it “in the sense of Claude Shannon”. As beautiful as Shannon’s notion of information is, he’s not talking about the kind of information I mean. He explicitly said in his seminal paper that information in his sense is not concerned with meaning:

Frequently the messages have meaning; that is they refer to or are correlated according to some system with certain physical or conceptual entities. These semantic aspects of communication are irrelevant to the engineering problem. The significant aspect is that the actual message is one selected from a set of possible messages.

In contrast, I’m interested in the semantic stuff, which flows between humans, and which makes possible decisions about things like climate change. Shannon invented a very useful quantitative measure of meaningless probability distributions.

That’s not the kind of information I’m talking about. When I say “I want to know what information is”, I’m saying I want to formulate the notion of human-usable semantic meaning, in as mathematical a way as possible.

Back to my problem: we need to steer the ship, and to do so we need to use information properly. Unfortunately, I had no idea what information is, nor how it’s used to make decisions (let alone to make good ones), nor how it’s obtained from our interaction with the world. Moreover, I didn’t have a clue how the minute information-handling at the micro-level, e.g., done by cells inside a body or employees inside a corporation, would yield information-handling at the macro (body or corporate) level.

I set out to try to understand what information is and how it can be communicated. What kind of stuff is information? It seems to follow rules: facts can be put together to form new facts, but only in certain ways. I was once explaining this idea to Dan Kan, and he agreed saying, “Yes, information is inherently a combinatorial affair.” What is the combinatorics of information?

Communication is similarly difficult to understand, once you dig into it. For example, my brain somehow enables me to use information and so does yours. But our brains are wired up in personal and ad hoc ways, when you look closely, a bit like a fingerprint or retinal scan. I found it fascinating that two highly personalized semantic networks could interface well enough to effectively collaborate.

There are two issues that I wanted to understand, and by to understand I mean to make mathematical to my own satisfaction. The first is what information is, as structured stuff, and what communication is, as a transfer of structured stuff. The second is how communication at micro-levels can create, or be, understanding at macro-levels, i.e., how a group can steer as a singleton.

Looking back on this endeavor now, I remain concerned. Things are getting increasingly complex, in the sorts of ways predicted by Mark C. Taylor in his book, and we seem to be losing some control: of the NSA, of privacy, of people 3D printing guns or germs, of drones, of big financial institutions, etc.

Can we expect or hope that our species as a whole will make decisions that are healthy, like keeping the temperature down, given the information we have available? Are we in the driver’s seat, or is our ship currently in the process of spiraling out of our control?

Let’s assume that we don’t want to panic but that we do want to participate in helping the human community to make appropriate decisions. A possible first step could be to formalize the notion of “using information well”. If we could do this rigorously, it would go a long way toward helping humanity get onto a healthy course. Further, mathematics is one of humanity’s best inventions. Using this tool to improve our ability to use information properly is a non-partisan approach to addressing the issue. It’s not about fighting, it’s about figuring out what’s happening, and weighing all our options in an informed way.

So, I ask: What kind of mathematics might serve as a formal ground for the notion of meaningful information, including both its successful communication and its role in decision-making?

• Part 2: Creating a knowledge network.

• Part 3: From parts to wholes.

Higher-Dimensional Rewriting in Warsaw (Part 1)

This summer there will be a conference on higher-dimensional algebra and rewrite rules in Warsaw. They want people to submit papers! I’ll give a talk about presentations of symmetric monoidal categories that arise in electrical engineering and control theory. This is part of the network theory program, which we talk about so often here on Azimuth.

There should also be interesting talks about combinatorial algebra, homotopical aspects of rewriting theory, and more:

• Higher-Dimensional Rewriting and Applications, 28-29 June 2015, Warsaw, Poland. Co-located with the RDP, RTA and TLCA conferences. Organized by Yves Guiraud, Philippe Malbos and Samuel Mimram.

Description

Over recent years, rewriting methods have been generalized from strings and terms to richer algebraic structures such as operads, monoidal categories, and more generally higher dimensional categories. These extensions of rewriting fit in the general scope of higher-dimensional rewriting theory, which has emerged as a unifying algebraic framework. This approach allows one to perform homotopical and homological analysis of rewriting systems (Squier theory). It also provides new computational methods in combinatorial algebra (Artin-Tits monoids, Coxeter and Garside structures), in homotopical and homological algebra (construction of cofibrant replacements, Koszulness property). The workshop is open to all topics concerning higher-dimensional generalizations and applications of rewriting theory, including

• higher-dimensional rewriting: polygraphs / computads, higher-dimensional generalizations of string/term/graph rewriting systems, etc.

• homotopical invariants of rewriting systems: homotopical and homological finiteness properties, Squier theory, algebraic Morse theory, coherence results in algebra and higher-dimensional category theory, etc.

• linear rewriting: presentations and resolutions of algebras and operads, Gröbner bases and generalizations, homotopy and homology of algebras and operads, Koszul duality theory, etc.

• applications of higher-dimensional and linear rewriting and their interactions with other fields: calculi for quantum computations, algebraic lambda-calculi, proof nets, topological models for concurrency, homotopy type theory, combinatorial group theory, etc.

• implementations: the workshop will also be interested in implementation issues in higher-dimensional rewriting and will allow demonstrations of prototypes of existing and new tools in higher-dimensional rewriting.

Submitting

Important dates:

• Submission: April 15, 2015

• Notification: May 6, 2015

• Final version: May 20, 2015

• Conference: 28-29 June, 2015

Submissions should consist of an extended abstract, approximately 5 pages long, in standard article format, in PDF. The page for uploading those is here. The accepted extended abstracts will be made available electronically before the
workshop.

Organizers

Program committee:

• Vladimir Dotsenko (Trinity College, Dublin)

• Yves Guiraud (INRIA / Université Paris 7)

• Jean-Pierre Jouannaud (École Polytechnique)

• Philippe Malbos (Université Claude Bernard Lyon 1)

• Paul-André Melliès (Université Paris 7)

• Samuel Mimram (École Polytechnique)

• Tim Porter (University of Wales, Bangor)

• Femke van Raamsdonk (VU University, Amsterdam)

Trends in Reaction Network Theory (Part 1)

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.

Network Theory Seminar (Part 4)

 

Since I was in Banff, my student Franciscus Rebro took over this week and explained more about cospan categories. These are a tool for constructing categories where the morphisms are networks such as electrical circuit diagrams, signal flow diagrams, Markov processes and the like. For some more details see:

• John Baez and Brendan Fong, A compositional framework for passive linear networks.

Cospan categories are really best thought of as bicategories, and Franciscus gets into this aspect too.

Network Theory (Part 33)

Last time I came close to describing the ‘black box functor’, which takes an electrical circuit made of resistors

and sends it to its behavior as viewed from outside. From outside, all you can see is the relation between currents and potentials at the ‘terminals’—the little bits of wire that poke out of the black box:

I came close to defining the black box functor, but I didn’t quite make it! This time let’s finish the job.

The categories in question

The black box functor

goes from the category where morphisms are circuits made of resistors, to the category where morphisms are linear relations. Let me remind you how these categories work, and introduce a bit of new notation.

Here is the category

• an object is a finite set;

• a morphism from to is an isomorphism class of cospans

in the category of graphs with edges labelled by resistances: numbers in Here we think of the finite sets and as graphs with no edges. We call the set of inputs and the set of outputs.

• we compose morphisms in by composing isomorphism classes of cospans.

And here is the category

• an object is a finite-dimensional real vector space;

• a morphism from to is a linear relation meaning a linear subspace

• we compose a linear relation and a linear relation in the usual way we compose relations, getting:

In case you’re wondering: I’ve just introduced the wiggly arrow notation

for a linear relation from to because it suggests that a relation is a bit like a function but more general. Indeed, a function is a special case of a relation, and composing functions is a special case of composing relations.

The black box functor

Now, how do we define the black box functor?

Defining it on objects is easy. An object of is a finite set and we define

The idea is that could be a set of inputs or outputs, and then

is a list of numbers: the potentials and currents at those inputs or outputs.

So, the interesting part is defining the black box functor on morphisms!

For this we start with a morphism in :

The labelled graph consists of:

• a set of nodes,

• a set of edges,

• maps sending each edge to its source and target,

• a map sending each edge to its resistance.

The cospan gives maps

These say how the inputs and outputs are interpreted as nodes in the circuit. We’ll call the nodes that come from inputs or outputs ‘terminals’. So, mathematically,

is the set of terminals: the union of the images of and

In the simplest case, the maps and are one-to-one, with disjoint ranges. Then each terminal either comes from a single input, or a single output, but not both! This is a good picture to keep in mind. But some subtleties arise when we leave this simplest case and consider other cases.

Now, the black box functor is supposed to send our circuit to a linear relation. I’ll call the circuit for short, though it’s really the whole cospan

So, our black box functor is supposed to send this circuit to a linear relation

This is a relation between the potentials and currents at the input terminals and the potentials and currents at the output terminals! How is it defined?

I’ll start by outlining how this works.

First, our circuit picks out a subspace

This is the subspace of allowed potentials and currents on the terminals. I’ll explain this and why it’s called a bit later. Briefly, it comes from the principle of minimum power, described last time.

Then, the map

gives a linear relation

This says how the potentials and currents at the inputs are related to those at the terminals. Similarly, the map

gives a linear relation

This says how the potentials and currents at the outputs are related to those at the terminals.

Next, we can ‘turn around’ any linear relation

to get a relation

defined by

Here we are just switching the input and output potentials, but when we switch the currents we also throw in a minus sign. The reason is that we care about the current flowing in to an input, but out of an output.

Finally, one more trick: given a linear subspace

of a vector space we get a linear relation

called the identity restricted to , defined like this:

If is all of this relation is actually the identity function on Otherwise it’s a partially defined function that’s defined only on and is the identity there. (A partially defined function is an example of a relation.) My notation is probably bad, but I don’t know a better one, so bear with me.

Let’s use all these ideas to define

To do this, we compose three linear relations:

1) We start with

2) We compose this with

3) Then we compose this with

Note that:

1) says how the potentials and currents at the inputs are related to those at the terminals,

2) picks out which potentials and currents at the terminals are actually allowed, and

3) says how the potentials and currents at the terminals are related to those at the outputs.

So, I hope all makes sense, at least in some rough way. In brief, here’s the formula:

Now I just need to fill in some details. First, how do we define and They work exactly the same way, by ‘copying potentials and adding currents’, so I’ll just talk about one. Second, how do we define the subspace This uses the principle of minimum power.

Duplicating potentials and adding currents

Any function between finite sets

gives a linear map

Mathematicians call this linear map the pullback along and for any it’s defined by

In our application, we think of as a list of potentials at terminals. The function could map a bunch of inputs to the same terminal, and the above formula says the potential at this terminal gives the potential at all those inputs. So, we are copying potentials.

We also get a linear map going the other way:

Mathematicians call this the pushforward along and for any it’s defined by

In our application, we think of as a list of currents entering at some inputs. The function could map a bunch of inputs to the same terminal, and the above formula says the current at this terminal is the sum of the currents at all those inputs. So, we are adding currents.

Putting these together, our map

gives a linear relation

where the pair is related to the pair iff

and

So, here’s the rule of thumb when attaching the points of to the input terminals of our circuit: copy potentials, but add up currents. More formally:

The principle of minimum power

Finally, how does our circuit define a subspace

of allowed potential-current pairs at the terminals? The trick is to use the ideas we discussed last time. If we know the potential at all nodes of our circuit, say , we know the power used by the circuit:

We saw last time that if we fix the potentials at the terminals, the circuit will choose potentials at the other nodes to minimize this power. We can describe the potential at the terminals by

So, the power for a given potential at the terminals is

Actually this is half the power: I stuck in a factor of 1/2 for some reason we’ll soon see. This is a quadratic function

so its derivative is linear. And, our work last time showed something interesting: to compute the current flowing into a terminal we just differentiate with respect to the potential at that terminal:

This is the reason for the 1/2: when we take the derivative of we bring down a 2 from differentiating all those squares, and to make that go away we need a 1/2.

The space of allowed potential-current pairs at the terminals is thus the linear subspace

And this completes our precise description of the black box functor!

The hard part is this:

Theorem. is a functor.

In other words, we have to prove that it preserves composition:

For that, read our paper:

• John Baez and Brendan Fong, A compositional framework for passive linear networks.