Biology subjects

Martin, N. S.

Publications and source records attributed to Martin, N. S..

10 recordsLinked to original sources

bioRxiv2026

Using a simplified Rough Mount Fuji model to disentangle how multi-peaked fitness landscapes can be highly navigable

Evolving populations, especially in the strong-selection-weak-mutation limit, can be modelled as adaptive walks on fitness landscapes, moving in fitness-increasing mutational steps until reaching a fitness peak--a local optimum. Simulations of such adaptive walks--on a multi-peaked empirical landscape of the folA gene and on landscapes generated by the Rough Mount Fuji (RMF) model-- have shown that some landscapes are highly navigable, meaning that the highest x% of peaks are reached by >> x% of adaptive walks. This prompts the question of how adaptive walks can be so successful despite the local, myopic rules behind each adaptive step. Here, we investigate this question using simulations and mathematical approximations of random adaptive walks on a simplified RMF landscape. The landscape has a low-to-intermediate fitness region, whose size reconciles a low peak density with a high peak number. Despite the high number of peaks, walkers are likely to exit this region without terminating at a peak because the probability of a peak transition at each step is low and a fitness gradient guides walkers to the high-fitness region in few steps. Thus, three features are sufficient to explain why adaptive walks in the simplified RMF landscape are likely to reach a small fraction of top-ranking peaks: a low-to-intermediate fitness region with a high number of peaks, a low peak-transition probability, and which is crossed in few steps. We find that these three features are also present in the empirical folA landscape, suggesting that similar principles may apply.

evolutionary biology↗

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

RNA secondary structures are conserved but random

Noncoding RNAs perform a wide range of essential biological functions, and their secondary structures are often conserved by purifying selection. However, such conservation does not necessarily imply that positive selection shaped their evolutionary origins. Here, we test for global signs of positive selection by studying the distribution of secondary structures in naturally occurring noncoding RNA. We find that, to first order, these structures are statistically indistinguishable from those produced by a relatively small set of randomly generated sequences. The distributions are, however, profoundly shaped by a strong bias in the arrival of phenotypic variation, such that only an exponentially small subset of all possible structures is likely to occur in nature. In other words, the secondary structure repertoire of natural noncoding RNAs largely reflects this developmental bias rather than further adaptive fine-tuning. Detecting genuine signatures of selection, beyond randomness, in the distribution of secondary structures, therefore requires careful calibration against appropriate null models that account for the underlying bias. We perform a large-scale and detailed analysis of four extensive datasets covering a wide spectrum of functional RNA classes. We describe one potential signature of adaptation on structure: archaeal ribosomal RNA structures are simpler and more robust than predictions of the sequence null model, and hyperthermophiles are less complex than archaea in other niches, but the effects are relatively small. This example illustrates the difficulty of inferring a creative role for natural selection in shaping evolutionary outcomes for RNA secondary structure.

evolutionary biology↗

bioRxiv2025

A simple model captures key characteristics of biological non-deterministic genotype-phenotype maps

By connecting genotypic mutations to the higher-order phenotypes relevant for selection, genotype-phenotype (GP) maps play a key role in evolution. GP maps are typically investigated using computational models of molecular phenotypes (for example, RNA secondary structures and simplified models of protein tertiary and quaternary structures), but GP map concepts are relevant beyond these specific models and so a systematic, model-independent approach is needed. This can be achieved by characterising GP maps in terms of properties like phenotypic frequencies, mutational robustness and evolvabilities, which can be computed for any given GP map. This approach has given insight into the shared features of GP maps and their evolutionary relevance. However, this progress is largely limited to the simplest case, where each genotype corresponds to a single, categorical phenotype. Here, I turn to a more realistic, but also more complex non-deterministic (ND) treatment, where each genotype generates an ensemble of phenotypes: I start by comparing the ND GP maps of the three biophysical models mentioned above using a recently proposed framework. Then, I find a simpler, synthetic map, which replicates key shared features for a range of modelling choices, suggesting that few ingredients are needed for these features to appear and highlighting the importance of non-linearities in GP maps. This synthetic ND GP map can be useful as a realistic, conceptually and computationally simpler model for future analyses of ND GP maps, their properties and their implications for evolutionary processes.

evolutionary biology↗

bioRxiv2025

Combined inhibition of NAD synthesis and C-terminal binding protein cooperatively induce cell death and inhibit growth of High Grade Serous Ovarian Carcinoma

The transcriptional scaffolds C-terminal Binding Proteins (CtBP) 1 and 2 are overexpressed and act as oncogenic dependencies in multiple cancers but importantly encode a chemically targetable dehydrogenase domain. CtBP promotes survival of high grade serous ovarian carcinoma (HGSOC) cells by repressing expression of Death Receptors (DR) 4 and 5, which activate caspase 8-dependent apoptosis. We have previously developed a series of substrate competitive CtBP dehydrogenase inhibitors active in multiple cell and preclinical solid tumor models. In the current study, we validated CtBP 1 and 2 overexpression in a longitudinal series of primary and metastatic/recurrent HGSOC cases. Furthermore, our lead CtBP dehydrogenase inhibitor, JW-98 induced apoptosis and exhibited variable single agent IC50 values in HGSOC cell lines, but depletion of nicotinamide adenine dinucleotide (NAD) using the NAD synthesis inhibitor GMX1778 strikingly sensitized tumor cells to JW-98 treatment. Mechanistically, the JW-98/GMX-1778 combination effectively disrupted CtBP dimerization that requires stoichiometric levels of intracellular NAD and is required for oncogenic transcriptional activities. Highlighting the translational potential of this combination, combined JW-98/GMX1778 treatment of OVCAR3 HGSOC xenografts in immunodeficient mice abrogated tumor growth without observable toxicity. CtBP/NAD combined inhibition represents a novel therapeutic strategy that could improve outcomes in chemoresistant HGSOC.

cancer 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↗

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

The non-deterministic genotype-phenotype map of RNA secondary structure

Selection and variation are both key aspects in the evolutionary process. Previous research on the mapping between molecular sequence (genotype) and molecular fold (phenotype) has shown the presence of several structural properties in different biological contexts, implying that these might be universal in evolutionary spaces. The deterministic genotype-phenotype (GP) map that links short RNA sequences to minimum free energy secondary structures has been studied extensively because of its computational tractability and biologically realistic nature. However, this mapping ignores the phenotypic plasticity of RNA. We define a GP map that incorporates non-deterministic phenotypes, and take RNA as a case study; we use the Boltzmann probability distribution of folded structures and examine the structural properties of non-deterministic (ND) GP maps for RNA sequences of length 12 and coarse-grained RNA structures of length 30 (RNAshapes30). A framework is presented to study robustness, evolvability and neutral spaces in the non-deterministic map. This framework is validated by demonstrating close correspondence between the non-deterministic quantities and sample averages of their deterministic counterparts. When using the non-deterministic framework we observe the same structural properties as in the deterministic GP map, such as bias, negative correlation between genotypic robustness and evolvability, and positive correlation between phenotypic robustness and evolvability.

biophysics↗

bioRxiv2023

The Boltzmann distributions of folded molecular structures predict likely changes through random mutations

New folded molecular structures can only evolve after arising through mutations. This aspect is modelled using genotype-phenotype (GP) maps, which connect sequence changes through mutations to changes in molecular structures. Previous work has shown that the likelihood of appearing through mutations can differ by orders of magnitude from structure to structure and that this can affect the outcomes of evolutionary processes. Thus, we focus on the phenotypic mutation probabilities{phi} qp, i.e. the likelihood that a random mutation changes structure p into structure q. For both RNA secondary structures and the HP protein model, we show that a simple biophysical principle can explain and predict how this likelihood depends on the new structure q:{phi} qp is high if sequences that fold into p as the minimum-free-energy structure are likely to have q as an alternative structure with high Boltzmann frequency. This generalises the existing concept of plastogenetic congruence from individual sequences to the entire neutral spaces of structures. Our result helps us understand why some structural changes are more likely than others, can be used as a basis for estimating these likelihoods via sampling and makes a connection to alternative structures with high Boltzmann frequency, which could be relevant in evolutionary processes.

biophysics↗