The Story of Science is a nice video series

 I am on the lookout for good video resources about science that I can recommend to others, particularly non-scientists. By chance, I recently came across the BBC production, The Story of Science: Power, Proof, and Passion, hosted by Michael Mosley.

There is also a beautiful book that goes with the series, containing more detail, including colour illustrations. I was able to get the DVDs and the book from my local public library.

I particularly appreciate that science is presented as a human endeavour and progress is influenced by local contexts (economic, political, religious, ...). That can be acknowledged and enjoyed without descending into a social constructivist view of scientific knowledge. In a similar vein, the series does not have an ideological edge, or embrace some common tropes that too often popular video treatments may promote such as science the saviour, science the moneymaker, science the spoiler, science the monster-maker, ... or that science is uncontrollable, is inscrutable, or is the domain of evil/eccentric geniuses,....

The series introduced me to several colourful characters who played key roles in the history of science, including Hennig Brand, Hans Sloan, Georges Cuvier, Horace-Benedict de Saussure, Simon Sevin, Richard Trevithick, ...

Elite imitation and flailing universities

The mission of universities is thinking: teaching students to think and enabling scholars to think about the world we live in. Yet, it is debatable whether most universities in the world achieve these goals. Arguably, things are getting worse. Universities are flailing. Why?

Most universities desperately want to be elite. They want to be like Harvard, Caltech, Oxford, Princeton, Berkeley, Stanford, ...

But non-elite universities do not have the necessary resources to be elite. Yet they are controlled by elites (management on high salaries, faculty educated at elite universities) who want to be elite and so settle for elite imitation.

"A flailing university is what happens when the principal cannot control its agents. The flailing university cannot implement its own plans and may have its plans actively subverted when its agents work at cross-purposes. The non-elite university flails because it is simultaneously too large and too small: too large because the non-elite university attempts to legislate and regulate every aspect of the work of faculty and students and too small because it lacks the resources and personnel to achieve its ambitions. 

To explain the mismatch between the non-elite universities' ambitions and their abilities, consider the premature demands by elites in non-elite universities for goals, policies, curricula, infrastructure, and outcomes more appropriate to an elite university. 

In order to satisfy external actors (government, business, parents, ...) non-elite universities often take on tasks that overwhelm institutional capacity, leading to premature load bearing. As these authors put it, “By starting off with unrealistic expectations of the range, complexity, scale, and speed with which organizational capability can be built, external actors set both themselves and (more importantly) the students and researchers that they are attempting to assist to fail”. 

The expectations of external actors are only one source of imitation, however. Who people read, listen to, admire, learn from, and wish to emulate is also key. Another factor driving inappropriate imitation is that the elites in non-elite universities—senior management and high-profile faculty—are closely connected with business elites and elite universities, usually more closely than they are to the students and faculty at their own university. As a result, this elite initiates and supports policies that appear to it to be normal even though such policies may have little relevance to the student and faculty as a whole and may be wildly at odds with the university capacity. This kind of mimicry of what appear to be the best elite university policies and practices is not necessarily ill intentioned. It is simply one by-product of the background within which the elites operates. University managers engage with business elites and managers at other non-elite universities."

I actually did not write most of the text above, I just took the text from the first two pages of the article below and replaced some words (e.g. Indian state with non-elite university, Indian citizens with students and faculty). 

Premature Imitation and India’s Flailing State 

Shruti Rajagopalan, Alexander T. Tabarrok

I came across the article after listening to a podcast episode that interviews the two authors, recommended by my son.

I also recommend Shutri's own podcast, Ideas of India, including a recent episode, Where did development economics go wrong?

Previously, I have discussed similar issues from the angle of the strategic importance for nation building of non-elite institutions within a country, and how science curricula should focus more on basic skills.

What do you think? Are universities like a flailing state? Is the problem elite imitation?

Why is there so much symmetry in biological systems?

 One of the biggest questions in biology is, What is the relationship between genotypes and phenotypes? In different words, how does a specific gene (DNA sequence) encode information that allows a very specific biological structure with a unique function to emerge?

Like big questions in many fields, this is a question about emergence.

In biology, this mapping from genotype to phenotype occurs at many levels from protein structure to human personality. An example is how the RNA encodes the structure of a SARS-CoV2 virion.

A fascinating thing about biological structures is that many have a certain amount of symmetry. The human body has reflection symmetry and many virions have icosahedral symmetry. What is the origin of this tendency to symmetry? Could evolution produce it?

Scientists will sometimes make statements such as the following about evolution.

Symmetric structures preferentially arise not just due to natural selection but also because they require less specific information to encode and are therefore much more likely to appear as phenotypic variation through random mutations.

How do we know this is true? Can such a statement be falsified? Or at least, can we produce concrete models or biological systems that are consistent with this statement?

There is a fascinating paper in PNAS that addresses the questions above.

Symmetry and simplicity spontaneously emerge from the algorithmic nature of evolution 

Iain G. Johnston, Kamaludin Dingle, Sam F. Greenbury, Chico Q. Camargo, Jonathan P. K. Doye, Sebastian E. Ahnert, and Ard A. Louis 

Here are a few highlights from the article. First, how one gets specific about information content and algorithms.

Genetic mutations are random in the sense that they occur independently of the phenotypic variation they produce. This does not, however, mean that the probability P(p) that a Genotype-Phenotype [GP] map produces a phenotype p upon random sampling of genotypes will be anything like a uniformly random distribution. 

Instead, ... arguments based on the coding theorem of algorithmic information theory (AIT) (7) predict that the P(p) of many GP maps should be highly biased toward phenotypes with low Kolmogorov complexity K(p) (8). 

High symmetry can, in turn, be linked to low K(p) (6, 9–11). An intuitive explanation for this algorithmic bias toward symmetry proceeds in two steps: 

1) Symmetric phenotypes typically need less information to encode algorithmically, due to repetition of subunits. This higher compressibility reduces constraints on genotypes, implying that more genotypes will map to simpler, more symmetric phenotypes than to more complex asymmetric ones (2, 3). 

2) Upon random mutations these symmetric phenotypes are much more likely to arise as potential variation (12, 13), so that a strong bias toward symmetry may emerge even without natural selection for symmetry.

The authors consider several concrete models and biological systems that illustrate this bias toward symmetry. The first involves the structure of protein complexes, as given in the Protein Data Base (PDB).

A) Protein complexes self-assemble from individual units. 

(B) Frequency of 6-mer protein complex topologies found in the PDB versus the number of interface types, a measure of complexity 

𝐾˜
$$K˜(p). 

Symmetry groups are in standard Schoenflies notation: C6, D3, C3, C2, and C1. There is a strong preference for low-complexity/high-symmetry structures. 

(C) Histograms of scaled frequencies of symmetries for 6-mer topologies found in the PDB (dark red) versus the frequencies by symmetry of the morphospace of all possible 6-mers illustrate that symmetric structures are hugely overrepresented in the PDB database. 

Note the logarithmic scales for the probabilities (frequencies), meaning that the probabilities span four orders of magnitude. The authors claim that "many genotype–phenotype maps are exponentially biased toward phenotypes with low descriptional complexity. "

This intuition that simpler outputs are more likely to appear upon random inputs into a computer programming language can be precisely quantified in the field of AIT (7), where the Kolmogorov complexity K(p) of a string p is formally defined as a shortest program that generates p on a suitably chosen universal Turing machine (UTM). 

From AIT the authors produce a bound (equation 1, and below), that exhibits the exponential decay of probability with complexity, similar to that seen in their graphs, such as the one shown below, for a model gene regulatory network that is modeled by 60 ordinary differential equations (ODEs). The red dashed line is the bound below.

𝑃(𝑝)≤2−𝑎𝐾˜(𝑝)−𝑏,  [1

Scaled frequency vs. complexity 𝐾˜(𝑝)for the budding yeast ODE cell cycle model (30). Phenotypes are grouped by complexity of the time output of the key CLB2/SIC1 complex concentration. Higher frequency means a larger fraction of parameters generate this time curve. The red circle denotes the wild-type phenotype, which is one of the simplest and most likely phenotypes to appear. The dashed line shows a possible upper bound from Eq. 1. There is a clear bias toward low-complexity outputs.

One minor comment is that I was surprised that the authors did not reference the classic 1956 paper by Crick and Watson. They introduced the concept of "genetic economy". Prior to any knowledge of the actual structure of virions, they predicted that virions would have icosahedral symmetry because that reduced the cost of the genome coding for the structure of the virion.

Hence, it would be interesting to explore the relationship between the PNAS paper and this one.

Structural puzzles in virology solved with an overarching icosahedral design principle

Reidun Twarock & Antoni Luque

There is a nice New York Times article about the PNAS paper. I thank Sophie van Houtryve for bringing that to my attention leading me to the PNAS paper.