Co-expression of signaling proteins improves robustness of the bacterial chemotaxis pathway

Biological systems are exposed to various perturbations that affect performance of the cellular networks, with stochastic variation in protein levels, or gene expression noise, being one of the major sources of intracellular perturbations. We recently used Escherichia coli chemotaxis as a model to analyze robustness against such noise and demonstrated theoretically and experimentally that a steady-state output of the pathway is robust against concerted variation in the levels of all chemotaxis proteins. However, our model predicted that the pathway topology does not confer much robustness against an uncorrelated variation in the protein levels. To test whether additional robustness features might be missing from our model, we compare here its predictions with an experimentally determined chemotactic performance under varying levels of individual proteins. Our data show that the pathway is indeed even more robust than predicted to two types of perturbations-the variation in the levels of the adaptation enzymes and a correlated expression of CheY and CheZ. Although the design features that are responsible for this higher robustness still remain to be understood, our results stress the importance of a robust design of both native and synthetic signaling networks.     

Extremes of human heat tolerance: life at the precipice of thermoregulatory failure

1. Human life is sustainable only below an internal temperature of roughly 42–44 °C. Yet our ability to survive at severe environmental extremes is testimony to the marvels of integrative human physiology.

2. One approach to understanding human thermoregulatory capacity is to examine the upper limits of thermal balance between man and the air environment, i.e. the maximal environmental conditions under which humans can maintain a steady-state core temperature. Heat acclimation expands the zone of thermal balance.

3. Human beings can and do, often willingly, tolerate extreme heat stresses well above these thermal balance limits. Survival in all such cases is limited to abbreviated exposure times, which in turn are limited by the robustness of the thermoregulatory response.

4. Figures are provided that relate tolerance time and the rate of change in core temperature to environmental characteristics based on data compiled from the literature.     

Thermal effects vary predictably across levels of organization: empirical results and theoretical basis

Thermal performance curves have provided a common framework to study the impact of temperature in biological systems. However, few generalities have emerged to date. Here, we combine an experimental approach with theoretical analyses to demonstrate that performance curves are expected to vary predictably with the levels of biological organization. We measured rates of enzymatic reactions, organismal performance and population viability in Drosophila acclimated to different thermal conditions and show that performance curves become narrower with thermal optima shifting towards lower temperatures at higher levels or organization. We then explain these results on theoretical grounds, showing that this pattern reflects the cumulative impact of asymmetric thermal effects that piles up with complexity. These results and the proposed framework are important to understand how organisms, populations and ecological communities might respond to changing thermal conditions.     

Cyclic variations in incubation conditions induce adaptive responses to later heat exposure in chickens: a review

Selection programs have enabled broiler chickens to gain muscle mass without similar enlargement of the cardiovascular and respiratory systems that are essential for thermoregulatory efficiency. Meat-type chickens cope with high ambient temperature by reducing feed intake and growth during chronic and moderate heat exposure. In case of acute heat exposure, a dramatic increase in morbidity and mortality can occur. In order to alleviate heat stress in the long term, research has recently focused on early thermal manipulation. Aimed at stimulation of long-term thermotolerance, the thermal manipulation of embryos is a method based on fine tuning of incubation conditions, taking into account the level and duration of increases in temperature and relative humidity during a critical period of embryogenesis. The consequences of thermal manipulation on the performance and meat quality of broiler chickens have been explored to ensure the potential application of this strategy. The physiological basis of the method is the induction of epigenetic and metabolic mechanisms that control body temperature in the long term. Early thermal manipulation can enhance poultry resistance to environmental changes without much effect on growth performance. This review presents the main strategies of early heat exposure and the physiological concepts on which these methods were based. The cellular mechanisms potentially underlying the adaptive response are discussed as well as the potential interest of thermal manipulation of embryos for poultry production.     

Network Translation and Steady-State Properties of Chemical Reaction Systems

Network translation has recently been used to establish steady-state properties of mass action systems by corresponding the given system to a generalized one which is either dynamically or steady-state equivalent. In this work, we further use network translation to identify network structures which give rise to the well-studied property of absolute concentration robustness in the corresponding mass action systems. In addition to establishing the capacity for absolute concentration robustness, we show that network translation can often provide a method for deriving the steady-state value of the robust species. We furthermore present a MILP algorithm for the identification of translated chemical reaction networks that improves on previous approaches, allowing for easier application of the theory.     

Counter-Gradient Variation in Respiratory Performance of Coral Reef Fishes at Elevated Temperatures

The response of species to global warming depends on how different populations are affected by increasing temperature throughout the species'' geographic range. Local adaptation to thermal gradients could cause populations in different parts of the range to respond differently. In aquatic systems, keeping pace with increased oxygen demand is the key parameter affecting species'' response to higher temperatures. Therefore, respiratory performance is expected to vary between populations at different latitudes because they experience different thermal environments. We tested for geographical variation in respiratory performance of tropical marine fishes by comparing thermal effects on resting and maximum rates of oxygen uptake for six species of coral reef fish at two locations on the Great Barrier Reef (GBR), Australia. The two locations, Heron Island and Lizard Island, are separated by approximately 1200 km along a latitudinal gradient. We found strong counter-gradient variation in aerobic scope between locations in four species from two families (Pomacentridae and Apogonidae). High-latitude populations (Heron Island, southern GBR) performed significantly better than low-latitude populations (Lizard Island, northern GBR) at temperatures up to 5°C above average summer surface-water temperature. The other two species showed no difference in aerobic scope between locations. Latitudinal variation in aerobic scope was primarily driven by up to 80% higher maximum rates of oxygen uptake in the higher latitude populations. Our findings suggest that compensatory mechanisms in high-latitude populations enhance their performance at extreme temperatures, and consequently, that high-latitude populations of reef fishes will be less impacted by ocean warming than will low-latitude populations.     

Phytoplankton thermal responses adapt in the absence of hard thermodynamic constraints

To better predict how populations and communities respond to climatic temperature variation, it is necessary to understand how the shape of the response of fitness-related rates to temperature evolves (the thermal performance curve). Currently, there is disagreement about the extent to which the evolution of thermal performance curves is constrained. One school of thought has argued for the prevalence of thermodynamic constraints through enzyme kinetics, whereas another argues that adaptation can—at least partly—overcome such constraints. To shed further light on this debate, we perform a phylogenetic meta-analysis of the thermal performance curves of growth rate of phytoplankton—a globally important functional group—controlling for environmental effects (habitat type and thermal regime). We find that thermodynamic constraints have a minor influence on the shape of the curve. In particular, we detect a very weak increase of maximum performance with the temperature at which the curve peaks, suggesting a weak “hotter-is-better” constraint. Also, instead of a constant thermal sensitivity of growth across species, as might be expected from strong constraints, we find that all aspects of the thermal performance curve evolve along the phylogeny. Our results suggest that phytoplankton thermal performance curves adapt to thermal environments largely in the absence of hard thermodynamic constraints.     

Dimensionless solutions and general characteristics of bioheat transfer during thermal therapy

The derivation and application of the general characteristics of bioheat transfer for medical applications are shown in this paper. Two general bioheat transfer characteristics are derived from solutions of one-dimensional Pennes’ bioheat transfer equation: steady-state thermal penetration depth, which is the deepest depth where the heat effect reaches; and time to reach steady-state, which represents the amount of time necessary for temperature distribution to converge to a steady-state. All results are described by dimensionless form; therefore, these results provide information on temperature distribution in biological tissue for various thermal therapies by transforming to dimension form.     

Differential dynamic properties of scleroderma fibroblasts in response to perturbation of environmental stimuli

Diseases are believed to arise from dysregulation of biological systems (pathways) perturbed by environmental triggers. Biological systems as a whole are not just the sum of their components, rather ever-changing, complex and dynamic systems over time in response to internal and external perturbation. In the past, biologists have mainly focused on studying either functions of isolated genes or steady-states of small biological pathways. However, it is systems dynamics that play an essential role in giving rise to cellular function/dysfunction which cause diseases, such as growth, differentiation, division and apoptosis. Biological phenomena of the entire organism are not only determined by steady-state characteristics of the biological systems, but also by intrinsic dynamic properties of biological systems, including stability, transient-response, and controllability, which determine how the systems maintain their functions and performance under a broad range of random internal and external perturbations. As a proof of principle, we examine signal transduction pathways and genetic regulatory pathways as biological systems. We employ widely used state-space equations in systems science to model biological systems, and use expectation-maximization (EM) algorithms and Kalman filter to estimate the parameters in the models. We apply the developed state-space models to human fibroblasts obtained from the autoimmune fibrosing disease, scleroderma, and then perform dynamic analysis of partial TGF-beta pathway in both normal and scleroderma fibroblasts stimulated by silica. We find that TGF-beta pathway under perturbation of silica shows significant differences in dynamic properties between normal and scleroderma fibroblasts. Our findings may open a new avenue in exploring the functions of cells and mechanism operative in disease development.     

The role of temperature variability on insect performance and population dynamics in a warming world

Despite the amount of research on the consequences of global warming on ecological systems, most studies examine the impact of increases in average temperature. However, there are few studies concerning the role of thermal variability on ecological processes. Based on insect thermal and population ecology, we propose a theoretical framework for organizing the study of the role that thermal mean and variability plays in individual performance, and how it may affect population dynamics. Starting with three predictions of global warming scenarios, we develop null models of the expected changes in individual physiological performance and population dynamics. Ecological consequences in each scenario may range from simple changes in performance to drastic changes in population fluctuations and geographic ranges. In particular, our null models show that potential changes in the intrinsic population growth rate (R_m) will depend on the interaction of mean temperature and thermal variability, and that the net effect of the interaction could be synergistic or antagonistic. To evaluate these null models, we fit performance curves to compiled data from the literature on measurements of R_m at several constant and fluctuating temperatures. The fitted models showed that several of the qualitative characteristics predicted by the null model may be found in the fitted curves. We expect that this framework will be useful as a guide to study the influence of thermal changes on the dynamics of natural populations. Synthesis Despite the common assertion that global warming impacts depend on not only the mean temperatures but also on thermal variability, theoretical approaches to explain how the interaction of thermal mean and variability determines fitness are lacking. Here we propose a framework for studying the role of thermal mean and variability on individual performance and population dynamics. We developed null models that show how changes in the intrinsic population growth rate (R_m) will depend on the interaction of mean temperature and thermal variability, and that the net effect could be synergistic or antagonistic. We expect that this framework will be useful to study the influence of thermal changes on natural populations.     

Ca2+ signaling via the neuronal calcium sensor-1 regulates associative learning and memory in C. elegans

On a radial temperature gradient, C. elegans worms migrate, after conditioning with food, toward their cultivation temperature and move along this isotherm. This experience-dependent behavior is called isothermal tracking (IT). Here we show that the neuron-specific calcium sensor-1 (NCS-1) is essential for optimal IT. ncs-1 knockout animals show major defects in IT behavior, although their chemotactic, locomotor, and thermal avoidance behaviors are normal. The knockout phenotype can be rescued by reintroducing wild-type NCS-1 into the AIY interneuron, a key component of the thermotaxis network. A loss-of-function form of NCS-1 incapable of binding calcium does not restore IT, whereas NCS-1 overexpression enhances IT performance levels, accelerates learning (faster acquisition), and produces a memory with slower extinction. Thus, proper calcium signaling via NCS-1 defines a novel pathway essential for associative learning and memory.     

Predicting the growth of the amphibian chytrid fungus in varying temperature environments

Environmental temperature is a crucial abiotic factor that influences the success of ectothermic organisms, including hosts and pathogens in disease systems. One example is the amphibian chytrid fungus, Batrachochytrium dendrobatidis (Bd), which has led to widespread amphibian population declines. Understanding its thermal ecology is essential to effectively predict outbreaks. Studies that examine the impact of temperature on hosts and pathogens often do so in controlled constant temperatures. Although varying temperature experiments are becoming increasingly common, it is unrealistic to test every temperature scenario. Thus, reliable methods that use constant temperature data to predict performance in varying temperatures are needed. In this study, we tested whether we could accurately predict Bd growth in three varying temperature regimes, using a Bayesian hierarchical model fit with constant temperature Bd growth data. We fit the Bayesian hierarchical model five times, each time changing the thermal performance curve (TPC) used to constrain the logistic growth rate to determine how TPCs influence the predictions. We then validated the model predictions using Bd growth data collected from the three tested varying temperature regimes. Although all TPCs overpredicted Bd growth in the varying temperature regimes, some functional forms performed better than others. Varying temperature impacts on disease systems are still not well understood and improving our understanding and methodologies to predict these effects could provide insights into disease systems and help conservation efforts.     

DOES ENVIRONMENTAL ROBUSTNESS PLAY A ROLE IN FLUCTUATING ENVIRONMENTS?

Fluctuating environments are expected to select for individuals that have highest geometric fitness over the experienced environments. This leads to the prediction that genetically determined environmental robustness in fitness, and average fitness across environments should be positively genetically correlated to fitness in fluctuating environments. Because quantitative genetic experiments resolving these predictions are missing, we used a full‐sib, half‐sib breeding design to estimate genetic variance for egg‐to‐adult viability in Drosophila melanogaster exposed to two constant or fluctuating temperatures that were above the species’ optimum temperature, during development. Viability in two constant environments (25°C or 30°C) was used to estimate breeding values for environmental robustness of viability (i.e., reaction norm slope) and overall viability (reaction norm elevation). These breeding values were regressed against breeding values of viability at two different fluctuating temperatures (with a mean of 25°C or 30°C). Our results based on genetic correlations show that average egg‐to‐adult viability across different constant thermal environments, and not the environmental robustness, was the most important factor for explaining the fitness in fluctuating thermal environments. Our results suggest that the role of environmental robustness in adapting to fluctuating environments might be smaller than anticipated.     

Perfect and near-perfect adaptation in a model of bacterial chemotaxis

The signaling apparatus mediating bacterial chemotaxis can adapt to a wide range of persistent external stimuli. In many cases, the bacterial activity returns to its prestimulus level exactly, and this perfect adaptability is robust against variations in various chemotaxis protein concentrations. We model the bacterial chemotaxis signaling pathway, from ligand binding to CheY phosphorylation. By solving the steady-state equations of the model analytically, we derive a full set of conditions for the system to achieve perfect adaptation. The conditions related to the phosphorylation part of the pathway are discovered for the first time, while other conditions are generalizations of the ones found in previous works. Sensitivity of the perfect adaptation is evaluated by perturbing these conditions. We find that, even in the absence of some of the perfect adaptation conditions, adaptation can be achieved with near-perfect precision as a result of the separation of scales in both chemotaxis protein concentrations and reaction rates, or specific properties of the receptor distribution in different methylation states. Since near-perfect adaptation can be found in much larger regions of the parameter space than that defined by the perfect adaptation conditions, their existence is essential to understand robustness in bacterial chemotaxis.     

Yield optimization of regulated metabolic systems using deterministic branch-and-reduce methods

This paper addresses product yield optimization in microorganisms grown in continuous culture. Traditional optimization strategies of random mutagenesis and selection will eventually have limited efficacy, thus requiring more focused strategies. The best candidates for such strategies appear to be mathematical models that capture the essence of metabolic systems and permit optimization with computational methods. In the past, models used for this purpose have been stoichiometric, kinetic in the form of S-systems, or ad hoc. This work presents a deterministic approach based on generalized mass action (GMA) systems. These systems are interesting in that they allow direct merging of stoichiometric and S-system models. Two illustrations are considered. In the first case, the fermentation pathway in Saccharomyces cerevisiae is optimized for ethanol production under steady-state conditions. The model of this pathway is relatively small, with five states and eight rate constants. The second example addresses the maximization of citric acid in the mold Aspergillus niger. For the optimization of this larger pathway system with 30 states and 60 reactions, a Mixed Integer Nonlinear Programming (MINLP) is proposed. It is shown that efficient MINLP algorithms, based on convexification, branch-and-reduce methods, and binary variable selection, are essential for solving these difficult optimization problems.     

Optimal design of feedback control by inhibition

The local stability of unbranched biosynthetic pathways is examined by mathematical analysis and computer simulation using a novel nonlinear formalism that appears to accurately describe biochemical systems. Four factors affecting the stability are examined: strength of feedback inhibition, equalization of the values among the corresponding kinetic parameters for the reactions of the pathway, pathway length, and alternative patterns of feedback interactions. The strength of inhibition and the pattern of feedback interactions are important determinants of steady-state behavior. The simple pattern of end-product inhibition in unbranched pathways may have evolved because it optimizes the steady-state behavior and is temporally most responsive to change. Stability in these simple systems is achieved by shortening pathway length either physically or, in the case of necessarily long pathways, kinetically by a wide devergence in the values of the corresponding kinetic parameters for the reactions of the pathway. These conclusions are discussed in the light of available experimental evidence.     

Conservation of glycolytic oscillations in Saccharomyces cerevisiae and human pancreatic beta-cells: a study of metabolic robustness

The present study compares two computer models of the first part of glucose catabolism in different organisms in search of evolutionarily conserved characteristics of the glycolysis cycle and proposes the main parameters that define the stable steady-state or oscillatory behavior of the glycolytic system. It is suggested that in both human pancreatic beta-cells and Saccharomyces cerevisiae there are oscillations that, despite differences in wave form and period of oscillation, share the same robustness strategy: the oscillation is not controlled by only one but by at least two parameters that will have more or less control over the pathway flux depending on the initial state of the system as well as on extra-cellular conditions. This observation leads to two important interpretations: the first is that in both S. cerevisiae and human beta-cells, despite differences in enzyme kinetics and mechanism of feedback control, evolution seems to have kept an oscillatory behavior coupled to the glucose concentration outside the cytoplasm, and the second is that the development of drugs to regulate metabolic dysfunctions in more complex systems may require further study, not only determining which enzyme is controlling the flux of the system but also under which conditions and how its control is maintained by the enzyme or transferred to other enzymes in the pathway as the drug starts acting.     

Warming will affect phytoplankton differently: evidence through a mechanistic approach

Although the consequences of global warming in aquatic ecosystems are only beginning to be revealed, a key to forecasting the impact on aquatic communities is an understanding of individual species' vulnerability to increased temperature. Despite their microscopic size, phytoplankton support about half of the global primary production, drive essential biogeochemical cycles and represent the basis of the aquatic food web. At present, it is known that phytoplankton are important targets and, consequently, harbingers of climate change in aquatic systems. Therefore, investigating the capacity of phytoplankton to adapt to the predicted warming has become a relevant issue. However, considering the polyphyletic complexity of the phytoplankton community, different responses to increased temperature are expected. We experimentally tested the effects of warming on 12 species of phytoplankton isolated from a variety of environments by using a mechanistic approach able to assess evolutionary adaptation (the so-called ratchet technique). We found different degrees of tolerance to temperature rises and an interspecific capacity for genetic adaptation. The thermal resistance level reached by each species is discussed in relation to their respective original habitats. Our study additionally provides evidence on the most resistant phytoplankton groups in a future warming scenario.     

An Analytical Queueing Network Model for Flexible Manufacturing Systems with a Discrete Handling Device and Transfer Blockings

In this paper, we study job shop-like flexible manufacturing systems (FMSs) with a discrete material handling system (MHS). In such FMSs, the MHS is a critical device, the unavailability of which may induce transfer blockings of the machines. The FMS devices therefore are hierarchically structured into primary and secondary devices to manage such blocking and avoid deadlocks in these FMSs. For evaluating the quantitative steady-state performance of such FMSs, we propose an analytical queueing network model that relies on an approximate method proposed for analyzing computer systems with simultaneous possessions of resources. Such a model is obtained using the concept of passive resources and by aggregating the FMS workload data so that models are much more tractable. The analytical results are validated against discrete event simulation and shown to be very encouraging. We also show how to increase their robustness, especially under light workload conditions, by modifying an assumption of the method concerning service time distributions.     

Evolution of phenotypic fluctuation under host-parasite interactions

Robustness and plasticity are essential features that allow biological systems to cope with complex and variable environments. In a constant environment, robustness, i.e., insensitivity of phenotypes, is expected to increase, whereas plasticity, i.e., the changeability of phenotypes, tends to diminish. Under a variable environment, existence of plasticity will be relevant. The robustness and plasticity, on the other hand, are related to phenotypic variances. As phenotypic variances decrease with the increase in robustness to perturbations, they are expected to decrease through the evolution. However, in nature, phenotypic fluctuation is preserved to a certain degree. One possible cause for this is environmental variation, where one of the most important “environmental” factors will be inter-species interactions. As a first step toward investigating phenotypic fluctuation in response to an inter-species interaction, we present the study of a simple two-species system that comprises hosts and parasites. Hosts are expected to evolve to achieve a phenotype that optimizes fitness. Then, the robustness of the corresponding phenotype will be increased by reducing phenotypic fluctuations. Conversely, plasticity tends to evolve to avoid certain phenotypes that are attacked by parasites. By using a dynamic model of gene expression for the host, we investigate the evolution of the genotype-phenotype map and of phenotypic variances. If the host–parasite interaction is weak, the fittest phenotype of the host evolves to reduce phenotypic variances. In contrast, if there exists a sufficient degree of interaction, the phenotypic variances of hosts increase to escape parasite attacks. For the latter case, we found two strategies: if the noise in the stochastic gene expression is below a certain threshold, the phenotypic variance increases via genetic diversification, whereas above this threshold, it is increased mediated by noise-induced phenotypic fluctuation. We examine how the increase in the phenotypic variances caused by parasite interactions influences the growth rate of a single host, and observed a trade-off between the two. Our results help elucidate the roles played by noise and genetic mutations in the evolution of phenotypic fluctuation and robustness in response to host–parasite interactions.