Biological structures frequently exhibit modularity and symmetry, but the origin of such trends is not well understood. It can be tempting to assume — by analogy to engineering design — that symmetry and modularity arise from natural selection. However, evolution, unlike engineers, cannot plan ahead, and so these traits must also afford some immediate selective advantage which is hard to reconcile with the breadth of systems where symmetry is observed. In a new paper, published in the Proceedings of the National Academy of Sciences, scientists introduce an alternative hypothesis based on an algorithmic picture of evolution; it suggests that 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 genetic variation through random mutations.
From snowflakes to sunflowers, starfish to sharks, symmetry is everywhere in nature.
Not just in the body plans which govern shape and form, but right down to the microscopic molecular machines keeping cells alive.
Although there is a larger collection of asymmetrical forms in the natural world, symmetrical patterns seem to occur more often than you would expect if due to sheer random chance.
It is tempting to assume that evolution is looking at the advantages of simple modular and symmetrical shapes just as engineers, architects and Swedish furniture designers do.
Biologists, however, will point out that evolution works one generation at a time — rather than making adaptations for future benefits — and there needs to be an immediate evolutionary advantage for a mutation to stick.
At this stage, it is useful to remind ourselves that there are two stages in evolutionary development.
The first is the genetic mutation that causes a variation in a particular physical characteristic (a phenotype) and the second is the natural selection that leads to some traits dominating over others.
“Most evolutionary theory concentrates on the second ‘survival of the fittest’ step,” said University of Oxford’s Professor Ard Louis.
“But what if the first ‘arrival of variation’ step is highly biased towards phenotypes high in symmetry or modularity. Could that lead to the bias towards these traits that we observe in nature.”
Professor Louis and colleagues put together data from protein clusters, RNA molecules and generic circuits and found that, despite the myriad of different shapes and structures, there was a startling bias towards simple structural symmetry.
Running computer simulations on the same biological systems confirmed the bias in nature. A protein cluster simulation with 13,079,255 different possible structure shapes had only five shapes with the symmetry of a square.
All things being equal, that means there would be a five in thirteen million chance of that simple square being returned.
Yet applying the evolutionary algorithm returned one of those five simple squares 30% of the time.
The study authors looked to computer science for the secret behind nature’s sleight of hand as they continued their investigation into the bias.
In algorithmic information theory (AIT), the complexity of an object is measured by the length of its shortest description.
For example, a sequence of the letters AB a million times could be described as either a million repetitions of AB or the full sequence of ABABAB in its entirety.
The shorter description is less complex and considerably more efficient — 28 characters, rather than a million characters. A random sequence with no possible ‘shorthand’ to describe it would be truly complex.
“It’s much more efficient to follow an instruction that says, ‘do this, and then repeat it x times,’ than to follow all the detailed instructions required for a more complex asymmetrical shape,” Professor Louis said.
The idea that nature often follows a less complex set of instructions, which are simpler to follow, is behind the paper’s key message — the notion of a distinct developmental bias.
The scientists suggest that the idea of a deck stacked with shapes as prescribed by shorter, simpler ‘instructions’ at the point of phenotype variation offers a much better explanation for the statistical improbability of so many symmetrical shapes in nature.
“The question of whether or not bias in the arrival of variation has an impact on evolutionary outcomes has been highly contested for many decades,” Professor Louis said.
“Our examples are simple enough to allow us to address this question head-on, with clear results pointing towards the critical importance of such bias.”
Iain G. Johnston et al. 2022. Symmetry and simplicity spontaneously emerge from the algorithmic nature of evolution. PNAS 119 (11): e2113883119; doi: 10.1073/pnas.2113883119
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