Development of antibiotic regimens using graph based evolutionary algorithms

This paper examines the use of evolutionary algorithms in the development of antibiotic regimens given to production animals. A model is constructed that combines the lifespan of the animal and the bacteria living in the animal's gastro-intestinal tract from the early finishing stage until the animal reaches market weight. This model is used as the fitness evaluation for a set of graph based evolutionary algorithms to assess the impact of diversity control on the evolving antibiotic regimens. The graph based evolutionary algorithms have two objectives: to find an antibiotic treatment regimen that maintains the weight gain and health benefits of antibiotic use and to reduce the risk of spreading antibiotic resistant bacteria. This study examines different regimens of tylosin phosphate use on bacteria populations divided into Gram positive and Gram negative types, with a focus on Campylobacter spp. Treatment regimens were found that provided decreased antibiotic resistance relative to conventional methods while providing nearly the same benefits as conventional antibiotic regimes. By using a graph to control the information flow in the evolutionary algorithm, a variety of solutions along the Pareto front can be found automatically for this and other multi-objective problems.     

Evolution of evolvability via adaptation of mutation rates

We examine a simple form of the evolution of evolvability-the evolution of mutation rates-in a simple model system. The system is composed of many agents moving, reproducing, and dying in a two-dimensional resource-limited world. We first examine various macroscopic quantities (three types of genetic diversity, a measure of population fitness, and a measure of evolutionary activity) as a function of fixed mutation rates. The results suggest that (i) mutation rate is a control parameter that governs a transition between two qualitatively different phases of evolution, an ordered phase characterized by punctuated equilibria of diversity, and a disordered phase of characterized by noisy fluctuations around an equilibrium diversity, and (ii) the ability of evolution to create adaptive structure is maximized when the mutation rate is just below the transition between these two phases of evolution. We hypothesize that this transition occurs when the demands for evolutionary memory and evolutionary novelty are typically balanced. We next allow the mutation rate itself to evolve, and we observe that evolving mutation rates adapt to values at this transition. Furthermore, the mutation rates adapt up (or down) as the evolutionary demands for novelty (or memory) increase, thus supporting the balance hypothesis.     

Competition as a source of constraint on life history evolution in natural populations

Competition among individuals is central to our understanding of ecology and population dynamics. However, it could also have major implications for the evolution of resource-dependent life history traits (for example, growth, fecundity) that are important determinants of fitness in natural populations. This is because when competition occurs, the phenotype of each individual will be causally influenced by the phenotypes, and so the genotypes, of competitors. Theory tells us that indirect genetic effects arising from competitive interactions will give rise to the phenomenon of ‘evolutionary environmental deterioration'', and act as a source of evolutionary constraint on resource-dependent traits under natural selection. However, just how important this constraint is remains an unanswered question. This article seeks to stimulate empirical research in this area, first highlighting some patterns emerging from life history studies that are consistent with a competition-based model of evolutionary constraint, before describing several quantitative modelling strategies that could be usefully applied. A recurrent theme is that rigorous quantification of a competition''s impact on life history evolution will require an understanding of the causal pathways and behavioural processes by which genetic (co)variance structures arise. Knowledge of the G-matrix among life history traits is not, in and of itself, sufficient to identify the constraints caused by competition.     

Environmental uncertainty, autocorrelation and the evolution of survival

Survival is a key fitness component and the evolution of age- and stage-specific patterns in survival is a central question in evolutionary biology. In variable environments, favouring chances of survival at the expense of other fitness components could increase fitness by spreading risk across uncertain conditions, especially if environmental conditions improve in the future. Both the magnitude of environmental variation and temporal autocorrelation in the environment might therefore affect the evolution of survival patterns. Despite this, the influence of temporal autocorrelation on the evolution of survival patterns has not been addressed. Here, we use a trade-off structure which reflects the empirically inspired paradigm of acquisition and allocation of resources to investigate how the evolutionarily stable survival probability is shaped in variable, density-dependent environments. We show that temporal autocorrelation is likely to be an important aspect of environmental variability that contributes to shaping age- and stage-specific patterns of survival probabilities in nature.     

Stochasticity Versus Determinism: Consequences for Realistic Gene Regulatory Network Modelling and Evolution

Gene regulation is one important mechanism in producing observed phenotypes and heterogeneity. Consequently, the study of gene regulatory network (GRN) architecture, function and evolution now forms a major part of modern biology. However, it is impossible to experimentally observe the evolution of GRNs on the timescales on which living species evolve. In silico evolution provides an approach to studying the long-term evolution of GRNs, but many models have either considered network architecture from non-adaptive evolution, or evolution to non-biological objectives. Here, we address a number of important modelling and biological questions about the evolution of GRNs to the realistic goal of biomass production. Can different commonly used simulation paradigms, in particular deterministic and stochastic Boolean networks, with and without basal gene expression, be used to compare adaptive with non-adaptive evolution of GRNs? Are these paradigms together with this goal sufficient to generate a range of solutions? Will the interaction between a biological goal and evolutionary dynamics produce trade-offs between growth and mutational robustness? We show that stochastic basal gene expression forces shrinkage of genomes due to energetic constraints and is a prerequisite for some solutions. In systems that are able to evolve rates of basal expression, two optima, one with and one without basal expression, are observed. Simulation paradigms without basal expression generate bloated networks with non-functional elements. Further, a range of functional solutions was observed under identical conditions only in stochastic networks. Moreover, there are trade-offs between efficiency and yield, indicating an inherent intertwining of fitness and evolutionary dynamics.     

The power of fitness in mammals: perceptions from the African slipstream

Evolutionary physiology is the emerging physiological discipline. Unlike environmental physiology or ecophysiology, whose definitions have long been made quite clear, evolutionary physiology has a broader scope of objectives, and its definition lacks a concise treatise. This paper presents the argument that the lack of a common definition of evolutionary physiology is retarding the unification of the mechanistic and amechanistic physiological sciences, a multidisciplinary obligation crucial for a holistic understanding of a physiological basis of fitness. The divide between mechanistic "how" questions, devoted primarily to homeostasis, and evolutionary "why" questions, concerned with understanding phenotypic and genotypic physiological variation, remains broad and is currently not conducive to synergy in the physiological disciplines. Unification may be facilitated, however, by embracing a common currency of measurement and analysis. A likely candidate is the cascade of energy from the environment to offspring and the evolution of physiological form and function, including homeostasis, associated with power management. This currency approach seeks to identify an energetic basis of fitness, namely, whether or how the evolution of life-history traits is influenced by energetic constraints and/or trade-offs.     

Neutrality and Robustness in Evo-Devo: Emergence of Lateral Inhibition

Embryonic development is defined by the hierarchical dynamical process that translates genetic information (genotype) into a spatial gene expression pattern (phenotype) providing the positional information for the correct unfolding of the organism. The nature and evolutionary implications of genotype–phenotype mapping still remain key topics in evolutionary developmental biology (evo-devo). We have explored here issues of neutrality, robustness, and diversity in evo-devo by means of a simple model of gene regulatory networks. The small size of the system allowed an exhaustive analysis of the entire fitness landscape and the extent of its neutrality. This analysis shows that evolution leads to a class of robust genetic networks with an expression pattern characteristic of lateral inhibition. This class is a repertoire of distinct implementations of this key developmental process, the diversity of which provides valuable clues about its underlying causal principles.     

Guidelines: From artificial evolution to computational evolution: a research agenda

Computational scientists have developed algorithms inspired by natural evolution for at least 50 years. These algorithms solve optimization and design problems by building solutions that are 'more fit' relative to desired properties. However, the basic assumptions of this approach are outdated. We propose a research programme to develop a new field: computational evolution. This approach will produce algorithms that are based on current understanding of molecular and evolutionary biology and could solve previously unimaginable or intractable computational and biological problems.     

The fitness cost of generalization: present limitations and future possible solutions

One of the longest and liveliest debates in the evolutionary and ecological literature has centred on the existence and magnitude of constraints that can also be described by a proverb 'jack-of-all-trades is a master of none'. Often assumed, rarely tested, this proverb/assumption states that evolution of generalization necessarily entails a cost. The cost is expressed in terms of fitness loss elsewhere along an environmental gradient that leads to a genetic fitness trade-off between a generalist and a specialist. Although there is a well-developed body of knowledge that documents the cost of adaptation in general, the genetic fitness cost of generalization remains unclear. An empirical test of such cost is not a trivial task because it requires knowledge of a genotype's fundamental ecological niche breadth to document the process of generalization. The estimation of genetic fitness correlation between environments, a commonly used method in the literature, has a limited explanatory power regarding the cost of generalization, and new approaches are needed to further clarify the existence as well as the nature/pattern of constraints in evolution of generalization and specialization. A new approach is proposed to examine experimentally the genetic fitness cost of generalization, which is based on statistical analysis of tolerance curve properties. The approach can be used to study natural populations of both unicellular and multicellular organisms.  © 2007 The Linnean Society of London, Biological Journal of the Linnean Society , 2007, 90 , 583–590.     

Local evolvability of statistically neutral GasNet robot controllers

In this paper we introduce and apply the concept of local evolvability to investigate the behaviour of populations during evolutionary search. We focus on the evolution of GasNet neural network controllers for a robotic visual discrimination problem, showing that the evolutionary process undergoes long neutral fitness epochs. We show that the local evolvability properties of the search space surrounding a group of statistically neutral solutions do vary across the course of an evolutionary run, especially during periods of population takeover. However, once takeover is complete there is no evidence for further increase in local evolvability across fitness epochs. We also see no evidence for the neutral evolution of increased solution robustness, but show that this may be due to the ability of evolutionary algorithms to focus search on volumes of the fitness landscape with above average robustness.     

The devil in the details of life-history evolution: Instability and reversal of genetic correlations during selection on<Emphasis Type="Italic">Drosophila</Emphasis> development

The evolutionary relationships between three major components of Darwinian fitness, development rate, growth rate and preadult survival, were estimated using a comparison of 55 distinct populations ofDrosophila melanogaster variously selected for age-specific fertility, environmental-stress tolerance and accelerated development. Development rate displayed a strong net negative evolutionary correlation with weight at eclosion across all selection treatments, consistent with the existence of a size-versus-time tradeoff between these characters. However, within the data set, the magnitude of the evolutionary correlation depended upon the particular selection treatments contrasted. A previously proposed tradeoff between preadult viability and growth rate was apparent only under weak selection for juvenile fitness components. Direct selection for rapid development led to sharp reductions in both growth rates and viability. These data add to the mounting results from experimental evolution that illustrate the sensitivity of evolutionary correlations to (i) genotype-by-environment (G X E) interaction, (ii) complex functional-trait interactions, and (iii) character definition. Instability, disappearance and reversal of patterns of genetic covariation often occur over short evolutionary time frames and as the direct product of selection, rather than some stochastic process. We suggest that the functional architecture of fitness is a rapidly evolving matrix with reticulate properties, a matrix that we understand only poorly.     

Biomolecular information gained through in vitro evolution

An in vitro evolution is a simplified Darwinian evolution in well-controlled surroundings. This evolution process can be modeled as a hill-climbing or adaptive walk on a fitness landscape in sequence space. The evolving molecular system gains at least two kinds of information originating from the converged sequences and the fitness increment of the evolving biopolymer as the adaptive walker. These two represent two aspects of the biomolecular information, its extent and its content, respectively. Here, we review studies related to formulation of the “content” and “extent” of biomolecular information. The two aspects are interconnected through physicochemical properties of the biopolymer, contrary to the case of conventional information, which seems to be independent of matter. The interconnection was analyzed based on the analogy between the evolution process and thermodynamics. The linear combination of the two by a temperature-like fluctuation factor resulted in a free-energy-like monotonically increasing function during the evolution process.     

Expression Differentiation Is Constrained to Low-Expression Proteins over Ecological Timescales

Protein expression level is one of the strongest predictors of protein sequence evolutionary rate, with high-expression protein sequences evolving at slower rates than low-expression protein sequences largely because of constraints on protein folding and function. Expression evolutionary rates also have been shown to be negatively correlated with expression level across human and mouse orthologs over relatively long divergence times (i.e., ∼100 million years). Long-term evolutionary patterns, however, often cannot be extrapolated to microevolutionary processes (and vice versa), and whether this relationship holds for traits evolving under directional selection within a single species over ecological timescales (i.e., <5000 years) is unknown and not necessarily expected. Expression is a metabolically costly process, and the expression level of a particular protein is predicted to be a tradeoff between the benefit of its function and the costs of its expression. Selection should drive the expression level of all proteins close to values that maximize fitness, particularly for high-expression proteins because of the increased energetic cost of production. Therefore, stabilizing selection may reduce the amount of standing expression variation for high-expression proteins, and in combination with physiological constraints that may place an upper bound on the range of beneficial expression variation, these constraints could severely limit the availability of beneficial expression variants. To determine whether rapid-expression evolution was restricted to low-expression proteins owing to these constraints on highly expressed proteins over ecological timescales, we compared venom protein expression levels across mainland and island populations for three species of pit vipers. We detected significant differentiation in protein expression levels in two of the three species and found that rapid-expression differentiation was restricted to low-expression proteins. Our results suggest that various constraints on high-expression proteins reduce the availability of beneficial expression variants relative to low-expression proteins, enabling low-expression proteins to evolve and potentially lead to more rapid adaptation.     

The evolution of senescence in multi-component systems

Actuarial senescence is characterized by an increase in mortality rate with increasing chronological age. The reliability theory of senescence proposes that organisms’ vital functions can be modelled as a suite of damageable, irreplaceable elements (typically genes or their products) that protect their bearer from condition-dependent death so long as at least one of the elements remains intact. Current incarnations of the reliability theory of senescence are continuous-time models with no explicit evolutionary component. Here, we use elementary probability theory and evolutionary dynamics analysis to derive a discrete-time version of the reliability theory of senescence. We include three variations on this theme: the ‘Series’ model in which damage to any of n elements results in death, the ‘Parallel’ model, in which damage accumulates in random order and damage to all n elements results in death, and the ‘Cascade’ (multi-stage) model, which is like the Parallel model, except the irreparable damage necessarily follows a strict sequence. For simplicity, we refer to the state of having multiple elements as ‘redundancy’, but this does not imply that the elements are necessarily identical. We show that redundancy leads to actuarial senescence in the Parallel and Cascade models but not in the Series model. We further demonstrate that in the Parallel and Cascade models, lifetime reproductive output (a potential proxy for fitness in populations with discrete generations) is a positive but decelerating function of redundancy. The positive nature of the fitness function leads to the prediction that redundancy and senescence should evolve from non-redundant, non-senescing ancestral populations; however, the deceleration of the fitness function leads to the prediction that this evolution towards increased redundancy will eventually be limited by mutation-selection balance. Using evolutionary dynamics analysis involving the discrete-generation quasispecies equation, we confirm these two predictions. Finally, we show that a population's equilibrium redundancy is sensitive to the environmental conditions that prevailed during its evolution, such as the rate of extrinsic mortality.     

Mortality plateaus and directionality theory

Recent large scale studies of senescence in animals and humans have revealed mortality rates that levelled off at advanced ages. These empirical findings are now known to be inconsistent with evolutionary theories of senescence based on the Malthusian parameter as a measure of fitness. This article analyses the incidence of mortality plateaus in terms of directionality theory, a new class of models based on evolutionary entropy as a measure of fitness. We show that the intensity of selection, in the context of directionality theory, is a convex function of age, and we invoke this property to predict that in populations evolving under bounded growth constraints, evolutionarily stable mortality patterns will be described by rates which abate with age at extreme ages. The explanatory power of directionality theory, in contrast with the limitations of the Malthusian model, accords with the claim that evolutionary entropy, rather than the Malthusian parameter, constitutes the operationally valid measure of Darwinian fitness.     

Genetic diversity,virulence and fitness evolution in an obligate fungal parasite of bees

Within‐host competition is predicted to drive the evolution of virulence in parasites, but the precise outcomes of such interactions are often unpredictable due to many factors including the biology of the host and the parasite, stochastic events and co‐evolutionary interactions. Here, we use a serial passage experiment (SPE) with three strains of a heterothallic fungal parasite (Ascosphaera apis) of the Honey bee (Apis mellifera) to assess how evolving under increasing competitive pressure affects parasite virulence and fitness evolution. The results show an increase in virulence after successive generations of selection and consequently faster production of spores. This faster sporulation, however, did not translate into more spores being produced during this longer window of sporulation; rather, it appeared to induce a loss of fitness in terms of total spore production. There was no evidence to suggest that a greater diversity of competing strains was a driver of this increased virulence and subsequent fitness cost, but rather that strain‐specific competitive interactions influenced the evolutionary outcomes of mixed infections. It is possible that the parasite may have evolved to avoid competition with multiple strains because of its heterothallic mode of reproduction, which highlights the importance of understanding parasite biology when predicting disease dynamics.     

Developmental constraints on fin diversity

The fish fin is a breathtaking repository full of evolutionary diversity, novelty, and convergence. Over 500 million years, the adaptation to novel habitats has provided landscapes of fin diversity. Although comparative anatomy of evolutionarily divergent patterns over centuries has highlighted the fundamental architectures and evolutionary trends of fins, including convergent evolution, the developmental constraints on fin evolution, which bias the evolutionary trajectories of fin morphology, largely remain elusive. Here, we review the evolutionary history, developmental mechanisms, and evolutionary underpinnings of paired fins, illuminating possible developmental constraints on fin evolution. Our compilation of anatomical and genetic knowledge of fin development sheds light on the canalized and the unpredictable aspects of fin shape in evolution. Leveraged by an arsenal of genomic and genetic tools within the working arena of spectacular fin diversity, evolutionary developmental biology embarks on the establishment of conceptual framework for developmental constraints, previously enigmatic properties of evolution.