Biology subjects

Louis, A. A.

Publications and source records attributed to Louis, A. A..

10 recordsLinked to original sources

bioRxiv2025

Developmental bias explains the evolutionary trend towards simple leaf shapes

The relative influence of developmental bias and natural selection on evolutionary outcomes remains a central, unresolved question in evolutionary biology. Here, we combine large-scale phylogenetic trees with herbarium data to confirm that, across the evolutionary history of angiosperms, transitions from complex to simple leaf shapes have occurred at a significantly higher rate than the reverse. To investigate whether this directional trend reflects an underlying developmental bias, we simulate evolutionary transitions by introducing random mutations into a well-established computational model of leaf development. Despite excluding selection, the transition rates produced by the model closely align with those observed in the phylogenetic data. This agreement indicates that a strong developmental bias, reflecting the relative ease with which gene regulatory networks generate simple over complex leaves, has played a major role in shaping the evolution of leaf morphology. Our findings echo recent work showing that similar biases towards simplicity influence the evolution of molecular structures, including RNA secondary structures and protein quaternary assemblies. Together, these results suggest that a developmental bias favouring simplicity may be a widespread and general feature of evolutionary systems.

evolutionary biology↗

bioRxiv2025

Predicting the topography of fitness landscapes from the structure of genotype-phenotype maps

Ruggedness, the prevalence of fitness peaks, and navigability, the existence of fitness-increasing paths to a target, are key factors affecting evolution on fitness landscapes. We analytically predict landscape ruggedness for genotype-phenotype maps with randomly assigned fitness, using only the sizes of neutral components - mutationally connected genotype sets sharing the same phenotype- and their evolvabilities, the number of neighbouring phenotypes. Further, more evolvable peaks tend to have higher fitness and a minimal evolvability is required for navigability.

evolutionary biology↗

bioRxiv2024

Controlling DNA-RNA strand displacement kinetics with base distribution

DNA-RNA hybrid strand displacement underpins the function of many natural and engineered systems. Understanding and controlling factors affecting DNA-RNA strand displacement reactions is necessary to enable control of processes such as CRISPR-Cas9 gene editing. By combining multi-scale modelling with strand displacement experiments we show that the distribution of bases along the displacement domain of an invading strand has a very strong effect on reaction kinetics. Merely by redistributing bases within a displacement domain of fixed base composition, we are able to design sequences whose reaction rates span more than two orders of magnitude. We characterize this effect in reactions involving the invasion of dsDNA by an RNA strand and invasion of a hybrid duplex by a DNA strand. We show that oxNA, a recently introduced coarse-grained model of DNA-RNA hybrids, can reproduce trends in experimentally observed reaction rates. We also develop a kinetic model for predicting strand displacement rates. On the basis of these results, we argue that base distribution effects are likely to play an important role in the function of the guide RNAs that direct CRISPR-Cas systems.

biophysics↗

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↗

bioRxiv2023

Maximum Mutational Robustness in Genotype-Phenotype Maps Follows a Self-similar Blancmange-like Curve

Phenotype robustness, defined as the average mutational robustness of all the genotypes that map to a given phenotype, plays a key role in facilitating neutral exploration of novel phenotypic variation by an evolving population. By applying results from coding theory, we prove that the maximum phenotype robustness occurs when genotypes are organised as bricklayers graphs, so called because they resemble the way in which a bricklayer would fill in a Hamming graph. The value of the maximal robustness is given by a fractal continuous everywhere but differentiable nowhere sums-of-digits function from number theory. Interestingly, genotype-phenotype (GP) maps for RNA secondary structure and the HP model for protein folding can exhibit phenotype robustness that exactly attains this upper bound. By exploiting properties of the sums-of-digits function, we prove a lower bound on the deviation of the maximum robustness of phenotypes with multiple neutral components from the bricklayers graph bound, and show that RNA secondary structure phenotypes obey this bound. Finally, we show how robustness changes when phenotypes are coarse-grained and derive a formula and associated bounds for the transition probabilities between such phenotypes.

evolutionary biology↗

bioRxiv2022

Predicting phenotype transition probabilities via conditional algorithmic probability approximations

Unravelling the structure of genotype-phenotype (GP) maps is an important problem in biology. Recently, arguments inspired by algorithmic information theory (AIT) and Kolmogorov complexity have been invoked to uncover simplicity bias in GP maps, an exponentially decaying upper bound in phenotype probability with increasing phenotype descriptional complexity. This means that phenotypes with very many genotypes assigned via the GP map must be simple, while complex phenotypes must have few genotypes assigned. Here we use similar arguments to bound the probability P (x [->] y) that phenotype x, upon random genetic mutation, transitions to phenotype y. The bound is [Formula], where [Formula] is the estimated conditional complexity of y given x, quantifying how much extra information is required to make y given access to x. This upper bound is related to the conditional form of algorithmic probability from AIT. We demonstrate the practical applicability of our derived bound by predicting phenotype transition probabilities (and other related quantities) in simulations of RNA and protein secondary structures. Our work contributes to a general mathematical understanding of GP maps, and may facilitate the prediction of transition probabilities directly from examining phenotype themselves, without utilising detailed knowledge of the GP map.

evolutionary biology↗

bioRxiv2021

The structure of genotype-phenotype maps makes fitness landscapes navigable

Fitness landscapes are often described in terms of peaks and valleys, implying an intuitive low-dimensional landscape of the kind encountered in everyday experience. The space of genotypes, however, is extremely high-dimensional, which results in counter-intuitive properties of genotype-phenotype maps, such as the close proximity of one phenotype to many others. Here we investigate how common structural properties of high-dimensional genotype-phenotype maps, such as the presence of neutral networks, affect the navigability of fitness landscapes. For three biologically realistic genotype-phenotype map models--RNA secondary structure, protein tertiary structure and protein complexes--we find that, even under random fitness assignment, fitness maxima can be reached from almost any other phenotype without passing through a fitness valley. This in turn implies that true fitness valleys are very rare. By considering evolutionary simulations between pairs of real examples of functional RNA sequences, we show that accessible paths are also likely to be utilised under evolutionary dynamics.

evolutionary biology↗

bioRxiv2020

Phenotype bias determines how RNA structures occupy the morphospace of all possible shapes

Morphospaces representations of phenotypic characteristics are often populated unevenly, leaving large parts unoccupied. Such patterns are typically ascribed to contingency, or else to natural selection disfavouring certain parts of the morphospace. The extent to which developmental bias, the tendency of certain phenotypes to preferentially appear as potential variation, also explains these patterns is hotly debated. Here we demonstrate quantitatively that developmental bias is the primary explanation for the occupation of the morphospace of RNA secondary structure (SS) shapes. Upon random mutations, some RNA SS shapes (the frequent ones) are much more likely to appear than others. By using the RNAshapes method to define coarse-grained SS classes, we can directly compare the frequencies that non-coding RNA SS shapes appear in the RNAcentral database to frequencies obtained upon random sampling of sequences. We show that: a) Only the most frequent structures appear in nature; the vast majority of possible structures in the morphospace have not yet been explored. b) Remarkably small numbers of random sequences are needed to produce all the RNA SS shapes found in nature so far. c) Perhaps most surprisingly, the natural frequencies are accurately predicted, over several orders of magnitude in variation, by the likelihood that structures appear upon uniform random sampling of sequences. The ultimate cause of these patterns is not natural selection, but rather strong phenotype bias in the RNA genotype-phenotype map, a type of developmental bias or "findability constraint", which limits evolutionary dynamics to a hugely reduced subset of structures that are easy to "find".

evolutionary biology↗

bioRxiv2018

Rational design of hidden thermodynamic driving through DNA mismatch repair

Recent years have seen great advances in the development of synthetic self-assembling molecular systems. Designing out-of-equilibrium architectures, however, requires a more subtle control over the thermodynamics and kinetics of reactions. We propose a new mechanism for enhancing thermodynamic drive of DNA strand displacement reactions whilst barely perturbing forward reaction rates - introducing mismatches in an internal location within the initial duplex. Through a combination of experiment and simulation, we demonstrate that displacement rates are strongly sensitive to mismatch location and can be tuned by rational design. By placing mismatches away from duplex ends, the thermodynamic drive for a strand-displacement reaction can be varied without significantly affecting the forward reaction rate. This hidden thermodynamic driving motif is ideal for the engineering of nonequilibrium systems that rely on catalytic control and must be robust to leak reactions.

synthetic biology↗