Symmetry and simplicity spontaneously emerge from the algorithmic nature of evolution

Journal article

Engineers routinely design systems to be modular and symmetric in order to increase robustness to perturbations and to facilitate alterations at a later date. Biological structures also frequently exhibit modularity and symmetry, but the origin of such trends is much less 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. Here we introduce an alternative nonadaptive 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 phenotypic variation through random mutations. Arguments from algorithmic information theory can formalize this intuition, leading to the prediction that many genotype–phenotype maps are exponentially biased toward phenotypes with low descriptional complexity. A preference for symmetry is a special case of this bias toward compressible descriptions. We test these predictions with extensive biological data, showing that protein complexes, RNA secondary structures, and a model gene regulatory network all exhibit the expected exponential bias toward simpler (and more symmetric) phenotypes. Lower descriptional complexity also correlates with higher mutational robustness, which may aid the evolution of complex modular assemblies of multiple components.

Authors: Johnston, IG; Dingle, K; Greenbury, SF; Camargo, CQ; Doye, JPK

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: National Academy of Sciences
• Journal: Proceedings of the National Academy of Sciences of the United States of America
• Volume: 119
• Issue: 11
• Article number: e2113883119
• Publication date: 2022-03-11
• Acceptance date: 2021-12-20
• DOI: 10.1073/pnas.2113883119
• EISSN: 1091-6490
• ISSN: 0027-8424
• Language: English
• Keywords: phenotypic bias; algorithmic information theory; FFR; evolutionary theory