Emergence in ant colonies

Go to the ant, you sluggard;
    consider its ways and be wise!
It has no commander,
    no overseer or ruler,
yet it stores its provisions in summer

and gathers its food at harvest.

    Proverbs 6:6-8

Ant colonies are amazing. It is incredible what they can achieve. I love the video below. It highlights how complex structures and functions emerge in an ant colony even though there is no individual directing the whole operation.

Ant colonies are often cited as an example of emergence, including how complexity can emerge from simple rules. Ant colonies feature in Godel, Escher, Bach by Douglas Hofstadter, Emergence: from chaos to order by John Holland, and Emergence: The Connected Lives of Ants, Brains, Cities, and Software by Steven Johnson.

Important steps towards describing and understanding a system with emergent properties include identifying how to break down the system into single components and determining how those components interact with one another.

As described in the video the colony is composed of several distinct classes (castes) of ant: soldiers, excavators, foragers, garbage collectors, and gardeners. Each ant has a very limited repertoire of methods to interact with other ants and their environment. Ants have poor hearing and sight. They communicate with a few signals involving touch, but mostly communicate by producing trails of distinct chemicals (pheromones).  Each organic molecule is identified with a specific message such as follow this trail, detection of food, presence of an enemy, or danger.

For an ant colony the components are simple and the interactions between the parts are simple. Nevertheless, complex structures such as bridges and tree houses emerge. There is no chief engineer directing the construction of these structures or a blueprint drawn up by an architect. The queen is not a dictator mandating that the colony must last for her lifetime, which covers many generations of worker ants.

Ant colonies have characteristic properties of emergent systems. The system has properties that the individual components do not. Complex structures can emerge from a system with simple components and interactions. The properties that emerge are hard to predict a priori. That is if one only knew about the properties of individual ants and how they interact, and not the properties of the colony, it would be hard to predict that they could achieve what they do.

Universality is highlighted in a nice review article, The principles of collective animal behaviour by D.J.T Sumpter. Some of the abstract is below.

I argue that the key to understanding collective behaviour lies in identifying the principles of the behavioural algorithms followed by individual animals and of how information flows between the animals. These principles, such as positive feedback, response thresholds and individual integrity, are repeatedly observed in very different animal societies. The future of collective behaviour research lies in classifying these principles, establishing the properties they produce at a group level and asking why they have evolved in so many different and distinct natural systems.

Who should get to attend elite universities in the USA?

Equitable access to good education is a desirable goal. Yet it rarely happens and debate about how to achieve it can be diluted by focusing on access to elite institutions and on "culture war" rhetoric.

This week The Economist had a leader (editorial) about admission policies for universities in the USA. Below I reproduce some of the leader, highlighting some points I found poignant.

A diversity of backgrounds in elite institutions is a desirable goal. In pursuing it, though, how much violence should be done to other liberal principles—fairness, meritocracy, the treatment of people as individuals and not avatars for their group identities? At present, the size of racial preferences is large and hard to defend. The child of two college-educated Nigerian immigrants probably has more advantages in life than the child of an Asian taxi driver or a white child born into Appalachian poverty. Such backgrounds all add to diversity. But, under the current regime, the first is heavily more favoured than the others.

Racial preferences are not, however, the most galling thing about the ultra-selective universities that anoint America’s elite. ...A startling 43% of white students admitted to Harvard enjoy some kind of non-academic admissions preference: being an athlete, the child of an alumnus, or a member of the dean’s list of special applicants (such as the offspring of powerful people or big donors). 

A cynic could argue that racial balancing works as a virtue-signalling veneer atop a grotesquely unfair system. A study published in 2017 found that most of Harvard’s undergraduates hailed from families in the top 10% of the income distribution. Princeton had more students from the top 1% than the bottom 60%. When this is the case, it seems unfair that it is often minority students—not the trust-funders—who have their credentials questioned. University presidents and administrators who preen about all their diverse classes might look at how Britain—a country of kings, queens, knights and lords—has fostered a university system that is less riven with ancestral privilege.

...Legacy admissions should be ended. Colleges claiming that alumni donations would wither without them should look to Caltech, MIT and Johns Hopkins— ....[who all] ditched the practice..

In some ways, the question of who gets into a handful of elite universities is a distraction from the deeper causes of social immobility in America. Schooling in poorer neighbourhoods was dismal even before covid-19. The long school closures demanded by teachers’ unions wiped out two decades of progress in test scores for nine-year-olds, with hard-up, black and Hispanic children worst affected. Efforts to help the needy should start before birth and be sustained throughout childhood. Nothing the Supreme Court says about the consideration of race in college admissions will affect the more basic problem, that too few Americans from poorer families are sufficiently well-nurtured or well-taught to be ready to apply to college. However the court rules, that is a debate America needs to have.

On a related note, Malcolm Gladwell has a fascinating podcast episode, Outliers, Revisited  that brings out some of the issues including, how privileged parents game the system for their children.

Did Turing really "explain" pattern formation?

 Exactly seventy years ago, Alan Turing published a seminal article, in which he proposed a simple reaction-diffusion model for pattern formation in biological systems. The basic idea is that there are two molecules (morphogens) that react with one another chemically and also diffuse through the system.

The potential relevance of the model can be seen by comparing the lower panels below. The left panel is a real fish and the right panel shows the results of a simulation. The figure above is taken from a beautiful review article published a decade ago.

Reaction-Diffusion Model as a Framework for Understanding Biological Pattern Formation  Shigeru Kondo and Takashi Miura

The authors state that the model is not accepted by many experimental biologists and hope their review will lead to a greater engagement with it. Some of the reasons are related to issues in the philosophy of science and how to model complex systems. What is an explanation? What is the role of simple models for complex systems that ignore so many details?

Kondo and Miura point out the universality of the reaction-diffusion model in the sense that a similar model can be derived where the "molecules" are instead a circuit of cellular signals. Diffusion can be replaced by a relay of signals between cells. Alfred Gierer and  Hans Meinhardt in 1972 showed that all that is required is a network with "a short-range positive feedback [competing] with a long-range negative feedback."

A short video from the Sante Fe Institute also provides a helpful introduction including some simulations.

 

There is another problem with Turing's model that is succinctly described in the opening paragraph of a recent PRL. In a system with two molecular species, patterns only form when there is a large difference between the diffusivity of the two molecules. However, this seems unrealistic because one expects the molecules to have comparable diffusivities.

Turing’s Diffusive Threshold in Random Reaction-Diffusion Systems 

Pierre A. Haas and Raymond E. Goldstein 

 In 1952, Turing described the pattern-forming instability that now bears his name [1]: diffusion can destabilize a fixed point of a system of reactions that is stable in well-mixed conditions. Nigh on threescore and ten years on, the contribution of Turing’s mechanism to chemical and biological morphogenesis remains debated, not least because of the diffusive threshold inherent in the mechanism: chemical species in reaction systems are expected to have roughly equal diffusivities, yet Turing instabilities cannot arise at equal diffusivities [2,3]. It remains an open problem to determine the diffusivity difference required for generic systems to undergo this instability, yet this diffusive threshold has been recognized at least since reduced models of the Belousov–Zhabotinsky reaction [4,5] only produced Turing patterns at unphysically large diffusivity differences.

I first became aware of this paper through a commentary by Changbong Hyeon, at the Journal Club for Condensed Matter. It is also helpful because it explains the simple mathematics behind the threshold value of the model parameters for pattern formation. 

A few things I have learnt from professional editors

 Until a few years ago I had never engaged with or received feedback from my writing from a professional editor. This is because the only genre I wrote that involved an editor was papers for scientific journals. But the editors of journals are not really editors in the literary sense. They are more like gatekeepers. Colleagues and collaborators may provide feedback on written work, but again they are amateurs.

In the past few years, I have been writing some popular articles and a popular book and have been part of a writing group. In the process, I have engaged with several professional editors. They were getting paid to make my writing better. I have learnt a lot. Here are a few of the things. On the one hand, some of this may not seem that relevant to scientific articles and grant applications. On the other hand, think of the joy of reading a beautiful scientific article, such as those by Roald Hoffmann. Think of how many papers you try to read and you cannot figure out what they are actually about. Also, I think this is particularly relevant to writing review articles, somewhat of a lost art.

Can it be shorter? Most of the writing I have worked with editors on had a strict word limit. I struggled to stay within it. However, the editors forced/helped me in two ways. First, the fixed word limit helped me structure the work and be realistic about the volume of content. For example, for my Very Short Introduction, I broke down the 35,000-word limit to ten chapters, each of about 3500 words. This made the writing quite manageable. Second, editors helped by cutting out content that was not essential, even when I loved it. Third, editors rewrote some of my sentences making them both shorter and clearer. Seeing their improvements I became aware of some of my bad habits.

Find your voice and tell a story. We are all unique and each piece of writing is unique and is making a unique point. Don't try and be someone else. A grant application needs to make the case that your proposed project is unique and that you are uniquely qualified to do it. Your writing will be more engaging and compelling if it expresses your unique perspective and there is a natural narrative.

A few of these suggestions overlap with some of Stephen King's writing tips. 

Self-organisation in complex fluids

 I am at the beach this week and so a lot of time is spent staring at waves, clouds, sunsets, and patterns in the sand. There is a lot of beauty and a lot of beautiful science, most of which I know only a little about. For example, what is the essential physics and simplest theory that can explain the patterns below?

To start understanding the beautiful patterns seen in natural systems I have found helpful the two-page Quick Study in Physics Today

The universe in a cup of coffee by John Wettlaufer

Your morning java or tea is a rotating, cooling laboratory that reflects the physics of such large-scale phenomena as stellar dynamics and energy transport in Earth’s atmosphere and oceans. 

A nice demonstration is to put the hot liquid in a glass jar and then just add a few drops of cold milk and see the beautiful patterns that emerge.

The key idea is there is a balance between thermal bouyancy (hot air rises) and viscous stresses. This balance can lead to symmetry breaking and self-organisation. In planetary systems rotation can play a significant role, particularly when there is a balance of viscous forces and the coriolis force. This can lead to the formation of vortices. The Quick Study includes snapshops from a video that is worth watching,  supplementary material from this PRL.

The article also discusses the importance of Rayleigh-Benard convection in many geophysical phenomena. Something interesting I learnt is that this is actually a misnomer, as is often the case in science. According to Wikipedia, 

This pattern of convection, whose effects are due solely to a temperature gradient, was first successfully analyzed in 1916 by Lord Rayleigh (1842–1919).^[16] Rayleigh assumed boundary conditions in which the vertical velocity component and temperature disturbance vanish at the top and bottom boundaries (perfect thermal conduction). Those assumptions resulted in the analysis losing any connection with Henri Bénard's experiment. This resulted in discrepancies between theoretical and experimental results until 1958, when John Pearson (1930– ) reworked the problem based on surface tension.^[9] This is what was originally observed by Bénard.

Systemic flaws that are undermining good science

Everyone likes to be right. But, sometimes I really wish I was wrong, particularly about problems I see in the world. I wish I was wrong about science being broken. Some of these issues I discuss in the final chapter of Condensed Matter Physics: A Very Short Introduction, due to the relevance of these problems to the future of the field.

Similar concerns were discussed with greater clarity, way back in 2014, by four scientists who are much more experienced and distinguished than I am. 

Rescuing US biomedical research from its systemic flaws 

Bruce Alberts, Marc W. Kirschner, Shirley Tilghman, and Harold Varmus

Positions the different authors have held include President of the US Academy of Sciences, President of Princeton University, and Director of the National Institutes of Health.

Although the article focuses on biomedical research I think the three words "medicine, biomedical, and biology" could be replaced respectively with "technology, materials science, and condensed matter physics" almost everywhere in the article. 

Here are a few quotes.

The long-held but erroneous assumption of never-ending rapid growth in biomedical science has created an unsustainable hypercompetitive system that is discouraging even the most outstanding prospective students from entering our profession—and making it difficult for seasoned investigators to produce their best work. This is a recipe for long-term decline, and the problems cannot be solved with simplistic approaches. Instead, it is time to confront the dangers at hand and rethink some fundamental features of the US biomedical research ecosystem.

... the remarkable outpouring of innovative research from American laboratories—high-throughput DNA sequencing, sophisticated imaging, structural biology, designer chemistry, and computational biology—has led to impressive advances in medicine and fueled a vibrant pharmaceutical and biotechnology sector. In the context of such progress, it is remarkable that even the most successful scientists and most promising trainees are increasingly pessimistic about the future of their chosen career.

... hypercompetition for the resources and positions that are required to conduct science suppresses the creativity, cooperation, risk-taking, and original thinking required to make fundamental discoveries.

The system now favors those who can guarantee results rather than those with potentially path-breaking ideas that, by definition, cannot promise success. Young investigators are discouraged from departing too far from their postdoctoral work, when they should instead be posing new questions and inventing new approaches. Seasoned investigators are inclined to stick to their tried-and-true formulas for success rather than explore new fields. 

One manifestation of this shift to short-term thinking is the inflated value that is now accorded to studies that claim a close link to medical practice. Human biology has always been a central part of the US biomedical effort... Many surprising discoveries, powerful research tools, and important medical benefits have arisen from efforts to decipher complex biological phenomena in model organisms. In a climate that discourages such work by emphasizing short-term goals, scientific progress will inevitably be slowed, and revolutionary findings will be deferred.

As competition for jobs and promotions increases, the inflated value given to publishing in a small number of so-called “high impact” journals has put pressure on authors to rush into print, cut corners, exaggerate their findings, and overstate the significance of their work. 

The development of original ideas that lead to important scientific discoveries takes time for thinking, reading, and talking with peers. Today, time for reflection is a disappearing luxury for the scientific community. 

...administrative tasks are taking up an ever-increasing fraction of the day and present serious obstacles to concentration on the scientific mission itself. 

The following is particularly true of luxury journals. 

Professional editors are increasingly serving in roles played in the past by working scientists and can undermine the enterprise when they base judgments about publication on newsworthiness rather than scientific quality. 

Even after they have landed a research position in academia or research institutes, new investigators wait an average of 4–5 y to receive federal funding for their work compared with 1 y in 1980 (2). Two stark statistics tell much of the tale—the average age at which PhD recipients assume their first tenure-track job is 37 y, and they are approaching 42 y when they are awarded their first NIH grant.

Although it varies across fields and individuals, I get the impression that most scientists do their best work in the rough age range of 35-45. Currently, people are spending most of these years looking for a permanent job and then applying for grants, rather than actually doing science.

The graph below shows just how much the system changed in just thirty years. NIH grants became "gentrified". In different words, all the grants now go to "old farts" doing the same old thing, rather than to "young turks" who want to try new things and have a real impact.

Percentage of NIH R01 Principal Investigators aged 36 and younger and aged 66 and older, 1980–2010

The authors did make some concrete proposals and in a follow-up article, they discuss a broader meeting held to discuss the issues.

Addressing systemic problems in the biomedical research enterprise

To what extent progress has been made in the biomedical community in the past eight years I do not know.

Probing the relationship between superexchange and superconductivity in cuprates

One of the most basic ideas in science is the controlled experiment. A single "independent" variable is changed while all others are held fixed. One then observes how the properties of the system change. Unfortunately, reality is more complicated and there are rarely any truly independent variables, particularly in materials science.

Since the discovery of cuprate superconductors one-quarter of a century ago there has been a constant struggle to tease out systematic trends that can provide insight into the underlying physics causing the superconductivity. This is a challenge because it is difficult to change only one variable. For example, a key property is how the superconductivity changes with the chemical composition of the material, particularly with regard to the doping level, i.e., the density of charge carriers. The problem is that with changes in doping, many other things change as well: the amount of disorder, the periodicity and strength of magnetic interactions, crystal structure, ... 

There is a beautiful experimental paper that recently overcomes these problems. 

On the electron pairing mechanism of copper-oxide high temperature superconductivity

Shane M. O’Mahony, Wangping Ren, Weijiong Chen,  Yi Xue Chong, Xiaolong Liu, H. Eisaki, S. Uchida, M. H. Hamidian, and J. C. Séamus Davis 

In a very clever way they can do all their measurements on a single material of fixed chemical composition, and yet vary a key parameter, the size of the energy difference between the relevant oxygen and copper electronic states, Epsilon.

In the material under study,  Bi2Sr2CaCu2O8+x, there are CuO5 units, as pictured below. In the crystal there is a modulation of delta, the distance at which the fifth oxygen sits above the CuO4 squares that form the square lattices that comprise the layers responsible for the superconductivity.

Due to electrostatics, the distance delta has an effect on the energy Epsilon. This in turn changes the size of the magnetic superexchange between neighbouring copper spins, as pictured below.

In the experiment, a STM is used to measure how Epsilon varies as delta varies (see the red dots in the Figure below). We then expect this to vary the superexchange.

An electron-pair (Josephson) STM is used to measure the magnitude of the superfluid density (electron-pair density) and how it changes with delta (see the blue dots in the figure below).

These two sets of measurement are combined in the second figure below. 

The yellow band in the figure above is the range of values expected from theory, including the recent paper.

Oxygen hole content, charge-transfer gap, covalency, and cuprate superconductivity

Nicolas Kowalski, Sidhartha Shankar Dash, Patrick Sémon, David Sénéchal, and André-Marie Tremblay

The theory is based on DMFT calculations for a three-band Hubbard model, following earlier work including by Weber, Haule, Kotliar, and independently by Maier.

Quanta magazine has a popular report on the experiment. The headline, "High-Temperature Superconductivity Understood at Last", overstates the significance of the experiment.

There are still issues of correlation versus causality. I would also like to see what other theories predict for the relationship between Epsilon and the pairing density. Nevertheless, it is a beautiful experiment and marks a significant advance.