Towards More Relevant Evolutionary Models: Integrating an Artificial Genome With a Developmental Phenotype

The relationship between the genotype and phenotype of organisms plays a key role in the evolutionary process. While Evolutionary Computation (EC) models have traditionally taken biological inspiration in the design of many key model components (e.g., genetic mutation and crossover, populations under natural selection, etc.), there is a need for more biological input in specifying how a genotype forms a phenotype. There are two powerful theoretical abstractions used in biology for explaining the evolutionary basis of phenotypic development. The first is that there is a sequence of hereditary information (the genotype) passed from one generation to the next. The second is that genes extracted from this sequence interact to form networks of regulation that, when coupled with environmental factors, control the development of an organism (the phenotype). An abstract model of gene regulation exists in the form of the Artificial Genome. This model provides a principled approach to extracting regulatory networks of genes from sequence-level information. L-systems provide a mature framework for modelling developmental phenotypes interacting within environments. This paper takes a step towards integrating these two models, providing a biologically-inspired modelling framework that bridges the chasm between processes occurring in evolutionary timescales, and those occurring within individual lifetimes

Unsupervisedly Prompting AlphaFold2 for Few-Shot Learning of Accurate Folding Landscape and Protein Structure Prediction

Data-driven predictive methods which can efficiently and accurately transform protein sequences into biologically active structures are highly valuable for scientific research and medical development. Determining accurate folding landscape using co-evolutionary information is fundamental to the success of modern protein structure prediction methods. As the state of the art, AlphaFold2 has dramatically raised the accuracy without performing explicit co-evolutionary analysis. Nevertheless, its performance still shows strong dependence on available sequence homologs. Based on the interrogation on the cause of such dependence, we presented EvoGen, a meta generative model, to remedy the underperformance of AlphaFold2 for poor MSA targets. By prompting the model with calibrated or virtually generated homologue sequences, EvoGen helps AlphaFold2 fold accurately in low-data regime and even achieve encouraging performance with single-sequence predictions. Being able to make accurate predictions with few-shot MSA not only generalizes AlphaFold2 better for orphan sequences, but also democratizes its use for high-throughput applications. Besides, EvoGen combined with AlphaFold2 yields a probabilistic structure generation method which could explore alternative conformations of protein sequences, and the task-aware differentiable algorithm for sequence generation will benefit other related tasks including protein design.Comment: version 2.0; 28 pages, 6 figure

Estimates of genetic differentiation measured by FST do not necessarily require large sample sizes when using many SNP markers

Population genetic studies provide insights into the evolutionary processes that influence the distribution of sequence variants within and among wild populations. FST is among the most widely used measures for genetic differentiation and plays a central role in ecological and evolutionary genetic studies. It is commonly thought that large sample sizes are required in order to precisely infer FST and that small sample sizes lead to overestimation of genetic differentiation. Until recently, studies in ecological model organisms incorporated a limited number of genetic markers, but since the emergence of next generation sequencing, the panel size of genetic markers available even in non-reference organisms has rapidly increased. In this study we examine whether a large number of genetic markers can substitute for small sample sizes when estimating FST. We tested the behavior of three different estimators that infer FST and that are commonly used in population genetic studies. By simulating populations, we assessed the effects of sample size and the number of markers on the various estimates of genetic differentiation. Furthermore, we tested the effect of ascertainment bias on these estimates. We show that the population sample size can be significantly reduced (as small as n = 4–6) when using an appropriate estimator and a large number of bi-allelic genetic markers (k.1,000). Therefore, conservation genetic studies can now obtain almost the same statistical power as studies performed on model organisms using markers developed with next-generation sequencing

A simple tectonic model for crustal accretion in the Slave Province: A 2.7-2.5 Ga granite greenstone terrane

A prograding (direction unspecified) trench-arc system is favored as a simple yet comprehensive model for crustal generation in a 250,000 sq km granite-greenstone terrain. The model accounts for the evolutionary sequence of volcanism, sedimentation, deformation, metamorphism and plutonism, observed througout the Slave province. Both unconformable (trench inner slope) and subconformable (trench outer slope) relations between the volcanics and overlying turbidities; and the existence of relatively minor amounts of pre-greenstone basement (microcontinents) and syn-greenstone plutons (accreted arc roots) are explained. Predictions include: a varaiable gap between greenstone volcanism and trench turbidite sedimentation (accompanied by minor volcanism) and systematic regional variations in age span of volcanism and plutonism. Implications of the model will be illustrated with reference to a 1:1 million scale geological map of the Slave Province (and its bounding 1.0 Ga orogens)

High through-put sequencing of the Parhyale hawaiensis mRNAs and microRNAs to aid comparative developmental studies

Understanding the genetic and evolutionary basis of animal morphological diversity will require comparative developmental studies that use new model organisms. This necessitates development of tools for the study of genetics and also the generation of sequence information of the organism to be studied. The development of next generation sequencing technology has enabled quick and cost effective generation of sequence information. Parhyale hawaiensis has emerged as a model organism of choice due to the development of advanced molecular tools, thus P. hawaiensis genetic information will help drive functional studies in this organism. Here we present a transcriptome and miRNA collection generated using next generation sequencing platforms. We generated approximately 1.7 million reads from a P. hawaiensis cDNA library constructed from embryos up to the germ band stage. These reads were assembled into a dataset comprising 163,501 transcripts. Using the combined annotation of Annot8r and pfam2go, Gene Ontology classifications was assigned to 20,597 transcripts. Annot8r was used to provide KEGG orthology to our transcript dataset. A total of 25,292 KEGG pathway assignments were defined and further confirmed with reciprocal blast against the NCBI nr protein database. This has identified many P. hawaiensis gene orthologs of key conserved signalling pathways involved in development. We also generated small RNA sequences from P. hawaiensis, identifying 55 conserved miRNAs. Sequenced small RNAs that were not annotated by stringent comparison to mirBase were used to search the Daphnia pulex for possible novel miRNAs. Using a conservative approach, we have identified 51 possible miRNA candidates conserved in the Daphnia pulex genome, which could be potential crustacean/arthropod specific miRNAs. Our study presents gene and miRNA discovery in a new model organism that does not have a sequenced genome. The data provided by our work will be valuable for the P. hawaiensis community as well as the wider evolutionary developmental biology community

Stochastic dynamics of adaptive trait and neutral marker driven by eco-evolutionary feedbacks

How the neutral diversity is affected by selection and adaptation is investigated in an eco-evolutionary framework. In our model, we study a finite population in continuous time, where each individual is characterized by a trait under selection and a completely linked neutral marker. Population dynamics are driven by births and deaths, mutations at birth, and competition between individuals. Trait values influence ecological processes (demographic events, competition), and competition generates selection on trait variation, thus closing the eco-evolutionary feedback loop. The demographic effects of the trait are also expected to influence the generation and maintenance of neutral variation. We consider a large population limit with rare mutation, under the assumption that the neutral marker mutates faster than the trait under selection. We prove the convergence of the stochastic individual-based process to a new measure-valued diffusive process with jumps that we call Substitution Fleming-Viot Process (SFVP). When restricted to the trait space this process is the Trait Substitution Sequence first introduced by Metz et al. (1996). During the invasion of a favorable mutation, a genetical bottleneck occurs and the marker associated with this favorable mutant is hitchhiked. By rigorously analysing the hitchhiking effect and how the neutral diversity is restored afterwards, we obtain the condition for a time-scale separation; under this condition, we show that the marker distribution is approximated by a Fleming-Viot distribution between two trait substitutions. We discuss the implications of the SFVP for our understanding of the dynamics of neutral variation under eco-evolutionary feedbacks and illustrate the main phenomena with simulations. Our results highlight the joint importance of mutations, ecological parameters, and trait values in the restoration of neutral diversity after a selective sweep.Comment: 29 page