Network Theory (Part 24)

Now we’ve reached the climax of our story so far: we’re ready to prove the deficiency zero theorem. First let’s talk about it informally a bit. Then we’ll review the notation, and then—hang on to your seat!—we’ll give the proof.

The crucial trick is to relate a bunch of chemical reactions, described by a ‘reaction network’ like this:

to a simpler problem where a system randomly hops between states arranged in the same pattern:

This is sort of amazing, because we’ve thrown out lots of detail. It’s also amazing because this simpler problem is linear. In the original problem, the chance that a reaction turns a B + E into a D is proportional to the number of B’s times the number of E’s. That’s nonlinear! But in the simplified problem, the chance that your system hops from state 4 to state 3 is just proportional to the probability that it’s in state 4 to begin with. That’s linear.

The wonderful thing is that, at least under some conditions, we can find equilibrium solutions of our original problem starting from equilibrium solutions of the simpler problem.

Let’s roughly sketch how it works, and where we are so far. Our simplified problem is described by an equation like this:

\displaystyle{ \frac{d}{d t} \psi = H \psi }

where \psi is a function that the probability of being in each state, and H describes the probability per time of hopping from one state to another. We can easily understand quite a lot about the equilibrium solutions, where \psi doesn’t change at all:

H \psi = 0

because this is a linear equation. We did this in Part 23. Of course, when I say ‘easily’, that’s a relative thing: we needed to use the Perron–Frobenius theorem, which Jacob introduced in Part 20. But that’s a well-known theorem in linear algebra, and it’s easy to apply here.

In Part 22, we saw that the original problem was described by an equation like this, called the ‘rate equation’:

\displaystyle{ \frac{d x}{d t} = Y H x^Y  }

Here x is a vector whose entries describe the amount of each kind of chemical: the amount of A’s, the amount of B’s, and so on. The matrix H is the same as in the simplified problem, but Y is a matrix that says how many times each chemical shows up in each spot in our reaction network:

The key thing to notice is x^Y, where we take a vector and raise it to the power of a matrix. We explained this operation back in Part 22. It’s this operation that says how many B + E pairs we have, for example, given the number of B’s and the number of E’s. It’s this that makes the rate equation nonlinear.

Now, we’re looking for equilibrium solutions of the rate equation, where the rate of change is zero:

Y H x^Y = 0

But in fact we’ll do even better! We’ll find solutions of this:

H x^Y = 0

And we’ll get these by taking our solutions of this:

H \psi = 0

and adjusting them so that

\psi = x^Y

while \psi remains a solution of H \psi = 0.

But: how do we do this ‘adjusting’? That’s the crux of the whole business! That’s what we’ll do today.

Remember, \psi is a function that gives a probability for each ‘state’, or numbered box here:

The picture here consists of two pieces, called ‘connected components’: the piece containing boxes 0 and 1, and the piece containing boxes 2, 3 and 4. It turns out that we can multiply \psi by a function that’s constant on each connected component, and if we had H \psi = 0 to begin with, that will still be true afterward. The reason is that there’s no way for \psi to ‘leak across’ from one component to another. It’s like having water in separate buckets. You can increase the amount of water in one bucket, and decrease it another, and as long as the water’s surface remains flat in each bucket, the whole situation remains in equilibrium.

That’s sort of obvious. What’s not obvious is that we can adjust \psi this way so as to ensure

\psi = x^Y

for some x.

And indeed, it’s not always true! It’s only true if our reaction network obeys a special condition. It needs to have ‘deficiency zero’. We defined this concept back in Part 21, but now we’ll finally use it for something. It turns out to be precisely the right condition to guarantee we can tweak any function on our set of states, multiplying it by a function that’s constant on each connected component, and get a new function \psi with

\psi = x^Y

When all is said and done, that is the key to the deficiency zero theorem.

Review

The battle is almost upon us—we’ve got one last chance to review our notation. We start with a stochastic reaction network:

This consists of:

• finite sets of transitions T, complexes K and species S,

• a map r: T \to (0,\infty) giving a rate constant for each transition,

source and target maps s,t : T \to K saying where each transition starts and ends,

• a one-to-one map Y : K \to \mathbb{N}^S saying how each complex is made of species.

Then we extend s, t and Y to linear maps:

Then we put inner products on these vector spaces as described last time, which lets us ‘turn around’ the maps s and t by taking their adjoints:

s^\dagger, t^\dagger : \mathbb{R}^K \to \mathbb{R}^T

More surprisingly, we can ‘turn around’ Y and get a nonlinear map using ‘matrix exponentiation’:

\begin{array}{ccc} \mathbb{R}^S &\to& \mathbb{R}^K \\                               x     &\mapsto&   x^Y \end{array}

This is most easily understood by thinking of x as a row vector and Y as a matrix:

Remember, complexes are made out of species. The matrix entry Y_{i j} says how many things of the jth species there are in a complex of the ith kind. If \psi \in \mathbb{R}^K says how many complexes there are of each kind, Y \psi \in \mathbb{R}^S says how many things there are of each species. Conversely, if x \in \mathbb{R}^S says how many things there are of each species, x^Y \in \mathbb{R}^K says how many ways we can build each kind of complex from them.

So, we get these maps:

Next, the boundary operator

\partial : \mathbb{R}^T \to \mathbb{R}^K

describes how each transition causes a change in complexes:

\partial = t - s

As we saw last time, there is a Hamiltonian

H : \mathbb{R}^K \to \mathbb{R}^K

describing a Markov processes on the set of complexes, given by

H = \partial s^\dagger

But the star of the show is the rate equation. This describes how the number of things of each species changes with time. We write these numbers in a list and get a vector x \in \mathbb{R}^S with nonnegative components. The rate equation says:

\displaystyle{ \frac{d x}{d t} = Y H x^Y }

We can read this as follows:

x says how many things of each species we have now.

x^Y says how many complexes of each kind we can build from these species.

s^\dagger x^Y says how many transitions of each kind can originate starting from these complexes, with each transition weighted by its rate.

H x^Y = \partial s^\dagger x^Y is the rate of change of the number of complexes of each kind, due to these transitions.

Y H x^Y is the rate of change of the number of things of each species.

The zero deficiency theorem

We are looking for equilibrium solutions of the rate equation, where the number of things of each species doesn’t change at all:

Y H x^Y = 0

In fact we will find complex balanced equilibrium solutions, where even the number of complexes of each kind doesn’t change:

H x^Y = 0

More precisely, we have:

Deficiency Zero Theorem (Child’s Version). Suppose we have a reaction network obeying these two conditions:

1. It is weakly reversible, meaning that whenever there’s a transition from one complex \kappa to another \kappa', there’s a directed path of transitions going back from \kappa' to \kappa.

2. It has deficiency zero, meaning \mathrm{im} \partial  \cap \mathrm{ker} Y = \{ 0 \} .

Then for any choice of rate constants there exists a complex balanced equilibrium solution of the rate equation where all species are present in nonzero amounts. In other words, there exists x \in \mathbb{R}^S with all components positive and such that:

H x^Y = 0

Proof. Because our reaction network is weakly reversible, the theorems in Part 23 show there exists \psi \in (0,\infty)^K with

H \psi = 0

This \psi may not be of the form x^Y, but we shall adjust \psi so that it becomes of this form, while still remaining a solution of H \psi = 0 latex . To do this, we need a couple of lemmas:

Lemma 1. \mathrm{ker} \partial^\dagger + \mathrm{im} Y^\dagger = \mathbb{R}^K.

Proof. We need to use a few facts from linear algebra. If V is a finite-dimensional vector space with inner product, the orthogonal complement L^\perp of a subspace L \subseteq V consists of vectors that are orthogonal to everything in L:

L^\perp = \{ v \in V : \quad \forall w \in L \quad \langle v, w \rangle = 0 \}

We have

(L \cap M)^\perp = L^\perp + M^\perp

where L and M are subspaces of V and + denotes the sum of two subspaces: that is, the smallest subspace containing both. Also, if T: V \to W is a linear map between finite-dimensional vector spaces with inner product, we have

(\mathrm{ker} T)^\perp = \mathrm{im} T^\dagger

and

(\mathrm{im} T)^\perp = \mathrm{ker} T^\dagger

Now, because our reaction network has deficiency zero, we know that

\mathrm{im} \partial \cap \mathrm{ker} Y = \{ 0 \}

Taking the orthogonal complement of both sides, we get

(\mathrm{im} \partial \cap \mathrm{ker} Y)^\perp = \mathbb{R}^K

and using the rules we mentioned, we obtain

\mathrm{ker} \partial^\dagger + \mathrm{im} Y^\dagger = \mathbb{R}^K

as desired.   █

Now, given a vector \phi in \mathbb{R}^K or \mathbb{R}^S with all positive components, we can define the logarithm of such a vector, component-wise:

(\ln \phi)_i = \ln (\phi_i)

Similarly, for any vector \phi in either of these spaces, we can define its exponential in a component-wise way:

(\exp \phi)_i = \exp(\phi_i)

These operations are inverse to each other. Moreover:

Lemma 2. The nonlinear operator

\begin{array}{ccc} \mathbb{R}^S &\to& \mathbb{R}^K \\                               x     &\mapsto&   x^Y \end{array}

is related to the linear operator

\begin{array}{ccc} \mathbb{R}^S &\to& \mathbb{R}^K \\                               x     &\mapsto&   Y^\dagger x \end{array}

by the formula

x^Y = \exp(Y^\dagger \ln x )

which holds for all x \in (0,\infty)^S.

Proof. A straightforward calculation. By the way, this formula would look a bit nicer if we treated \ln x as a row vector and multiplied it on the right by Y: then we would have

x^Y = \exp((\ln x) Y)

The problem is that we are following the usual convention of multiplying vectors by matrices on the left, yet writing the matrix on the right in x^Y. Taking the transpose Y^\dagger of the matrix Y serves to compensate for this.   █

Now, given our vector \psi \in (0,\infty)^K with H \psi = 0, we can take its logarithm and get \ln \psi \in \mathbb{R}^K. Lemma 1 says that

\mathbb{R}^K = \mathrm{ker} \partial^\dagger + \mathrm{im} Y^\dagger

so we can write

\ln \psi =  \alpha + Y^\dagger \beta

where \alpha \in \mathrm{ker} \partial^\dagger and \beta \in \mathbb{R}^S. Moreover, we can write

\beta = \ln x

for some x \in (0,\infty)^S, so that

\ln \psi = \alpha + Y^\dagger (\ln x)

Exponentiating both sides component-wise, we get

\psi  =   \exp(\alpha) \; \exp(Y^\dagger (\ln x))

where at right we are taking the component-wise product of vectors. Thanks to Lemma 2, we conclude that

\psi = \exp(\alpha) x^Y

So, we have taken \psi and almost written it in the form x^Y—but not quite! We can adjust \psi to make it be of this form:

\exp(-\alpha) \psi = x^Y

Clearly all the components of \exp(-\alpha) \psi are positive, since the same is true for both \psi and \exp(-\alpha). So, the only remaining task is to check that

H(\exp(-\alpha) \psi) = 0

We do this using two lemmas:

Lemma 3. If H \psi = 0 and \alpha \in \mathrm{ker} \partial^\dagger, then H(\exp(-\alpha) \psi) = 0.

Proof. It is enough to check that multiplication by \exp(-\alpha) commutes with the Hamiltonian H, since then

H(\exp(-\alpha) \psi) = \exp(-\alpha) H \psi = 0

Recall from Part 23 that H is the Hamiltonian of a Markov process associated to this ‘graph with rates’:

As noted here:

• John Baez and Brendan Fong, A Noether theorem for Markov processes.

multiplication by some function on K commutes with H if and only if that function is constant on each connected component of this graph. Such functions are called conserved quantities.

So, it suffices to show that \exp(-\alpha) is constant on each connected component. For this, it is enough to show that \alpha itself is constant on each connected component. But this will follow from the next lemma, since \alpha \in \mathrm{ker} \partial^\dagger.   █

Lemma 4. A function \alpha \in \mathbb{R}^K is a conserved quantity iff \partial^\dagger \alpha = 0 . In other words, \alpha is constant on each connected component of the graph s, t: T \to K iff \partial^\dagger \alpha = 0 .

Proof. Suppose \partial^\dagger \alpha = 0, or in other words, \alpha \in \mathrm{ker} \partial^\dagger, or in still other words, \alpha \in (\mathrm{im} \partial)^\perp. To show that \alpha is constant on each connected component, it suffices to show that whenever we have two complexes connected by a transition, like this:

\tau: \kappa \to \kappa'

then \alpha takes the same value at both these complexes:

\alpha_\kappa = \alpha_{\kappa'}

To see this, note

\partial \tau = t(\tau) - s(\tau) = \kappa' - \kappa

and since \alpha \in (\mathrm{im} \partial)^\perp, we conclude

\langle \alpha, \kappa' - \kappa \rangle = 0

But calculating this inner product, we see

\alpha_{\kappa'} - \alpha_{\kappa} = 0

as desired.

For the converse, we simply turn the argument around: if \alpha is constant on each connected component, we see \langle \alpha, \kappa' - \kappa \rangle = 0 whenever there is a transition \tau : \kappa \to \kappa'. It follows that \langle \alpha, \partial \tau \rangle = 0 for every transition \tau, so \alpha \in (\mathrm{im} \partial)^\perp .

And thus concludes the proof of the lemma!   █

And thus concludes the proof of the theorem!   █

And thus concludes this post!

6 Responses to Network Theory (Part 24)

  1. Robert Smart says:

    The requested URL

    /home/baez/networks/markov+Process_diagram_1.png

    was not found on this server.

  2. This is a bit unrelated, but I just had the pleasure of attending a talk by Susanna Still, a Professor of Information and Computer Science out here at the University of Hawaii. The punchline of her talk was that for systems far from thermodynamic equilibrium (like most in nature) there is a “fundamental equivalence between … model inefficiency and thermodynamic inefficiency, measured by dissipation.” pointing to a deep connection ‘between the effective use of information and efficient thermodynamic operation.” Here is a link to a pre-print of her coming paper. http://arxiv.org/abs/1203.3271/

  3. Perhaps you’ve already seen it, but it seems to me this paper may be relevant to this series or an adjacent one.

    • John Baez says:

      Thanks, that looks interesting! I hadn’t seen it:

      • R. L. Karp, M. Pérez Millán, T. Dasgupta, A. Dickenstein and J. Gunawardena, Complex-linear invariants of biochemical networks.

      Abstract: The nonlinearities found in molecular networks usually prevent mathematical analysis of network behaviour, which has largely been studied by numerical simulation. This can lead to difficult problems of parameter determination. However, molecular networks give rise, through mass-action kinetics, to polynomial dynamical systems, whose steady states are zeros of a set of polynomial equations. These equations may be analysed by algebraic methods, in which parameters are treated as symbolic expressions whose numerical values do not have to be known in advance. For instance, an “invariant” of a network is a polynomial expression on selected state variables that vanishes in any steady state. Invariants have been found that encode key network properties and that discriminate between different network structures. Although invariants may be calculated by computational algebraic methods, such as Gröobner bases, these become computationally infeasible for biologically realistic networks. Here, we exploit Chemical Reaction Network Theory (CRNT) to develop an efficient procedure for calculating invariants that are linear combinations of “complexes”, or the monomials coming from mass action. We show how this procedure can be used in proving earlier results of Horn and Jackson and of Shinar and Feinberg for networks of deficiency at most one. We then apply our method to enzyme bifunctionality, including the bacterial EnvZ/OmpR osmolarity regulator and the mammalian 6-phosphofructo-2-kinase/fructose-2,6-bisphosphatase glycolytic regulator, whose networks have deficiencies up to four. We show that bifunctionality leads to different forms of concentration control that are robust to changes in initial conditions or total amounts. Finally, we outline a systematic procedure for using complex-linear invariants to analyse molecular networks of any deficiency.

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