Biology subjects

Camargo, C. Q.

Publications and source records attributed to Camargo, C. Q..

3 recordsLinked to original sources

bioRxiv2023

Non-Poissonian bursts in the arrival of phenotypic variation can strongly affect the dynamics of adaptation

The introduction of novel phenotypic variation in a population through random mutations plays a crucial role in evolutionary dynamics. Here we show that, when the probability that a sequence has a particular phenotype in its 1-mutational neighbourhood is low, statistical fluctuations imply that in the weak-mutation or monomorphic regime, novel phenotypic variation is not introduced at a constant rate, but rather in non-Poissonian "bursts". In other words, a novel phenotype appears multiple times in quick succession, or not at all for many generations. We use the RNA secondary-structure genotype-phenotype map to explore how increasing levels of heterogeneity in mutational neighbourhoods strengthen the bursts. Similar results are obtained for the HP model for protein tertiary structure and the Biomorphs model for morphological development. Burst can profoundly affect adaptive dynamics. Most notably, they imply that differences in arrival rates of novel variation can influence fixation rates more than fitness differences do.

evolutionary biology↗

bioRxiv2023

Bias in the arrival of variation can dominate over natural selection in Richard Dawkins' biomorphs

Biomorphs, Richard Dawkins iconic model of morphological evolution, are traditionally used to demonstrate the power of natural selection to generate biological order from random mutations. Here we show that biomorphs can also be used to illustrate how developmental bias shapes adaptive evolutionary outcomes. In particular, we find that biomorphs exhibit phenotype bias, a type of developmental bias where certain phenotypes can be many orders of magnitude more likely than others to appear through random mutations. Moreover, this bias exhibits a strong Occams-razor-like preference for simpler phenotypes with low descriptional complexity. Such bias towards simplicity is formalised by an information-theoretic principle that can be intuitively understood from a picture of evolution randomly searching in the space of algorithms. By using population genetics simulations, we demonstrate how moderately adaptive phenotypic variation that appears more frequently upon random mutations will fix at the expense of more highly adaptive biomorph phenotypes that are less frequent. This result, as well as many other patterns found in the structure of variation for the biomorphs, such as high mutational robustness and a positive correlation between phenotype evolvability and robustness, closely resemble findings in molecular genotype-phenotype maps. Many of these patterns can be explained with an analytic model based on constrained and unconstrained sections of the genome. We postulate that the phenotype bias towards simplicity and other patterns biomorphs share with molecular genotype-phenotype maps may hold more widely for developmental systems, which would have implications for longstanding debates about internal versus external causes in evolution.

evolutionary biology↗

bioRxiv2021

Symmetry and simplicity spontaneously emerge from the algorithmic nature of evolution

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. But 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 non-adaptive 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 formalise this intuition, leading to the prediction that many genotype-phenotype maps are exponentially biased towards phenotypes with low descriptional complexity. A preference for symmetry is a special case of this bias towards compressible descriptions. We test these predictions with extensive biological data, showing that that protein complexes, RNA secondary structures, and a model gene-regulatory network all exhibit the expected exponential bias towards 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.

evolutionary biology↗