Evolutionary rescue on genotypic fitness landscapes.

Populations facing adverse environments, novel pathogens or invasive competitors may be destined to extinction if they are unable to adapt rapidly. Quantitative predictions of the probability of survival through adaptation, evolutionary rescue, have been previously developed for one of the most natural and well-studied mappings from an organism's traits to its fitness, Fisher's geometric model (FGM). While FGM assumes that all possible trait values are accessible via mutation, in many applications only a finite set of rescue mutations will be available, such as mutations conferring resistance to a parasite, predator or toxin. We predict the probability of evolutionary rescue, via de novo mutation, when this underlying genetic structure is included. We find that rescue probability is always reduced when its genetic basis is taken into account. Unlike other known features of the genotypic FGM, however, the probability of rescue increases monotonically with the number of available mutations and approaches the behaviour of the classical FGM as the number of available mutations approaches infinity.

Reproduction Number and Asymptotic Stability for the Dynamics of a Honey Bee Colony with Continuous Age Structure.

A system of partial differential equations is derived as a model for the dynamics of a honey bee colony with a continuous age distribution, and the system is then extended to include the effects of a simplified infectious disease. In the disease-free case, we analytically derive the equilibrium age distribution within the colony and propose a novel approach for determining the global asymptotic stability of a reduced model. Furthermore, we present a method for determining the basic reproduction number [Formula: see text] of the infection; the method can be applied to other age-structured disease models with interacting susceptible classes. The results of asymptotic stability indicate that a honey bee colony suffering losses will recover naturally so long as the cause of the losses is removed before the colony collapses. Our expression for [Formula: see text] has potential uses in the tracking and control of an infectious disease within a bee colony.

Age structure is critical to the population dynamics and survival of honeybee colonies.

Age structure is an important feature of the division of labour within honeybee colonies, but its effects on colony dynamics have rarely been explored. We present a model of a honeybee colony that incorporates this key feature, and use this model to explore the effects of both winter and disease on the fate of the colony. The model offers a novel explanation for the frequently observed phenomenon of 'spring dwindle', which emerges as a natural consequence of the age-structured dynamics. Furthermore, the results indicate that a model taking age structure into account markedly affects the predicted timing and severity of disease within a bee colony. The timing of the onset of disease with respect to the changing seasons may also have a substantial impact on the fate of a honeybee colony. Finally, simulations predict that an infection may persist in a honeybee colony over several years, with effects that compound over time. Thus, the ultimate collapse of the colony may be the result of events several years past.

Network analysis of human fMRI data suggests modular restructuring after simulated acquired brain injury.

The pathophysiology underlying neurocognitive dysfunction following mild traumatic brain injury (TBI), or concussion, is poorly understood. In order to shed light on the effects of TBI at the functional network or modular level, our research groups are engaged in the acquisition and analysis of functional magnetic resonance imaging data from subjects post-TBI. Complementary to this effort, in this paper we use mathematical and computational techniques to determine how modular structure changes in response to specific mechanisms of injury. In particular, we examine in detail the potential effects of focal contusions, diffuse axonal degeneration and diffuse microlesions, illustrating the extent to which functional modules are preserved or degenerated by each type of injury. One striking prediction of our study is that the left and right hemispheres show a tendency to become functionally separated post-injury, but only in response to diffuse microlesions. We highlight other key differences among the effects of the three modelled injuries and discuss their clinical implications. These results may help delineate the functional mechanisms underlying several of the cognitive sequelae associated with TBI.

Self-tolerance and autoimmunity in a regulatory T cell model.

The class of immunosuppressive lymphocytes known as regulatory T cells (Tregs) has been identified as a key component in preventing autoimmune diseases. Although Tregs have been incorporated previously in mathematical models of autoimmunity, we take a novel approach which emphasizes the importance of professional antigen presenting cells (pAPCs). We examine three possible mechanisms of Treg action (each in isolation) through ordinary differential equation (ODE) models. The immune response against a particular autoantigen is suppressed both by Tregs specific for that antigen and by Tregs of arbitrary specificities, through their action on either maturing or already mature pAPCs or on autoreactive effector T cells. In this deterministic approach, we find that qualitative long-term behaviour is predicted by the basic reproductive ratio R(0) for each system. When R(0)<1, only the trivial equilibrium exists and is stable; when R(0)>1, this equilibrium loses its stability and a stable non-trivial equilibrium appears. We interpret the absence of self-damaging populations at the trivial equilibrium to imply a state of self-tolerance, and their presence at the non-trivial equilibrium to imply a state of chronic autoimmunity. Irrespective of mechanism, our model predicts that Tregs specific for the autoantigen in question play no role in the system's qualitative long-term behaviour, but have quantitative effects that could potentially reduce an autoimmune response to sub-clinical levels. Our results also suggest an important role for Tregs of arbitrary specificities in modulating the qualitative outcome. A stochastic treatment of the same model demonstrates that the probability of developing a chronic autoimmune response increases with the initial exposure to self antigen or autoreactive effector T cells. The three different mechanisms we consider, while leading to a number of similar predictions, also exhibit key differences in both transient dynamics (ODE approach) and the probability of chronic autoimmunity (stochastic approach).

The impact of host-cell dynamics on the fixation probability for lytic viruses.

Lytic viruses are obligate parasites whose population dynamics are necessarily coupled to the dynamics of their host-cell population. The adaptation rate of these viruses has attracted considerable scientific interest, as they are a key model organism in experimental evolution. Nevertheless, to date mathematical models of experimental evolution have largely ignored the host-cell population. In this paper we incorporate two important features of host-cell dynamics-the possibility of clearance or death of an infected cell before lysis, and the possibility of changing host-cell density-into previous models for the fixation probability of lytic viruses. We compute the fixation probabilities of rare alleles that confer reproductive benefit through either an increase in attachment rate or burst size, or a reduction in lysis time. We find that host-cell clearance significantly reduces the fixation probabilities of all types of beneficial mutations, having the largest impact on mutations which reduce the lysis time, but has only modest effects on the pattern of fixation probabilities previously observed. We further predict that exponential growth of the host-cell population preferentially selects for mutations that affect burst size or lysis time, and exacerbates the sensitive dependence of fixation probabilities on the time between population bottlenecks. Even when burst size and lysis time are constrained to vary together, our results suggest that lytic viruses should readily adapt to optimize these traits to the timing between population bottlenecks.

The effects of population bottlenecks on clonal interference, and the adaptation effective population size.

Clonal interference refers to the competition that arises in asexual populations when multiple beneficial mutations segregate simultaneously. A large body of theoretical and experimental work now addresses this issue. Although much of the experimental work is performed in populations that grow exponentially between periodic population bottlenecks, the theoretical work to date has addressed only populations of a constant size. We derive an analytical approximation for the rate of adaptation in the presence of both clonal interference and bottlenecks, and compare this prediction to the results of an individual-based simulation, showing excellent agreement in the parameter regime in which clonal interference prevails. We also derive an appropriate definition for the effective population size for adaptive evolution experiments in the presence of population bottlenecks. This "adaptation effective population size" allows for a good approximation of the expected rate of adaptation, either in the strong-selection weak-mutation regime, or when clonal interference comes into play. In the multiple mutation regime, when the product of the population size and mutation rate is extremely large, these results no longer hold.

Fixation probability for lytic viruses: the attachment-lysis model.

The fixation probability of a beneficial mutation is extremely sensitive to assumptions regarding the organism's life history. In this article we compute the fixation probability using a life-history model for lytic viruses, a key model organism in experimental studies of adaptation. The model assumes that attachment times are exponentially distributed, but that the lysis time, the time between attachment and host cell lysis, is constant. We assume that the growth of the wild-type viral population is controlled by periodic sampling (population bottlenecks) and also include the possibility that clearance may occur at a constant rate, for example, through washout in a chemostat. We then compute the fixation probability for mutations that increase the attachment rate, decrease the lysis time, increase the burst size, or reduce the probability of clearance. The fixation probability of these four types of beneficial mutations can be vastly different and depends critically on the time between population bottlenecks. We also explore mutations that affect lysis time, assuming that the burst size is constrained by the lysis time, for experimental protocols that sample either free phage or free phage and artificially lysed infected cells. In all cases we predict that the fixation probability of beneficial alleles is remarkably sensitive to the time between population bottlenecks.

The fixation probability of beneficial mutations.

The fixation probability, the probability that the frequency of a particular allele in a population will ultimately reach unity, is one of the cornerstones of population genetics. In this review, we give a brief historical overview of mathematical approaches used to estimate the fixation probability of beneficial alleles. We then focus on more recent work that has relaxed some of the key assumptions in these early papers, providing estimates that have wider applicability to both natural and laboratory settings. In the final section, we address the possibility of future work that might bridge the gap between theoretical results to date and results that might realistically be applied to the experimental evolution of microbial populations. Our aim is to highlight the concrete, testable predictions that have arisen from the theoretical literature, with the intention of further motivating the invaluable interplay between theory and experiment.

Estimating the optimal bottleneck ratio for experimental evolution: the burst-death model.

Population bottlenecks are ubiquitous in nature, and are an inherent feature of the experimental protocol for many laboratory selection experiments. These bottlenecks can have profound effects on the rate and trajectory of evolution. In particular, there is a trade-off between sampling the population too frequently and imposing infrequent, but more severe, bottlenecks. In this paper we consider the effects of population bottlenecks, assuming a burst-death model for the life history of the organism under study. This model assumes that generation times are exponentially distributed and that at each generation, individuals in the population have a fixed number of offspring. The model also allows for a constant death rate between bottlenecks. We use this model to estimate the optimal bottleneck ratio, that is, the fraction of the population that should be sampled at each bottleneck in order to maximize the probability that beneficial mutations occur and are not lost. We find that the optimal ratio is roughly constant with respect to many of the model parameters, and that sampling about 20% of the population will maximize the rate of adaptation.

Fixation probabilities depend on life history: fecundity, generation time and survival in a burst-death model.

The burst-death model has been developed to describe the life history of organisms with variable generation times and a burst of a fixed number of offspring. The model also includes an optional constant clearance rate, such as washout from a chemostat, and the possibility of sustained periods of population growth followed by severe bottlenecks, as in serial passaging. In this model, a beneficial mutation can either increase the burst rate or the burst size, or reduce the clearance rate, thus increasing survival. In this article we examine the effects of these three possible mechanisms on both the Malthusian fitness and the fixation probability of the lineage. We find that equivalent relative increases in the burst rate or burst size confer equivalent increases in the Malthusian fitness of a lineage, whereas increasing survival typically has a more moderate effect on Malthusian fitness. In contrast, for beneficial mutations that confer the same increase in fitness, mutations that increase survival are the most likely to fix, followed by mutations that increase the burst rate. Mutations that increase the burst size are the least likely to fix. These results imply that mutant lineages with the highest Malthusian fitness are not, in many cases, the most likely to escape extinction.

Mutual information without the influence of phylogeny or entropy dramatically improves residue contact prediction.

MOTIVATION: Compensating alterations during the evolution of protein families give rise to coevolving positions that contain important structural and functional information. However, a high background composed of random noise and phylogenetic components interferes with the identification of coevolving positions. RESULTS: We have developed a rapid, simple and general method based on information theory that accurately estimates the level of background mutual information for each pair of positions in a given protein family. Removal of this background results in a metric, MIp, that correctly identifies substantially more coevolving positions in protein families than any existing method. A significant fraction of these positions coevolve strongly with one or only a few positions. The vast majority of such position pairs are in contact in representative structures. The identification of strongly coevolving position pairs can be used to impose significant structural limitations and should be an important additional constraint for ab initio protein folding. AVAILABILITY: Alignments and program files can be found in the Supplementary Information.

Drug-sparing regimens for HIV combination therapy: benefits predicted for "drug coasting".

Structured Treatment Interruptions (STI) during HIV drug therapy were thought to potentially reduce side effects and toxicity, boost immune involvement, and possibly lower the viral set-point. Clinical trials of STI regimens, however, have had mixed results. We use an established mathematical model of HAART to estimate possible therapeutic outcomes for STI and for other, similar patterns in HIV combination therapy. We perform an exhaustive search of patterns of up to 60 days, for triple-drug combinations involving accurate pharmacokinetics for 12 specific antiviral drugs. The results of this analysis are consistent with recent clinical trials which have demonstrated that STI-type patterns, involving therapy interruption of weeks or months, are rarely optimal. Our analysis predicts, however, that the benefit of treatment can often be improved by including very short drug-free periods, during which the patient effectively "coasts" for a day or two on adequate drug concentrations due to the long half-life of some pharmaceuticals. Our analysis predicts many cases in which this may be achieved without increasing the risk of drug-resistance. This suggests that "drug coasting" patterns, significantly shorter than STI patterns, may merit further clinical investigation in efforts to find drug-sparing regimens for HIV.

Fixation probabilities when generation times are variable: the burst death model.

Estimating the fixation probability of a beneficial mutation has a rich history in theoretical population genetics. Typically, to attain mathematical tractability, we assume that generation times are fixed, while the number of offspring per individual is stochastic. However, fixation probabilities are extremely sensitive to these assumptions regarding life history. In this article, we compute the fixation probability for a "burst-death" life-history model. The model assumes that generation times are exponentially distributed, but the number of offspring per individual is constant. We estimate the fixation probability for populations of constant size and for populations that grow exponentially between periodic population bottlenecks. We find that the fixation probability is, in general, substantially lower in the burst-death model than in classical models. We also note striking qualitative differences between the fates of beneficial mutations that increase burst size and mutations that increase the burst rate. In particular, once the burst size is sufficiently large relative to the wild type, the burst-death model predicts that fixation probability depends only on burst rate.

Optimal drug treatment regimens for HIV depend on adherence.

Drug therapies aimed at suppressing the human immunodeficiency virus (HIV) are highly effective, often reducing the viral load to below the limits of detection for years. Adherence to such antiviral regimens, however, is typically far from ideal. We have previously developed a model that predicts optimal treatment regimens by weighing drug toxicity against CD4+ T-cell counts, including the probability that drug resistance will emerge. We use this model to investigate the influence of adherence on therapy benefit. For a drug with a given half-life, we compare the effects of varying the dose amount and dose interval for different rates of adherence, and compute the optimal dose regimen for adherence between 65% and 95%. Our results suggest that for optimal treatment benefit, drug regimens should be adjusted for poor adherence, usually by increasing the dose amount and leaving the dose interval fixed. We also find that the benefit of therapy can be surprisingly robust to poor adherence, as long as the dose interval and dose amount are chosen accordingly.

Costs versus benefits: best possible and best practical treatment regimens for HIV.

Current HIV therapy, although highly effective, may cause very serious side effects, making adherence to the prescribed regimen difficult. Mathematical modeling may be used to evaluate alternative treatment regimens by weighing the positive results of treatment, such as higher levels of helper T cells, against the negative consequences, such as side effects and the possibility of resistance mutations. Although estimating the weights assigned to these factors is difficult, current clinical practice offers insight by defining situations in which therapy is considered "worthwhile". We therefore use clinical practice, along with the probability that a drug-resistant mutation is present at the start of therapy, to suggest methods of rationally estimating these weights. In our underlying model, we use ordinary differential equations to describe the time course of in-host HIV infection, and include populations of both activated CD4(+) T cells and CD8(+) T cells. We then determine the best possible treatment regimen, assuming that the effectiveness of the drug can be continually adjusted, and the best practical treatment regimen, evaluating all patterns of a block of days "on" therapy followed by a block of days "off" therapy. We find that when the tolerance for drug-resistant mutations is low, high drug concentrations which maintain low infected cell populations are optimal. In contrast, if the tolerance for drug-resistant mutations is fairly high, the optimal treatment involves periods of reduced drug exposure which consequently boost the immune response through increased antigen exposure. We elucidate the dependence of the optimal treatment regimen on the pharmacokinetic parameters of specific antiviral agents.

Functionally important residues in the peptidyl-prolyl isomerase Pin1 revealed by unigenic evolution.

Pin1 is a phosphorylation-dependent member of the parvulin family of peptidyl-prolyl isomerases exhibiting functional conservation between yeast and man. To perform an unbiased analysis of the regions of Pin1 essential for its functions, we generated libraries of randomly mutated forms of the human Pin1 cDNA and identified functional Pin1 alleles by their ability to complement the Pin1 homolog Ess1 in Saccharomyces cerevisiae. We isolated an extensive collection of functional mutant Pin1 clones harboring a total of 356 amino acid substitutions. Surprisingly, many residues previously thought to be critical in Pin1 were found to be altered in this collection of functional mutants. In fact, only 17 residues were completely conserved in these mutants and in Pin1 sequences from other eukaryotic organisms, with only two of these conserved residues located within the WW domain of Pin1. Examination of invariant residues provided new insights regarding a phosphate-binding loop that distinguishes a phosphorylation-dependent peptidyl-prolyl isomerase such as Pin1 from other parvulins. In addition, these studies led to an investigation of residues involved in catalysis including C113 that was previously implicated as the catalytic nucleophile. We demonstrate that substitution of C113 with D does not compromise Pin1 function in vivo nor does this substitution abolish catalytic activity in purified recombinant Pin1. These findings are consistent with the prospect that the function of residue 113 may not be that of a nucleophile, thus raising questions about the model of nucleophilic catalysis. Accordingly, an alternative catalytic mechanism for Pin1 is postulated.

Dynamics of recurrent viral infection.

In chronic viral infection, low levels of viral replication and infectious particle production are maintained over long periods, punctuated by brief bursts of high viral production and release. We apply well-established principles of modelling virus dynamics to the study of chronic viral infection, demonstrating that a model which incorporates the distinct contributions of cytotoxic T lymphocytes (CTLs) and antibodies exhibits long periods of quiescence followed by brief bursts of viral production. This suggests that for recurrent viral infections, no special mechanism or exogenous trigger is necessary to provoke an episode of reactivation; rather, the system may naturally cycle through recurrent episodes at intervals which can be many years long. We also find that exogenous factors which cause small fluctuations in the natural course of the infection can trigger a recurrent episode. Our model predicts that longer periods between recurrences are associated with more severe viral episodes. Four factors move the system towards less frequent, more severe episodes: decreased viral infectivity, decreased CTL efficacy, decreased memory T cell response and increased antibody efficacy.

Improving estimates of the basic reproductive ratio: using both the mean and the dispersal of transition times.

In both within-host and epidemiological models of pathogen dynamics, the basic reproductive ratio, R(0), is a powerful tool for gauging the risk associated with an emerging pathogen, or for estimating the magnitude of required control measures. Techniques for estimating R(0), either from incidence data or in-host clinical measures, often rely on estimates of mean transition times, that is, the mean time before recovery, death or quarantine occurs. In many cases, however, either data or intuition may provide additional information about the dispersal of these transition times about the mean, even if the precise form of the underlying probability distribution remains unknown. For example, we may know that recovery typically occurs within a few days of the mean recovery time. In this paper we elucidate common situations in which R(0) is sensitive to the dispersal of transition times about their respective means. We then provide simple correction factors that may be applied to improve estimates of R(0) when not only the mean but also the standard deviation of transition times out of the infectious state can be estimated.