TY - JOUR AB - Our focus here is on the infinitesimal model. In this model, one or several quantitative traits are described as the sum of a genetic and a non-genetic component, the first being distributed within families as a normal random variable centred at the average of the parental genetic components, and with a variance independent of the parental traits. Thus, the variance that segregates within families is not perturbed by selection, and can be predicted from the variance components. This does not necessarily imply that the trait distribution across the whole population should be Gaussian, and indeed selection or population structure may have a substantial effect on the overall trait distribution. One of our main aims is to identify some general conditions on the allelic effects for the infinitesimal model to be accurate. We first review the long history of the infinitesimal model in quantitative genetics. Then we formulate the model at the phenotypic level in terms of individual trait values and relationships between individuals, but including different evolutionary processes: genetic drift, recombination, selection, mutation, population structure, …. We give a range of examples of its application to evolutionary questions related to stabilising selection, assortative mating, effective population size and response to selection, habitat preference and speciation. We provide a mathematical justification of the model as the limit as the number M of underlying loci tends to infinity of a model with Mendelian inheritance, mutation and environmental noise, when the genetic component of the trait is purely additive. We also show how the model generalises to include epistatic effects. We prove in particular that, within each family, the genetic components of the individual trait values in the current generation are indeed normally distributed with a variance independent of ancestral traits, up to an error of order 1∕M. Simulations suggest that in some cases the convergence may be as fast as 1∕M. AU - Barton, Nicholas H AU - Etheridge, Alison AU - Véber, Amandine ID - 626 JF - Theoretical Population Biology SN - 00405809 TI - The infinitesimal model: Definition derivation and implications VL - 118 ER - TY - GEN AB - This text provides additional information about the model, a derivation of the analytic results in Eq (4), and details about simulations of an additional parameter set. AU - Lukacisinova, Marta AU - Novak, Sebastian AU - Paixao, Tiago ID - 9849 TI - Modelling and simulation details ER - TY - GEN AB - In this text, we discuss how a cost of resistance and the possibility of lethal mutations impact our model. AU - Lukacisinova, Marta AU - Novak, Sebastian AU - Paixao, Tiago ID - 9850 TI - Extensions of the model ER - TY - GEN AB - Based on the intuitive derivation of the dynamics of SIM allele frequency pM in the main text, we present a heuristic prediction for the long-term SIM allele frequencies with χ > 1 stresses and compare it to numerical simulations. AU - Lukacisinova, Marta AU - Novak, Sebastian AU - Paixao, Tiago ID - 9851 TI - Heuristic prediction for multiple stresses ER - TY - GEN AB - We show how different combination strategies affect the fraction of individuals that are multi-resistant. AU - Lukacisinova, Marta AU - Novak, Sebastian AU - Paixao, Tiago ID - 9852 TI - Resistance frequencies for different combination strategies ER - TY - THES AB - Bacteria and their pathogens – phages – are the most abundant living entities on Earth. Throughout their coevolution, bacteria have evolved multiple immune systems to overcome the ubiquitous threat from the phages. Although the molecu- lar details of these immune systems’ functions are relatively well understood, their epidemiological consequences for the phage-bacterial communities have been largely neglected. In this thesis we employed both experimental and theoretical methods to explore whether herd and social immunity may arise in bacterial popu- lations. Using our experimental system consisting of Escherichia coli strains with a CRISPR based immunity to the T7 phage we show that herd immunity arises in phage-bacterial communities and that it is accentuated when the populations are spatially structured. By fitting a mathematical model, we inferred expressions for the herd immunity threshold and the velocity of spread of a phage epidemic in partially resistant bacterial populations, which both depend on the bacterial growth rate, phage burst size and phage latent period. We also investigated the poten- tial for social immunity in Streptococcus thermophilus and its phage 2972 using a bioinformatic analysis of potentially coding short open reading frames with a signalling signature, encoded within the CRISPR associated genes. Subsequently, we tested one identified potentially signalling peptide and found that its addition to a phage-challenged culture increases probability of survival of bacteria two fold, although the results were only marginally significant. Together, these results demonstrate that the ubiquitous arms races between bacteria and phages have further consequences at the level of the population. AU - Payne, Pavel ID - 6291 SN - 2663-337X TI - Bacterial herd and social immunity to phages ER - TY - GEN AB - Mathematica notebooks used to generate figures. AU - Etheridge, Alison AU - Barton, Nicholas H ID - 9842 TI - Data for: Establishment in a new habitat by polygenic adaptation ER - TY - JOUR AB - The behaviour of gene regulatory networks (GRNs) is typically analysed using simulation-based statistical testing-like methods. In this paper, we demonstrate that we can replace this approach by a formal verification-like method that gives higher assurance and scalability. We focus on Wagner’s weighted GRN model with varying weights, which is used in evolutionary biology. In the model, weight parameters represent the gene interaction strength that may change due to genetic mutations. For a property of interest, we synthesise the constraints over the parameter space that represent the set of GRNs satisfying the property. We experimentally show that our parameter synthesis procedure computes the mutational robustness of GRNs—an important problem of interest in evolutionary biology—more efficiently than the classical simulation method. We specify the property in linear temporal logic. We employ symbolic bounded model checking and SMT solving to compute the space of GRNs that satisfy the property, which amounts to synthesizing a set of linear constraints on the weights. AU - Giacobbe, Mirco AU - Guet, Calin C AU - Gupta, Ashutosh AU - Henzinger, Thomas A AU - Paixao, Tiago AU - Petrov, Tatjana ID - 1351 IS - 8 JF - Acta Informatica SN - 00015903 TI - Model checking the evolution of gene regulatory networks VL - 54 ER - TY - JOUR AB - Evolutionary algorithms (EAs) form a popular optimisation paradigm inspired by natural evolution. In recent years the field of evolutionary computation has developed a rigorous analytical theory to analyse the runtimes of EAs on many illustrative problems. Here we apply this theory to a simple model of natural evolution. In the Strong Selection Weak Mutation (SSWM) evolutionary regime the time between occurrences of new mutations is much longer than the time it takes for a mutated genotype to take over the population. In this situation, the population only contains copies of one genotype and evolution can be modelled as a stochastic process evolving one genotype by means of mutation and selection between the resident and the mutated genotype. The probability of accepting the mutated genotype then depends on the change in fitness. We study this process, SSWM, from an algorithmic perspective, quantifying its expected optimisation time for various parameters and investigating differences to a similar evolutionary algorithm, the well-known (1+1) EA. We show that SSWM can have a moderate advantage over the (1+1) EA at crossing fitness valleys and study an example where SSWM outperforms the (1+1) EA by taking advantage of information on the fitness gradient. AU - Paixao, Tiago AU - Pérez Heredia, Jorge AU - Sudholt, Dirk AU - Trubenova, Barbora ID - 1336 IS - 2 JF - Algorithmica SN - 01784617 TI - Towards a runtime comparison of natural and artificial evolution VL - 78 ER - TY - JOUR AB - Much of quantitative genetics is based on the ‘infinitesimal model’, under which selection has a negligible effect on the genetic variance. This is typically justified by assuming a very large number of loci with additive effects. However, it applies even when genes interact, provided that the number of loci is large enough that selection on each of them is weak relative to random drift. In the long term, directional selection will change allele frequencies, but even then, the effects of epistasis on the ultimate change in trait mean due to selection may be modest. Stabilising selection can maintain many traits close to their optima, even when the underlying alleles are weakly selected. However, the number of traits that can be optimised is apparently limited to ~4Ne by the ‘drift load’, and this is hard to reconcile with the apparent complexity of many organisms. Just as for the mutation load, this limit can be evaded by a particular form of negative epistasis. A more robust limit is set by the variance in reproductive success. This suggests that selection accumulates information most efficiently in the infinitesimal regime, when selection on individual alleles is weak, and comparable with random drift. A review of evidence on selection strength suggests that although most variance in fitness may be because of alleles with large Nes, substantial amounts of adaptation may be because of alleles in the infinitesimal regime, in which epistasis has modest effects. AU - Barton, Nicholas H ID - 1199 JF - Heredity TI - How does epistasis influence the response to selection? VL - 118 ER -