Salar de Uyuni

22 January, 2016

 

I just learned about the Salar de Uyuni: the world’s largest salt flat, located in southwest Bolivia. It’s about 10,000 square kilometers in area!

It’s high up, near the crest of the Andes, 3,600 meters above sea level. Once there were permanent lakes here, but no more. This area is a transition zone: the eastern part gets rain in the summer, but clouds never make it past the western part, near the border with Chile. Further west comes the the famously dry Atacama Desert.

The Salar de Uyuni is high, but still it lives up to the name ‘salt flat’: its salt crust varies in height by less than one meter over the entire area. It’s so flat that people use it for testing equipment that measures altitudes.

Why is it so flat? Because the dry crust covers a huge pool of brine that is still liquid! This brine is a saturated solution of sodium chloride, lithium chloride and magnesium chloride in water. As a result, Salar de Uyuni contains over half of the world’s lithium reserves!

In the rainy season, the Salazar de Uyuni looks very different:

And when it’s wet, three different types of flamingos visit the Salar: the Chilean flamingo, the rare Andean flamingo, and the closely related but even rarer James flamingo, which for a while was thought to be extinct!


Flamingos eat algae that grow in the brine. This is why they’re pink! Newly hatched flamingos are gray or white. Their feathers become pink only thanks to carotene which they get from algae—or from crustaceans that in turn eat algae. Animals are not able to synthesize these molecules!



Carotene comes in different forms, but here is one of the most
common: β-carotene. I like it because it’s perfectly symmetrical. It has a long chain of carbons with alternating single and double bonds. Electrons vibrating along this chain absorb blue light. So carotene has the opposite color: orange!

It’s not just flamingos that need carotene or related compounds. Humans need a chemical called retinal in order to see:

It looks roughly like half a carotene molecule—and like
carotene, it’s good at absorbing light. Attached to a larger protein molecule called an opsin, retinal acts like a kind of antenna, catching particles of light. Humans can’t produce retinal without help from the foods we eat. Any chemical we can use to produce retinal is called ‘vitamin A’. So vitamin A isn’t one specific chemical: it’s a group. But beta carotene counts as a form of vitamin A.

Speaking of humans: people sometimes come to have fun in the Salar de Uyuni. There are hotels made of salt! And thanks to the featureless expanse of salt, you can take some amusing trick pictures:

Click on the pictures to find out more about them. For more on the Salar de Uyuni, try:

Salar de Uyuni, Wikipedia.

Puzzle: What kinds of algae, and other organisms, live in the brine of the Salar de Uyuni when it rains? How do they survive when it dries out? There must be some very interesting adaptations going on.


Glycolysis (Part 2)

18 January, 2016

Glyolysis is a way that organisms can get free energy from glucose without needing oxygen. Animals like you and me can do glycolysis, but we get more free energy from oxidizing glucose. Other organisms are anaerobic: they don’t need oxygen. And some, like yeast, survive mainly by doing glycolysis!

If you put yeast cells in water containing a constant low concentration of glucose, they convert it into alcohol at a constant rate. But if you increase the concentration of glucose something funny happens. The alcohol output starts to oscillate!

It’s not that the yeast is doing something clever and complicated. If you break down the yeast cells, killing them, this effect still happens. People think these oscillations are inherent to the chemical reactions in glycolysis.

I learned this after writing Part 1, thanks to Alan Rendall. I first met Alan when we were both working on quantum gravity. But last summer I met him in Copenhagen, where we both attending the workshop Trends in reaction network theory. It turned out that now he’s deep into the mathematics of biochemistry, especially chemical oscillations! He has a blog, and he’s written some great articles on glycolysis:

• Alan Rendall, Albert Goldbeter and glycolytic oscillations, Hydrobates, 21 January 2012.

• Alan Rendall, The Higgins–Selkov oscillator, Hydrobates, 14 May 2014.

In case you’re wondering, Hydrobates is the name of a kind of sea bird, the storm petrel. Alan is fond of sea birds. Since the ultimate goal of my work is to help our relationship with nature, this post is dedicated to the storm petrel:

The basics

Last time I gave a summary description of glycolysis:

glucose + 2 NAD+ + 2 ADP + 2 phosphate →
2 pyruvate + 2 NADH + 2 H+ + 2 ATP + 2 H2O

The idea is that a single molecule of glucose:

gets split into two molecules of pyruvate:

The free energy released from this process is used to take two molecules of adenosine diphosphate or ADP:

and attach to each one phosphate group, typically found as phosphoric acid:

thus producing two molecules of adenosine triphosphate or ATP:

along with 2 molecules of water.

But in the process, something else happens too! 2 molecules of nicotinamide adenine dinucleotide NAD get reduced. That is, they change from the oxidized form called NAD+:

to the reduced form called NADH, along with two protons: that is, 2 H+.

Puzzle 1. Why does NAD+ have a little plus sign on it, despite the two O’s in the picture above?

Left alone in water, ATP spontaneously converts back to ADP and phosphate:

ATP + H2O → ADP + phosphate

This process gives off 30.5 kilojoules of energy per mole. The cell harnesses this to do useful work by coupling this reaction to others. Thus, ATP serves as ‘energy currency’, and making it is the main point of glycolysis.

The cell can also use NADH to do interesting things. It generally has more free energy than NAD+, so it can power things while turning back into NAD+. Just how much more free energy it has depends a lot on conditions in the cell: for example, on the pH.

Puzzle 2. There is often roughly 700 times as much NAD+ as NADH in the cytoplasm of mammals. In these conditions, what is the free energy difference between NAD+ and NADH? I think this is something you’re supposed to be able to figure out.

Nothing in what I’ve said so far gives any clue about why glycolysis might exhibit oscillations. So, we have to dig deeper.

Some details

Glycolysis actually consists of 10 steps, each mediated by its own enzyme. Click on this picture to see all these steps:


If your eyes tend to glaze over when looking at this, don’t feel bad—so do mine. There’s a lot of information here. But if you look carefully, you’ll see that the 1st and 3rd stages of glycolysis actually convert 2 ATP’s to ADP, while the 7th and 10th convert 4 ADP’s to ATP. So, the early steps require free energy, while the later ones double this investment. As the saying goes, “it takes money to make money”.

This nuance makes it clear that if a cell starts with no ATP, it won’t be able to make ATP by glycolysis. And if has just a small amount of ATP, it won’t be very good at making it this way.

In short, this affects the dynamics in an important way. But I don’t see how it could explain oscillations in how much ATP is manufactured from a constant supply of glucose!

We can look up the free energy changes for each of the 10 reactions in glycolysis. Here they are, named by the enzymes involved:


I got this from here:

• Leslie Frost, Glycolysis.

I think these are her notes on Chapter 14 of Voet, Voet, and Pratt’s Fundamentals of Biochemistry. But again, I don’t think these explain the oscillations. So we have to look elsewhere.

Oscillations

By some careful detective work—by replacing the input of glucose by an input of each of the intermediate products—biochemists figured out which step causes the oscillations. It’s the 3rd step, where fructose-6-phosphate is converted into fructose-1,6-bisphosphate, powered by the conversion of ATP into ADP. The enzyme responsible for this step is called phosphofructokinase or PFK. And it turns out that PFK works better when there is ADP around!

In short, the reaction network shown above is incomplete: ADP catalyzes its own formation in the 3rd step.

How does this lead to oscillations? The Higgins–Selkov model is a scenario for how it might happen. I’ll explain this model, offering no evidence that it’s correct. And then I’ll take you to a website where you can see this model in action!

Suppose that fructose-6-phosphate is being produced at a constant rate. And suppose there’s some other reaction, which we haven’t mentioned yet, that uses up ADP at a constant rate. Suppose also that it takes two ADP’s to catalyze the 3rd step. So, we have these reactions:

→ fructose-6-phosphate
fructose-6-phosphate + 2 ADP → 3 ADP
ADP →

Here the blanks mean ‘nothing’, or more precisely ‘we don’t care’. The fructose-6-biphosphate is coming in from somewhere, but we don’t care where it’s coming from. The ADP is going away, but we don’t care where. We’re also ignoring the ATP that’s required for the second reaction, and the fructose-1,6-bisphosphate that’s produced by this reaction. All these features are irrelevant to the Higgins–Selkov model.

Now suppose there’s initially a lot of ADP around. Then the fructose-6-phosphate will quickly be used up, creating even more ADP. So we get even more ADP!

But as this goes on, the amount of fructose-6-phosphate sitting around will drop. So, eventually the production of ADP will drop. Thus, since we’re positing a reaction that uses up ADP at a constant rate, the amount of ADP will start to drop.

Eventually there will be very little ADP. Then it will be very hard for fructose-6-phosphate to get used up. So, the amount of fructose-6-phosphate will start to build up!

Of course, whatever ADP is still left will help use up this fructose-6-phosphate and turn it into ADP. This will increase the amount of ADP. So eventually we will have a lot of ADP again.

We’re back where we started. And so, we’ve got a cycle!

Of course, this story doesn’t prove anything. We should really take our chemical reaction network and translate it into some differential equations for the amount of fructose-6-phosphate and the amount of ADP. In the Higgins–Selkov model people sometimes write just ‘S’ for fructose-6-phosphate and ‘P’ for ADP. (In case you’re wondering, S stands for ‘substrate’ and P stands for ‘product’.) So, our chemical reaction network becomes

→ S
S + 2P → 3P
P →

and using the law of mass action we get these equations:

\displaystyle{ \frac{d S}{dt} = v_0 - k_1 S P^2 }

\displaystyle{ \frac{d P}{dt} = k_1 S P^2 - k_2 P }

where S and P stand for how much S and P we have, respectively, and v_0, k_1, k_2 are some constants.

Now we can solve these differential equations and see if we get oscillations. The answer depends on the constants v_0, k_1, k_2 and also perhaps the initial conditions.

To see what actually happens, try this website:

• Mike Martin, Glycolytic oscillations: the Higgins–Selkov model.

If you run it with the constants and initial conditions given to you, you’ll get oscillations. You’ll also get this vector field on the S,P plane, showing how the system evolves in time:


This is called a phase portrait, and its a standard tool for studying first-order differential equations where two variables depend on time.

This particular phase portrait shows an unstable fixed point and a limit cycle. That’s jargon for saying that in these conditions, the system will tend to oscillate. But if you adjust the constants, the limit cycle will go away! The appearance or disappearance of a limit cycle like this is called a Hopf bifurcation.

For details, see:

• Alan Rendall, Dynamical systems, Chapter 11: Oscillations.

He shows that the Higgins–Selkov model has a unique stationary solution (i.e. fixed point), which he describes. By linearizing it, he finds that this fixed point is stable when v_0 (the inflow of S) is less than a certain value, and unstable when it exceeds that value.

In the unstable case, if the solutions are all bounded as t \to \infty there must be a periodic solution. In the course notes he shows this for a simpler model of glycolysis, the Schnakenberg model. In some separate notes he shows it for the Higgins–Selkov model, at least for certain values of the parameters:

• Alan Rendall, The Higgins–Selkov oscillator.


Curiosity Meets Martian Dunes

17 January, 2016

In December, the rover Curiosity reached some sand dunes on Mars, giving us the first views of these dunes taken from the ground instead of from above. It’s impressive how the dune seems to shoot straight up from the rocks here!

In fact this slope—the steep downwind slope of one of “Bagnold Dunes” along the northwestern flank of Mount Sharp—is just about 27°. But mountaineers will confirm that slopes always looks steeper than they are.

The wind makes this dune move about one meter per year.

For more, see:

• NASA, NASA Mars rover Curiosity reaches sand dunes, 10 December 2015.

• Jet Propulsion Laboratory, Mastcam telephoto of a Martian dune’s downwind face, 4 January 2016.

• Jet Propulsion Laboratory, Slip face on downwind side of ‘Namib’ sand dune on Mars, 6 January 2016.



Information Geometry (Part 16)

14 January, 2016

joint with Blake Pollard

Lately we’ve been thinking about open Markov processes. These are random processes where something can hop randomly from one state to another (that’s the ‘Markov process’ part) but also enter or leave the system (that’s the ‘open’ part).

The ultimate goal is to understand the nonequilibrium thermodynamics of open systems—systems where energy and maybe matter flows in and out. If we could understand this well enough, we could understand in detail how life works. That’s a difficult job! But one has to start somewhere, and this is one place to start.

We have a few papers on this subject:

• Blake Pollard, A Second Law for open Markov processes. (Blog article here.)

• John Baez, Brendan Fong and Blake Pollard, A compositional framework for Markov processes. (Blog article here.)

• Blake Pollard, Open Markov processes: A compositional perspective on non-equilibrium steady states in biology. (Blog article here.)

However, right now we just want to show you three closely connected results about how relative entropy changes in open Markov processes.

Definitions

An open Markov process consists of a finite set X of states, a subset B \subseteq X of boundary states, and an infinitesimal stochastic operator H: \mathbb{R}^X \to \mathbb{R}^X, meaning a linear operator with

H_{ij} \geq 0 \ \  \text{for all} \ \ i \neq j

and

\sum_i H_{ij} = 0 \ \  \text{for all} \ \ j

For each state i \in X we introduce a population p_i  \in [0,\infty). We call the resulting function p : X \to [0,\infty) the population distribution.

Populations evolve in time according to the open master equation:

\displaystyle{ \frac{dp_i}{dt} = \sum_j H_{ij}p_j} \ \  \text{for all} \ \  i \in X-B

p_i(t) = b_i(t) \ \  \text{for all} \ \  i \in B

So, the populations p_i obey a linear differential equation at states i that are not in the boundary, but they are specified ‘by the user’ to be chosen functions b_i at the boundary states. The off-diagonal entry H_{ij} for i \neq j describe the rate at which population transitions from the jth to the ith state.

A closed Markov process, or continuous-time discrete-state Markov chain, is an open Markov process whose boundary is empty. For a closed Markov process, the open master equation becomes the usual master equation:

\displaystyle{  \frac{dp}{dt} = Hp }

In a closed Markov process the total population is conserved:

\displaystyle{ \frac{d}{dt} \sum_{i \in X} p_i = \sum_{i,j} H_{ij}p_j = 0 }

This lets us normalize the initial total population to 1 and have it stay equal to 1. If we do this, we can talk about probabilities instead of populations. In an open Markov process, population can flow in and out at the boundary states.

For any pair of distinct states i,j, H_{ij}p_j is the flow of population from j to i. The net flux of population from the jth state to the ith state is the flow from j to i minus the flow from i to j:

J_{ij} = H_{ij}p_j - H_{ji}p_i

A steady state is a solution of the open master equation that does not change with time. A steady state for a closed Markov process is typically called an equilibrium. So, an equilibrium obeys the master equation at all states, while for a steady state this may not be true at the boundary states. The idea is that population can flow in or out at the boundary states.

We say an equilibrium p : X \to [0,\infty) of a Markov process is detailed balanced if all the net fluxes vanish:

J_{ij} = 0 \ \  \text{for all} \ \ i,j \in X

or in other words:

H_{ij}p_j = H_{ji}p_i \ \  \text{for all} \ \ i,j \in X

Given two population distributions p, q : X \to [0,\infty) we can define the relative entropy

\displaystyle{  I(p,q) = \sum_i p_i \ln \left( \frac{p_i}{q_i} \right)}

When q is a detailed balanced equilibrium solution of the master equation, the relative entropy can be seen as the ‘free energy’ of p. For a precise statement, see Section 4 of Relative entropy in biological systems.

The Second Law of Thermodynamics implies that the free energy of a closed system tends to decrease with time, so for closed Markov processes we expect I(p,q) to be nonincreasing. And this is true! But for open Markov processes, free energy can flow in from outside. This is just one of several nice results about how relative entropy changes with time.

Results

Theorem 1. Consider an open Markov process with X as its set of states and B as the set of boundary states. Suppose p(t) and q(t) obey the open master equation, and let the quantities

\displaystyle{ \frac{Dp_i}{Dt} = \frac{dp_i}{dt} - \sum_{j \in X} H_{ij}p_j }

\displaystyle{  \frac{Dq_i}{Dt} = \frac{dq_i}{dt} - \sum_{j \in X} H_{ij}q_j }

measure how much the time derivatives of p_i and q_i fail to obey the master equation. Then we have

\begin{array}{ccl}   \displaystyle{  \frac{d}{dt}  I(p(t),q(t)) } &=& \displaystyle{ \sum_{i, j \in X} H_{ij} p_j  \left( \ln(\frac{p_i}{q_i}) - \frac{p_i q_j}{p_j q_i} \right)} \\ \\ && \; + \; \displaystyle{ \sum_{i \in B} \frac{\partial I}{\partial p_i} \frac{Dp_i}{Dt} +  \frac{\partial I}{\partial q_i} \frac{Dq_i}{Dt} }  \end{array}

This result separates the change in relative entropy change into two parts: an ‘internal’ part and a ‘boundary’ part.

It turns out the ‘internal’ part is always less than or equal to zero. So, from Theorem 1 we can deduce a version of the Second Law of Thermodynamics for open Markov processes:

Theorem 2. Given the conditions of Theorem 1, we have

\displaystyle{  \frac{d}{dt}  I(p(t),q(t)) \; \le \;  \sum_{i \in B} \frac{\partial I}{\partial p_i} \frac{Dp_i}{Dt} +  \frac{\partial I}{\partial q_i} \frac{Dq_i}{Dt}  }

Intuitively, this says that free energy can only increase if it comes in from the boundary!

There is another nice result that holds when q is an equilibrium solution of the master equation. This idea seems to go back to Schnakenberg:

Theorem 3. Given the conditions of Theorem 1, suppose also that q is an equilibrium solution of the master equation. Then we have

\displaystyle{  \frac{d}{dt}  I(p(t),q) =  -\frac{1}{2} \sum_{i,j \in X} J_{ij} A_{ij} \; + \; \sum_{i \in B} \frac{\partial I}{\partial p_i} \frac{Dp_i}{Dt} }

where

J_{ij} = H_{ij}p_j - H_{ji}p_i

is the net flux from j to i, while

\displaystyle{ A_{ij} = \ln \left(\frac{p_j q_i}{p_i q_j} \right) }

is the conjugate thermodynamic force.

The flux J_{ij} has a nice meaning: it’s the net flow of population from j to i. The thermodynamic force is a bit subtler, but this theorem reveals its meaning: it says how much the population wants to flow from j to i.

More precisely, up to that factor of 1/2, the thermodynamic force A_{ij} says how much free energy loss is caused by net flux from j to i. There’s a nice analogy here to water losing potential energy as it flows downhill due to the force of gravity.

Proofs

Proof of Theorem 1. We begin by taking the time derivative of the relative information:

\begin{array}{ccl} \displaystyle{ \frac{d}{dt}  I(p(t),q(t)) } &=&  \displaystyle{  \sum_{i \in X} \frac{\partial I}{\partial p_i} \frac{dp_i}{dt} +  \frac{\partial I}{\partial q_i} \frac{dq_i}{dt} } \end{array}

We can separate this into a sum over states i \in X - B, for which the time derivatives of p_i and q_i are given by the master equation, and boundary states i \in B, for which they are not:

\begin{array}{ccl} \displaystyle{ \frac{d}{dt}  I(p(t),q(t)) } &=&  \displaystyle{  \sum_{i \in X-B, \; j \in X} \frac{\partial I}{\partial p_i} H_{ij} p_j +                                               \frac{\partial I}{\partial q_i} H_{ij} q_j }\\  \\   && + \; \; \; \displaystyle{  \sum_{i \in B} \frac{\partial I}{\partial p_i} \frac{dp_i}{dt} +  \frac{\partial I}{\partial q_i} \frac{dq_i}{dt}}   \end{array}

For boundary states we have

\displaystyle{ \frac{dp_i}{dt} = \frac{Dp_i}{Dt} + \sum_{j \in X} H_{ij}p_j }

and similarly for the time derivative of q_i. We thus obtain

\begin{array}{ccl}  \displaystyle{ \frac{d}{dt}  I(p(t),q(t)) } &=&  \displaystyle{  \sum_{i,j \in X} \frac{\partial I}{\partial p_i} H_{ij} p_j + \frac{\partial I}{\partial q_i} H_{ij} q_j }\\  \\ && + \; \; \displaystyle{  \sum_{i \in B} \frac{\partial I}{\partial p_i} \frac{Dp_i}{Dt} +  \frac{\partial I}{\partial q_i} \frac{Dq_i}{Dt}}   \end{array}

To evaluate the first sum, recall that

\displaystyle{   I(p,q) = \sum_{i \in X} p_i \ln (\frac{p_i}{q_i})}

so

\displaystyle{\frac{\partial I}{\partial p_i}} =\displaystyle{1 +  \ln (\frac{p_i}{q_i})} ,  \qquad \displaystyle{ \frac{\partial I}{\partial q_i}}=  \displaystyle{- \frac{p_i}{q_i}   }

Thus, we have

\displaystyle{ \sum_{i,j \in X}  \frac{\partial I}{\partial p_i} H_{ij} p_j + \frac{\partial I}{\partial q_i} H_{ij} q_j  =   \sum_{i,j\in X} (1 +  \ln (\frac{p_i}{q_i})) H_{ij} p_j - \frac{p_i}{q_i} H_{ij} q_j }

We can rewrite this as

\displaystyle{   \sum_{i,j \in X} H_{ij} p_j  \left( 1 + \ln(\frac{p_i}{q_i}) - \frac{p_i q_j}{p_j q_i} \right) }

Since H_{ij} is infinitesimal stochastic we have \sum_{i} H_{ij} = 0, so the first term drops out, and we are left with

\displaystyle{   \sum_{i,j \in X} H_{ij} p_j  \left( \ln(\frac{p_i}{q_i}) - \frac{p_i q_j}{p_j q_i} \right) }

as desired.   █

Proof of Theorem 2. Thanks to Theorem 1, to prove

\displaystyle{  \frac{d}{dt}  I(p(t),q(t)) \; \le \;  \sum_{i \in B} \frac{\partial I}{\partial p_i} \frac{Dp_i}{Dt} +  \frac{\partial I}{\partial q_i} \frac{Dq_i}{Dt}  }

it suffices to show that

\displaystyle{   \sum_{i,j \in X} H_{ij} p_j  \left( \ln(\frac{p_i}{q_i}) - \frac{p_i q_j}{p_j q_i} \right) \le 0  }

or equivalently (recalling the proof of Theorem 1):

\displaystyle{ \sum_{i,j} H_{ij} p_j  \left( \ln(\frac{p_i}{q_i}) + 1 - \frac{p_i q_j}{p_j q_i} \right) \le 0 }

The last two terms on the left hand side cancel when i = j. Thus, if we break the sum into an i \ne j part and an i = j part, the left side becomes

\displaystyle{   \sum_{i \ne j} H_{ij} p_j  \left( \ln(\frac{p_i}{q_i}) + 1 - \frac{p_i q_j}{p_j q_i} \right) \; + \; \sum_j H_{jj} p_j \ln(\frac{p_j}{q_j}) }

Next we can use the infinitesimal stochastic property of H to write H_{jj} as the sum of -H_{ij} over i not equal to j, obtaining

\displaystyle{ \sum_{i \ne j} H_{ij} p_j  \left( \ln(\frac{p_i}{q_i}) + 1 - \frac{p_i q_j}{p_j q_i} \right) - \sum_{i \ne j} H_{ij} p_j \ln(\frac{p_j}{q_j}) } =

\displaystyle{ \sum_{i \ne j} H_{ij} p_j  \left( \ln(\frac{p_iq_j}{p_j q_i}) + 1 - \frac{p_i q_j}{p_j q_i} \right) }

Since H_{ij} \ge 0 when i \ne j and \ln(s) + 1 - s \le 0 for all s > 0, we conclude that this quantity is \le 0.   █

Proof of Theorem 3. Now suppose also that q is an equilibrium solution of the master equation. Then Dq_i/Dt = dq_i/dt = 0 for all states i, so by Theorem 1 we need to show

\displaystyle{ \sum_{i, j \in X} H_{ij} p_j  \left( \ln(\frac{p_i}{q_i}) - \frac{p_i q_j}{p_j q_i} \right)  \; = \;  -\frac{1}{2} \sum_{i,j \in X} J_{ij} A_{ij} }

We also have \sum_{j \in X} H_{ij} q_j = 0, so the second
term in the sum at left vanishes, and it suffices to show

\displaystyle{  \sum_{i, j \in X} H_{ij} p_j  \ln(\frac{p_i}{q_i}) \; = \;  - \frac{1}{2} \sum_{i,j \in X} J_{ij} A_{ij} }

By definition we have

\displaystyle{  \frac{1}{2} \sum_{i,j} J_{ij} A_{ij}} =  \displaystyle{  \frac{1}{2} \sum_{i,j}  \left( H_{ij} p_j - H_{ji}p_i \right)   \ln \left( \frac{p_j q_i}{p_i q_j} \right) }

This in turn equals

\displaystyle{  \frac{1}{2} \sum_{i,j}  H_{ij}p_j    \ln \left( \frac{p_j q_i}{p_i q_j} \right) -   \frac{1}{2} \sum_{i,j}  H_{ji}p_i  \ln \left( \frac{p_j q_i}{p_i q_j} \right) }

and we can switch the dummy indices i,j in the second sum, obtaining

\displaystyle{  \frac{1}{2} \sum_{i,j}  H_{ij}p_j    \ln \left( \frac{p_j q_i}{p_i q_j} \right) -   \frac{1}{2} \sum_{i,j}  H_{ij}p_j    \ln \left( \frac{p_i q_j}{p_j q_i} \right) }

or simply

\displaystyle{ \sum_{i,j} H_{ij} p_j \ln \left( \frac{p_j q_i}{p_i q_j} \right) }

But this is

\displaystyle{  \sum_{i,j} H_{ij} p_j \left(\ln ( \frac{p_j}{q_j}) + \ln (\frac{q_i}{p_i}) \right) }

and the first term vanishes because H is infinitesimal stochastic: \sum_i H_{ij} = 0. We thus have

\displaystyle{  \frac{1}{2} \sum_{i,j} J_{ij} A_{ij}} = \sum_{i,j} H_{ij} p_j  \ln (\frac{q_i}{p_i} )

as desired.   █


Information Geometry (Part 15)

11 January, 2016

It’s been a long time since you’ve seen an installment of the information geometry series on this blog! Before I took a long break, I was explaining relative entropy and how it changes in evolutionary games. Much of what I said is summarized and carried further here:

• John Baez and Blake Pollard, Relative entropy in biological systems. (Blog article here.)

But now Blake has a new paper, and I want to talk about that:

• Blake Pollard, Open Markov processes: a compositional perspective on non-equilibrium steady states in biology, to appear in Open Systems and Information Dynamics.

I’ll focus on just one aspect: the principle of minimum entropy production. This is an exciting yet controversial principle in non-equilibrium thermodynamics. Blake examines it in a situation where we can tell exactly what’s happening.

Non-equilibrium steady states

Life exists away from equilibrium. Left isolated, systems will tend toward thermodynamic equilibrium. However, biology is about open systems: physical systems that exchange matter or energy with their surroundings. Open systems can be maintained away from equilibrium by this exchange. This leads to the idea of a non-equilibrium steady state—a state of an open system that doesn’t change, but is not in equilibrium.

A simple example is a pan of water sitting on a stove. Heat passes from the flame to the water and then to the air above. If the flame is very low, the water doesn’t boil and nothing moves. So, we have a steady state, at least approximately. But this is not an equilibrium, because there is a constant flow of energy through the water.

Of course in reality the water will be slowly evaporating, so we don’t really have a steady state. As always, models are approximations. If the water is evaporating slowly enough, it can be useful to approximate the situation with a non-equilibrium steady state.

There is much more to biology than steady states. However, to dip our toe into the chilly waters of non-equilibrium thermodynamics, it is nice to start with steady states. And already here there are puzzles left to solve.

Minimum entropy production

Ilya Prigogine won the Nobel prize for his work on non-equilibrium thermodynamics. One reason is that he had an interesting idea about steady states. He claimed that under certain conditions, a non-equilibrium steady state will minimize entropy production!

There has been a lot of work trying to make the ‘principle of minimum entropy production’ precise and turn it into a theorem. In this book:

• G. Lebon and D. Jou, Understanding Non-equilibrium Thermodynamics, Springer, Berlin, 2008.

the authors give an argument for the principle of minimum entropy production based on four conditions:

time-independent boundary conditions: the surroundings of the system don’t change with time.

linear phenomenological laws: the laws governing the macroscopic behavior of the system are linear.

constant phenomenological coefficients: the laws governing the macroscopic behavior of the system don’t change with time.

symmetry of the phenomenological coefficients: since they are linear, the laws governing the macroscopic behavior of the system can be described by a linear operator, and we demand that in a suitable basis the matrix for this operator is symmetric: T_{ij} = T_{ji}.

The last condition is obviously the subtlest one; it’s sometimes called Onsager reciprocity, and people have spent a lot of time trying to derive it from other conditions.

However, Blake goes in a different direction. He considers a concrete class of open systems, a very large class called ‘open Markov processes’. These systems obey the first three conditions listed above, and the ‘detailed balanced’ open Markov processes also obey the last one. But Blake shows that minimum entropy production holds only approximately—with the approximation being good for steady states that are near equilibrium!

However, he shows that another minimum principle holds exactly, even for steady states that are far from equilibrium. He calls this the ‘principle of minimum dissipation’.

We actually discussed the principle of minimum dissipation in an earlier paper:

• John Baez, Brendan Fong and Blake Pollard, A compositional framework for Markov processes. (Blog article here.)

But one advantage of Blake’s new paper is that it presents the results with a minimum of category theory. Of course I love category theory, and I think it’s the right way to formalize open systems, but it can be intimidating.

Another good thing about Blake’s new paper is that it explicitly compares the principle of minimum entropy to the principle of minimum dissipation. He shows they agree in a certain limit—namely, the limit where the system is close to equilibrium.

Let me explain this. I won’t include the nice example from biology that Blake discusses: a very simple model of membrane transport. For that, read his paper! I’ll just give the general results.

The principle of minimum dissipation

An open Markov process consists of a finite set X of states, a subset B \subseteq X of boundary states, and an infinitesimal stochastic operator H: \mathbb{R}^X \to \mathbb{R}^X, meaning a linear operator with

H_{ij} \geq 0 \ \  \text{for all} \ \ i \neq j

and

\sum_i H_{ij} = 0 \ \  \text{for all} \ \ j

I’ll explain these two conditions in a minute.

For each i \in X we introduce a population p_i  \in [0,\infty). We call the resulting function p : X \to [0,\infty) the population distribution. Populations evolve in time according to the open master equation:

\displaystyle{ \frac{dp_i}{dt} = \sum_j H_{ij}p_j} \ \  \text{for all} \ \ i \in X-B

p_i(t) = b_i(t) \ \  \text{for all} \ \ i \in B

So, the populations p_i obey a linear differential equation at states i that are not in the boundary, but they are specified ‘by the user’ to be chosen functions b_i at the boundary states.

The off-diagonal entries H_{ij}, \ i \neq j are the rates at which population hops from the jth to the ith state. This lets us understand the definition of an infinitesimal stochastic operator. The first condition:

H_{ij} \geq 0 \ \  \text{for all} \ \ i \neq j

says that the rate for population to transition from one state to another is non-negative. The second:

\sum_i H_{ij} = 0 \ \  \text{for all} \ \ j

says that population is conserved, at least if there are no boundary states. Population can flow in or out at boundary states, since the master equation doesn’t hold there.

A steady state is a solution of the open master equation that does not change with time. A steady state for a closed Markov process is typically called an equilibrium. So, an equilibrium obeys the master equation at all states, while for a steady state this may not be true at the boundary states. Again, the reason is that population can flow in or out at the boundary.

We say an equilibrium q : X \to [0,\infty) of a Markov process is detailed balanced if the rate at which population flows from the ith state to the jth state is equal to the rate at which it flows from the jth state to the ith:

H_{ji}q_i = H_{ij}q_j \ \  \text{for all} \ \ i,j \in X

Suppose we’ve got an open Markov process that has a detailed balanced equilibrium q. Then a non-equilibrium steady state p will minimize a function called the ‘dissipation’, subject to constraints on its boundary populations. There’s a nice formula for the dissipation in terms of p and q.

Definition. Given an open Markov process with detailed balanced equilibrium q we define the dissipation for a population distribution p to be

\displaystyle{ D(p) = \frac{1}{2}\sum_{i,j} H_{ij}q_j \left( \frac{p_j}{q_j} - \frac{p_i}{q_i} \right)^2 }

This formula is a bit tricky, but you’ll notice it’s quadratic in p and it vanishes when p = q. So, it’s pretty nice.

Using this concept we can formulate a principle of minimum dissipation, and prove that non-equilibrium steady states obey this principle:

Definition. We say a population distribution p: X \to \mathbb{R} obeys the principle of minimum dissipation with boundary population b: X \to \mathbb{R} if p minimizes D(p) subject to the constraint that

p_i = b_i \ \  \text{for all} \ \ i \in B.

Theorem 1. A population distribution p is a steady state with p_i = b_i for all boundary states i if and only if p obeys the principle of minimum dissipation with boundary population b.

Proof. This follows from Theorem 28 in A compositional framework for Markov processes.

Minimum entropy production versus minimum dissipation

How does dissipation compare with entropy production? To answer this, first we must ask: what really is entropy production? And: how does the equilibrium state q show up in the concept of entropy production?

The relative entropy of two population distributions p,q is given by

\displaystyle{ I(p,q) = \sum_i p_i \ln \left( \frac{p_i}{q_i} \right) }

It is well known that for a closed Markov process with q as a detailed balanced equilibrium, the relative entropy is monotonically decreasing with time. This is due to an annoying sign convention in the definition of relative entropy: while entropy is typically increasing, relative entropy typically decreases. We could fix this by putting a minus sign in the above formula or giving this quantity I(p,q) some other name. A lot of people call it the Kullback–Leibler divergence, but I have taken to calling it relative information. For more, see:

• John Baez and Blake Pollard, Relative entropy in biological systems. (Blog article here.)

We say ‘relative entropy’ in the title, but then we explain why ‘relative information’ is a better name, and use that. More importantly, we explain why I(p,q) has the physical meaning of free energy. Free energy tends to decrease, so everything is okay. For details, see Section 4.

Blake has a nice formula for how fast I(p,q) decreases:

Theorem 2. Consider an open Markov process with X as its set of states and B as the set of boundary states. Suppose p(t) obeys the open master equation and q is a detailed balanced equilibrium. For any boundary state i \in B, let

\displaystyle{ \frac{Dp_i}{Dt} = \frac{dp_i}{dt} - \sum_{j \in X} H_{ij}p_j }

measure how much p_i fails to obey the master equation. Then we have

\begin{array}{ccl}   \displaystyle{  \frac{d}{dt}  I(p(t),q) } &=& \displaystyle{ \sum_{i, j \in X} H_{ij} p_j  \left( \ln(\frac{p_i}{q_i}) - \frac{p_i q_j}{p_j q_i} \right)} \\ \\ && \; + \; \displaystyle{ \sum_{i \in B} \frac{\partial I}{\partial p_i} \frac{Dp_i}{Dt} }  \end{array}

Moreover, the first term is less than or equal to zero.

Proof. For a self-contained proof, see Information geometry (part 16), which is coming up soon. It will be a special case of the theorems there.   █

Blake compares this result to previous work by Schnakenberg:

• J. Schnakenberg, Network theory of microscopic and macroscopic behavior of master equation systems, Rev. Mod. Phys. 48 (1976), 571–585.

The negative of Blake’s first term is this:

\displaystyle{ K(p) = - \sum_{i, j \in X} H_{ij} p_j  \left( \ln(\frac{p_i}{q_i}) - \frac{p_i q_j}{p_j q_i} \right) }

Under certain circumstances, this equals what Schnakenberg calls the entropy production. But a better name for this quantity might be free energy loss, since for a closed Markov process that’s exactly what it is! In this case there are no boundary states, so the theorem above says K(p) is the rate at which relative entropy—or in other words, free energy—decreases.

For an open Markov process, things are more complicated. The theorem above shows that free energy can also flow in or out at the boundary, thanks to the second term in the formula.

Anyway, the sensible thing is to compare a principle of ‘minimum free energy loss’ to the principle of minimum dissipation. The principle of minimum dissipation is true. How about the principle of minimum free energy loss? It turns out to be approximately true near equilibrium.

For this, consider the situation in which p is near to the equilibrium distribution q in the sense that

\displaystyle{ \frac{p_i}{q_i} = 1 + \epsilon_i }

for some small numbers \epsilon_i. We collect these numbers in a vector called \epsilon.

Theorem 3. Consider an open Markov process with X as its set of states and B as the set of boundary states. Suppose q is a detailed balanced equilibrium and let p be arbitrary. Then

K(p) = D(p) + O(\epsilon^2)

where K(p) is the free energy loss, D(p) is the dissipation, \epsilon_i is defined as above, and by O(\epsilon^2) we mean a sum of terms of order \epsilon_i^2.

Proof. First take the free energy loss:

\displaystyle{ K(p) = -\sum_{i, j \in X} H_{ij} p_j  \left( \ln(\frac{p_i}{q_i}) - \frac{p_i q_j}{p_j q_i} \right)}

Expanding the logarithm to first order in \epsilon, we get

\displaystyle{ K(p) =  -\sum_{i, j \in X} H_{ij} p_j  \left( \frac{p_i}{q_i} - 1 - \frac{p_i q_j}{p_j q_i} \right) + O(\epsilon^2) }

Since H is infinitesimal stochastic, \sum_i H_{ij} = 0, so the second term in the sum vanishes, leaving

\displaystyle{ K(p) =  -\sum_{i, j \in X} H_{ij} p_j  \left( \frac{p_i}{q_i} - \frac{p_i q_j}{p_j q_i} \right) \; + O(\epsilon^2) }

or

\displaystyle{ K(p) =  -\sum_{i, j \in X} \left( H_{ij} p_j  \frac{p_i}{q_i} - H_{ij} q_j \frac{p_i}{q_i} \right) \; + O(\epsilon^2) }

Since q is a equilibrium we have \sum_j H_{ij} q_j = 0, so now the last term in the sum vanishes, leaving

\displaystyle{ K(p) =  -\sum_{i, j \in X} H_{ij} \frac{p_i p_j}{q_i} \; + O(\epsilon^2) }

Next, take the dissipation

\displaystyle{ D(p) = \frac{1}{2}\sum_{i,j} H_{ij}q_j \left( \frac{p_j}{q_j} - \frac{p_i}{q_i} \right)^2 }

and expand the square, getting

\displaystyle{ D(p) = \frac{1}{2}\sum_{i,j} H_{ij}q_j \left( \frac{p_j^2}{q_j^2} - 2\frac{p_i p_j}{q_i q_j} +  \frac{p_i^2}{q_i^2} \right) }

Since H is infinitesimal stochastic, \sum_i H_{ij} = 0. The first term is just this times a function of j, summed over j, so it vanishes, leaving

\displaystyle{ D(p) = \frac{1}{2}\sum_{i,j} H_{ij}q_j \left(- 2\frac{p_i p_j}{q_i q_j} +  \frac{p_i^2}{q_i^2} \right) }

Since q is an equilibrium, \sum_j H_{ij} q_j = 0. The last term above is this times a function of i, summed over i, so it vanishes, leaving

\displaystyle{ D(p) = - \sum_{i,j} H_{ij}q_j  \frac{p_i p_j}{q_i q_j} = - \sum_{i,j} H_{ij} \frac{p_i p_j}{q_i}  }

This matches what we got for K(p), up to terms of order O(\epsilon^2).   █

In short: detailed balanced open Markov processes are governed by the principle of minimum dissipation, not minimum entropy production. Minimum dissipation agrees with minimum entropy production only near equilibrium.


Glycolysis (Part 1)

8 January, 2016

I’m trying to understand some biology. Being a mathematician I’m less interested in all the complicated details of life on this particular planet than in something a bit more abstract. I want to know ‘the language of life’: the right way to talk about living systems.

Of course, there’s no way to reach this goal without learning a lot of the complicated details. But I might as well be honest and state my goal, since it’s bound to put a strange spin on how I learn and talk about biology.

For example, when I heard people were using the pi-calculus to model a very simple bacterium, I wasn’t eager to know how close their model is to the Last Universal Ancestor, the primordial bug from which we all descend. Even though it’s a fascinating question, it’s not one I can help solve. Instead, I wanted to know if the pi-calculus is really the best language for this kind of model.

I also wanted to know what types of chemical reactions are needed for a cell to survive. I’ll never remember all the details of those reactions: I don’t have the right kind of mind for that. But I might manage to think about these reactions in abstract ways that biologists haven’t tried.

So, when I read this:

The minimal gene set prokaryote has been exhaustively described in the enhanced π-calculus. We represented the 237 genes, their relative products, and the metabolic pathways expressed and regulated by the genes, as the corresponding processes and channels. In particular: the glycolytic pathway, the pentose phosphate pathway, the pathways involved in nucleotide, aminoacids, coenzyme, lipids, and glycerol metabolism.

I instantly wanted to get an overall view of these reactions, without immersing myself in all the details.

Unfortunately I don’t know how to do this. Do you?

It might be like trying to learn grammar without learning vocabulary: not very easy, and perhaps unwise.

But I bet there’s a biochemistry textbook that would help me: one that focuses on the forest before getting into the names of all the trees. I may have even seen a such book! I’ve certainly tried to learn biochemistry. It’s a perfectly fascinating subject. But it’s only recently that I’ve gotten serious about chemical reaction networks and nonequilibrium thermodynamics. this may help guide my studies now.

Anyway, let me start with the ‘glycolytic pathway’. Glycolysis is the process of breaking down a sugar called glucose, thereby powering the formation of ATP, which holds energy in a form that the cell can easily use to do many things.

Glycolysis looks pretty complicated, at least if you’re a mathematician:


But when you’re trying to understand the activities of a complicated criminal network, a good slogan is ‘follow the money’. And for a chemical reaction network, you can ‘follow the conserved quantities’. We’ve got various kinds of atoms—hydrogen, carbon, nitrogen, oxygen, phosphorus—and the number of each kind is conserved. That should help us follow what’s going on.

Energy is also conserved, and that’s incredibly important in thermodynamics. Free energy—energy in forms that are actually useful—is not conserved. But it’s still very good to follow it, since while it can go away, turning into heat, it essentially never appears out of nowhere.

The usual definition of free energy is something like

F = E - TS

where E is energy, T is temperature and S is entropy. You can think of this roughly “energy minus energy in the form of heat”. There’s a lot more to say here, but I just want to add that free energy can also be interpreted as ‘relative information’, a purely information-theoretic concept. For an explanation, see Section 4 of this paper:

• John Baez and Blake Pollard, Relative entropy in biological systems. (Blog article here.)

Since I like abstract generalities, this information-theoretic way of understanding free energy appeals to me.

And of course free energy is useful, so an organism should care about it—and we should be able to track what an organism actually does with it. This is one of my main goals: understanding better what it means for a system to ‘do something with free energy’.

In glycolysis, some of the free energy of glucose gets transferred to ATP. ATP is a bit like ‘money’: it carries free energy in a way that the cell can easily ‘spend’ to do interesting things. So, at some point I want to look at an example of how the cell actually spends this money. But for now I want to think about glycolysis—which may be more like ‘cashing a check and getting money’.

First, let’s see what we get if we ‘black-box’ glycolysis. I’ve written about black-boxing electrical circuits and Markov processes: it’s a way to ignore their inner workings and focus on the relation between inputs and outputs.

Blake Pollard and I are starting to study the black-boxing of chemical reaction networks. If we black-box glycolysis, we get this:

glucose + 2 NAD+ + 2 ADP + 2 phosphate →
2 pyruvate + 2 NADH + 2 H+ + 2 ATP + 2 H2O

I’ll talk about NAD+ and NADH later; let’s temporarily ignore those.

A molecule of glucose has more free energy than 2 pyruvate molecules plus 2 water molecules. On the other hand, ADP + phosphate has less free energy than ATP. So, glycolysis is taking free energy from glucose and putting some of it into the handy form of ATP molecules. And a natural question is: how efficient is this reaction? How much free energy gets wasted?

Here’s an interesting paper that touches indirectly on this question:

• Daniel A. Beard, Eric Babson, Edward Curtis and Hong Qian, Thermodynamic constraints for biochemical networks, Journal of Theoretical Biology 228 (2004), 327–333.

They develop a bunch of machinery for studying chemical reaction networks, which I hope to explain someday. (Mathematicians will be delighted to hear that they use matroids, a general framework for studying linear dependence. Biochemists may be less delighted.) Then they apply this machinery to glycolysis, using computers to do some calculations, and they conclude:

Returning to the original problem of ATP production in energy metabolism, and searching for the flux vector that maximizes ATP production while satisfying the
mass balance constraint and the thermodynamic constraint, we find that at most 20.5 ATP are produced for each glucose molecule consumed.

So, they’re getting some upper bound on how good glycolysis could actually be!

Puzzle 1. What upper bounds can you get simply from free energy considerations?

For example, ignore NADH and NAD+ for a second, and ask how much ATP you could make from turning a molecule of glucose into pyruvate and water if free energy were the only consideration. To answer this, you could take the free energy of a mole glucose minus the free energy of the corresponding amount of pyruvate and water, and divide it by the free energy of a mole of ATP minus the free energy of the corresponding amount of ADP and phosphate. What do you get?

Puzzle 2. How do NADH and NAD+ fit into the story? In the last paragraph I ignored those. We shouldn’t really do that! NAD+ is an oxidized form of nicotinamide adenine dinucleotide. NADH is the the reduced form of the same chemical. In our cells, NADH has more free energy than NAD+. So, besides producing ‘free energy money’ in the form of ATP, glycolysis is producing it in the form of NADH! This should improve our upper bound on how much ATP could be produced by glycolysis.

However, the cell uses NADH for more than just ‘money’. It uses NADH to oxidize other chemicals and NAD+ to reduce them. Reduction and oxidation are really important in chemistry, including biochemistry. I need to understand this whole redox business better. Right now my guess is that it’s connected to yet another conserved quantity, which I haven’t mentioned so far.

Puzzle 3. What conserved quantity is that?


Arctic Melting — 2015

6 January, 2016

With help from global warming and the new El Niño, 2015 was a hot year. In fact it was the hottest since we’ve been keeping records—and it ended with a bang!

• Robinson Myer, The storm that will unfreeze the North Pole, The Atlantic, 29 December 2015.

The sun has not risen above the North Pole since mid-September. The sea ice—flat, landlike, windswept, and stretching as far as the eye can see—has been bathed in darkness for months.

But later this week, something extraordinary will happen: Air temperatures at the Earth’s most northernly region, in the middle of winter, will rise above freezing for only the second time on record.

On Wednesday, the same storm system that last week spun up deadly tornadoes in the American southeast will burst into the far north, centering over Iceland. It will bring strong winds and pressure as low as is typically seen during hurricanes.

That low pressure will suck air out of the planet’s middle latitudes and send it rushing to the Arctic. And so on Wednesday, the North Pole will likely see temperatures of about 35 degrees Fahrenheit, or 2 degrees Celsius. That’s 50 degrees hotter than average: it’s usually 20 degrees Fahrenheit below zero there at this time of year.

Here’s a temperature map from a couple days later—the last day of the year, 31 December 2015:

(Click on these images to enlarge them.)

And here, more revealing, is a map of the temperature anomaly: the difference between the temperature and the usual temperature at that place at that time of the year:

I think the temperature anomaly is off the scale at certain places in the Arctic—it should have been about 30 °C hotter than normal, or 55 °F.

These maps are from a great website that will show you a variety of weather maps for any day of the year:

Climate Reanalyzer.

How about the year as a whole?

You can learn a lot about Arctic sea ice here:

• National Snow and Ice Data Center, Arctic Sea Ice News.

Here’s one graph of theirs, which shows that the extent of Arctic sea ice in 2015 was very low. It was 2 standard deviations lower than the 2000–2012 average, though not as low as the record-breaking year of 2012:

Here’s another good source of data:

• Polar Science Center, PIOMAS arctic sea ice volume reanalysis.

PIOMAS stands for the Pan-Arctic Ice Ocean Modeling and Assimilation System. Here is their estimate of the Arctic sea ice volume over the course of 2015, compared to other years:

The annual cycle is very visible here.

It’s easier to see the overall trend in this graph:

This shows, for each day, the Arctic sea ice volume minus its average over 1979–2014 for that day of the year. This is a way to remove the annual cycle and focus on the big picture, including the strange events after 2012.

What to do?

The Arctic is melting.

What does that matter to us down here? We’ll probably get strange new weather patterns. It may already be happening. I hope it’s clear by now: the first visible impact of global warming is ‘wild weather’.

But what can we do about it? Of course we should stop burning carbon. But even if we stopped completely, that wouldn’t reverse the effects of the warming so far. Someday people may want to reverse its effects—at least for the Arctic.

So, it might be good to reread part of my interview with Gregory Benford. He has a plan to cool the Arctic, which he claims is quite affordable. He’s mainly famous as a science fiction author, but he’s also an astrophysicist at U. C. Irvine.

Geoengineering the Arctic

JB: I want to spend a bit more time on your proposal to screen the Arctic. There’s a good summary here:

• Gregory Benford, Climate controls, Reason Magazine, November 1997.

But in brief, it sounds like you want to test the results of spraying a lot of micron-sized dust into the atmosphere above the Arctic Sea during the summer. You suggest diatomaceous earth as an option, because it’s chemically inert: just silica. How would the test work, exactly, and what would you hope to learn?

GB: The US has inflight refueling aircraft such as the KC-10 Extender that with minor changes spread aerosols at relevant altitudes, and pilots who know how to fly big sausages filled with fluids.



Rather than diatomaceous earth, I now think ordinary SO2 or H2S will work, if there’s enough water at the relevant altitudes. Turns out the pollutant issue is minor, since it would be only a percent or so of the SO2 already in the Arctic troposphere. The point is to spread aerosols to diminish sunlight and look for signals of less sunlight on the ground, changes in sea ice loss rates in summer, etc. It’s hard to do a weak experiment and be sure you see a signal. Doing regional experiments helps, so you can see a signal before the aerosols spread much. It’s a first step, an in-principle experiment.

Simulations show it can stop the sea ice retreat. Many fear if we lose the sea ice in summer ocean currents may alter; nobody really knows. We do know that the tundra is softening as it thaws, making roads impassible and shifting many wildlife patterns, with unforeseen long term effects. Cooling the Arctic back to, say, the 1950 summer temperature range would cost maybe $300 million/year, i.e., nothing. Simulations show to do this globally, offsetting say CO2 at 500 ppm, might cost a few billion dollars per year. That doesn’t help ocean acidification, but it’s a start on the temperature problem.

JB: There’s an interesting blog on Arctic political, military and business developments:

• Anatoly Karlin, Arctic Progress.

Here’s the overview:

Today, global warming is kick-starting Arctic history. The accelerating melting of Arctic sea ice promises to open up circumpolar shipping routes, halving the time needed for container ships and tankers to travel between Europe and East Asia. As the ice and permafrost retreat, the physical infrastructure of industrial civilization will overspread the region […]. The four major populated regions encircling the Arctic Ocean—Alaska, Russia, Canada, Scandinavia (ARCS)—are all set for massive economic expansion in the decades ahead. But the flowering of industrial civilization’s fruit in the thawing Far North carries within it the seeds of its perils. The opening of the Arctic is making border disputes more serious and spurring Russian and Canadian military buildups in the region. The warming of the Arctic could also accelerate global warming—and not just through the increased economic activity and hydrocarbons production. One disturbing possibility is that the melting of the Siberian permafrost will release vast amounts of methane, a greenhouse gas that is far more potent than CO2, into the atmosphere, and tip the world into runaway climate change.

But anyway, unlike many people, I’m not mentioning risks associated with geoengineering in order to instantly foreclose discussion of it, because I know there are also risks associated with not doing it. If we rule out doing anything really new because it’s too expensive or too risky, we might wind up locking ourselves in a "business as usual" scenario. And that could be even more risky—and perhaps ultimately more expensive as well.

GB: Yes, no end of problems. Most impressive is how they look like a descending spiral, self-reinforcing.

Certainly countries now scramble for Arctic resources, trade routes opened by thawing—all likely to become hotly contested strategic assets. So too melting Himalayan glaciers can perhaps trigger "water wars" in Asia—especially India and China, two vast lands of very different cultures. Then, coming on later, come rising sea levels. Florida starts to go away. The list is endless and therefore uninteresting. We all saturate.

So droughts, floods, desertification, hammering weather events—they draw ever less attention as they grow more common. Maybe Darfur is the first "climate war." It’s plausible.

The Arctic is the canary in the climate coalmine. Cutting CO2 emissions will take far too long to significantly affect the sea ice. Permafrost melts there, giving additional positive feedback. Methane release from the not-so-perma-frost is the most dangerous amplifying feedback in the entire carbon cycle. As John Nissen has repeatedly called attention to, the permafrost permamelt holds a staggering 1.5 trillion tons of frozen carbon, about twice as much carbon as is in the atmosphere. Much would emerge as methane. Methane is 25 times as potent a heat-trapping gas as CO2 over a century, and 72 times as potent over the first 20 years! The carbon is locked in a freezer. Yet that’s the part of the planet warming up the fastest. Really bad news:

• Kevin Schaefer, Tingjun Zhang, Lori Bruhwiler and Andrew P. Barrett, Amount and timing of permafrost carbon release in response to climate warming, Tellus, 15 February 2011.

Particularly interesting is the slowing of thermohaline circulation. In John Nissen’s "two scenarios" work there’s an uncomfortably cool future—if the Gulf Stream were to be diverted by meltwater flowing into NW Atlantic. There’s also an unbearably hot future, if the methane from not-so-permafrost and causes global warming to spiral out of control. So we have a terrifying menu.

JB: I recently interviewed Nathan Urban here. He explained a paper where he estimated the chance that the Atlantic current you’re talking about could collapse. (Technically, it’s the Atlantic meridional overturning circulation, not quite the same as the Gulf Stream.) They got a 10% chance of it happening in two centuries, assuming a business as usual scenario. But there are a lot of uncertainties in the modeling here.

Back to geoengineering. I want to talk about some ways it could go wrong, how soon we’d find out if it did, and what we could do then.

For example, you say we’ll put sulfur dioxide in the atmosphere below 15 kilometers, and most of the ozone is above 20 kilometers. That’s good, but then I wonder how much sulfur dioxide will diffuse upwards. As the name suggests, the stratosphere is "stratified" —there’s not much turbulence. That’s reassuring. But I guess one reason to do experiments is to see exactly what really happens.

GB: It’s really the only way to go forward. I fear we are now in the Decade of Dithering that will end with the deadly 2020s. Only then will experiments get done and issues engaged. All else, as tempting as ideas and simulations are, spell delay if they do not couple with real field experiments—from nozzle sizes on up to albedo measures —which finally decide.

JB: Okay. But what are some other things that could go wrong with this sulfur dioxide scheme? I know you’re not eager to focus on the dangers, but you must be able to imagine some plausible ones: you’re an SF writer, after all. If you say you can’t think of any, I won’t believe you! And part of good design is looking for possible failure modes.

GB: Plenty an go wrong with so vast an idea. But we can learn from volcanoes, that give us useful experiments, though sloppy and noisy ones, about putting aerosols into the air. Monitoring those can teach us a lot with little expense.

We can fail to get the aerosols to avoid clumping, so they fall out too fast. Or we can somehow trigger a big shift in rainfall patterns—a special danger in a system already loaded with surplus energy, as is already displaying anomalies like the bitter winters in Europe, floods in Pakistan, drought in Darfur. Indeed, some of Alan Robock’s simulations of Arctic aerosol use show a several percent decline in monsoon rain—though that may be a plus, since flooding is the #1 cause of death and destruction during the Indian monsoon.

Mostly, it might just plain fail to work. Guessing outcomes is useless, though. Here’s where experiment rules, not simulations. This is engineering, which learns from mistakes. Consider the early days of aviation. Having more time to develop and test a system gives more time to learn how to avoid unwanted impacts. Of course, having a system ready also increases the probability of premature deployment; life is about choices and dangers.

More important right now than developing capability, is understanding the consequences of deployment of that capability by doing field experiments. One thing we know: both science and engineering advance most quickly by using the dance of theory with experiment. Neglecting this, preferring only experiment, is a fundamental mistake.


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