Selection for rapid uptake of scarce or fluctuating resource explains vulnerability of glycolysis to imbalance

Glycolysis is a conserved central pathway in energy metabolism that converts glucose to pyruvate with net production of two ATP molecules. Because ATP is produced only in the lower part of glycolysis (LG), preceded by an initial investment of ATP in the upper glycolysis (UG), achieving robust start-up of the pathway upon activation presents a challenge: a sudden increase in glucose concentration can throw a cell into a self-sustaining imbalanced state in which UG outpaces LG, glycolytic intermediates accumulate and the cell is unable to maintain high ATP concentration needed to support cellular functions. Such metabolic imbalance can result in "substrate-accelerated death", a phenomenon observed in prokaryotes and eukaryotes when cells are exposed to an excess of substrate that previously limited growth. Here, we address why evolution has apparently not eliminated such a costly vulnerability and propose that it is a manifestation of an evolutionary trade-off, whereby the glycolysis pathway is adapted to quickly secure scarce or fluctuating resource at the expense of vulnerability in an environment with ample resource. To corroborate this idea, we perform individual-based eco-evolutionary simulations of a simplified yeast glycolysis pathway consisting of UG, LG, phosphate transport between a vacuole and a cytosol, and a general ATP demand reaction. The pathway is evolved in constant or fluctuating resource environments by allowing mutations that affect the (maximum) reaction rate constants, reflecting changing expression levels of different glycolytic enzymes. We demonstrate that under limited constant resource, populations evolve to a genotype that exhibits balanced dynamics in the environment it evolved in, but strongly imbalanced dynamics under ample resource conditions. Furthermore, when resource availability is fluctuating, imbalanced dynamics confers a fitness advantage over balanced dynamics: when glucose is abundant, imbalanced pathways can quickly accumulate the glycolytic intermediate FBP as intracellular storage that is used during periods of starvation to maintain high ATP concentration needed for growth. Our model further predicts that in fluctuating environments, competition for glucose can result in stable coexistence of balanced and imbalanced cells, as well as repeated cycles of population crashes and recoveries that depend on such polymorphism. Overall, we demonstrate the importance of ecological and evolutionary arguments for understanding seemingly maladaptive aspects of cellular metabolism

Del MA al mar. El museo de la historia de Andalucía

Un edificio fuerte y radical evocará en su seno la semblanza de una comunidad con un rico pasado cultural que se proyecta hacia el futuro. El arquitecto explica las claves de este centro, levantado en Granada

Content Based Model Transformations: Solutions to Existing Issues with Application in Information Security

Model-Driven Engineering uses models in various stages of the software engineering. To reduce the cost of modelling and production, models are reused by transforming. Therefore the accuracy of model transformations plays a key role in ensuring the quality of the process. However, problems exist when trying to transform a very abstract and content dependent model. This paper describes the issues arising from such transformations. Solutions to solve problems in content based model transformation are proposed as well. The usage of proposed solutions allowing realization of semi-automatic transformations was integrated into a tool, designed for OPC/XML drawing file transformations to CySeMoL models. The accuracy of transformations in this tool has been analyzed and presented in this paper to acquire data on the proposed solutions influence to the accuracy in content based model transformation

Superior color rendering with a phosphor-converted blue-cyan monolithic light-emitting diode

The future generation of modern illumination should not only be cheap and highly efficient, but also demonstrate high quality of light, light which allows better color differentiation and fidelity. Here we are presenting a novel approach to create a white solid-state light source providing ultimate color rendition necessary for a number of applications. The proposed semi-hybrid device combines a monolithic blue-cyan light emitting diode (MBC LED) with a green-red phosphor mixture. It has shown a superior color rendering index (CRI), 98.6, at correlated color temperature of around 3400 K. The MBC LED epi-structure did not suffer from the efficiency reduction typical for monolithic multi-color emitters and was implemented in the two most popular chip designs: “epi-up” and “flip-chip”. Redistribution of the blue and cyan band amplitudes in the white-light emission spectrum, using the operating current, is found to be an effective tool for fine tuning the color characteristics. (Figure presented.)

A deterministic oscillatory model of microtubule growth and shrinkage for differential actions of short chain fatty acids.

Short chain fatty acids (SCFA), principally acetate, propionate, butyrate and valerate, are produced in pharmacologically relevant concentrations by the gut microbiome. Investigations indicate that they exert beneficial effects on colon epithelia. There is increasing interest in whether different SCFAs have distinct functions which may be exploited for prevention or treatment of colonic diseases including colorectal cancer (CRC), inflammatory bowel disease and obesity. Based on experimental evidence, we hypothesised that odd-chain SCFAs may possess anti-mitotic capabilities in colon cancer cells by disrupting microtubule (MT) structural integrity via dysregulation of β-tubulin isotypes. MT dynamic instability is an essential characteristic of MT cellular activity. We report a minimal deterministic model that takes a novel approach to explore the hypothesised pathway by triggering spontaneous oscillations to represent MT dynamic behaviour. The dynamicity parameters in silico were compared to those reported in vitro. Simulations of untreated and butyrate (even-chain length) treated cells reflected MT behaviour in interphase or untreated control cells. The propionate and valerate (odd-chain length) simulations displayed increased catastrophe frequencies and longer periods of MT-fibre shrinkage. Their enhanced dynamicity was dissimilar to that observed in mitotic cells, but parallel to that induced by MT-destabilisation treatments. Antimicrotubule drugs act through upward or downward modulation of MT dynamic instability. Our computational modelling suggests that metabolic engineering of the microbiome may facilitate managing CRC risk by predicting outcomes of SCFA treatments in combination with AMDs

Model based dynamics analysis in live cell microtubule images

Background: The dynamic growing and shortening behaviors of microtubules are central to the fundamental roles played by microtubules in essentially all eukaryotic cells. Traditionally, microtubule behavior is quantified by manually tracking individual microtubules in time-lapse images under various experimental conditions. Manual analysis is laborious, approximate, and often offers limited analytical capability in extracting potentially valuable information from the data. Results: In this work, we present computer vision and machine-learning based methods for extracting novel dynamics information from time-lapse images. Using actual microtubule data, we estimate statistical models of microtubule behavior that are highly effective in identifying common and distinct characteristics of microtubule dynamic behavior. Conclusion: Computational methods provide powerful analytical capabilities in addition to traditional analysis methods for studying microtubule dynamic behavior. Novel capabilities, such as building and querying microtubule image databases, are introduced to quantify and analyze microtubule dynamic behavior

Mathematical modelling and numerical simulation of the morphological development of neurons

BACKGROUND: The morphological development of neurons is a very complex process involving both genetic and environmental components. Mathematical modelling and numerical simulation are valuable tools in helping us unravel particular aspects of how individual neurons grow their characteristic morphologies and eventually form appropriate networks with each other. METHODS: A variety of mathematical models that consider (1) neurite initiation (2) neurite elongation (3) axon pathfinding, and (4) neurite branching and dendritic shape formation are reviewed. The different mathematical techniques employed are also described. RESULTS: Some comparison of modelling results with experimental data is made. A critique of different modelling techniques is given, leading to a proposal for a unified modelling environment for models of neuronal development. CONCLUSION: A unified mathematical and numerical simulation framework should lead to an expansion of work on models of neuronal development, as has occurred with compartmental models of neuronal electrical activity

Phenotypic Heterogeneity and the Evolution of Bacterial Life Cycles

Most bacteria live in colonies, where they often express different cell types. The ecological significance of these cell types and their evolutionary origin are often unknown. Here, we study the evolution of cell differentiation in the context of surface colonization. We particularly focus on the evolution of a ‘sticky’ cell type that is required for surface attachment, but is costly to express. The sticky cells not only facilitate their own attachment, but also that of non-sticky cells. Using individual-based simulations, we show that surface colonization rapidly evolves and in most cases leads to phenotypic heterogeneity, in which sticky and non-sticky cells occur side by side on the surface. In the presence of regulation, cell differentiation leads to a remarkable set of bacterial life cycles, in which cells alternate between living in the liquid and living on the surface. The dominant life stage is formed by the surface-attached colony that shows many complex features: colonies reproduce via fission and by producing migratory propagules; cells inside the colony divide labour; and colonies can produce filaments to facilitate expansion. Overall, our model illustrates how the evolution of an adhesive cell type goes hand in hand with the evolution of complex bacterial life cycles

Challenges in microbial ecology: building predictive understanding of community function and dynamics.

The importance of microbial communities (MCs) cannot be overstated. MCs underpin the biogeochemical cycles of the earth's soil, oceans and the atmosphere, and perform ecosystem functions that impact plants, animals and humans. Yet our ability to predict and manage the function of these highly complex, dynamically changing communities is limited. Building predictive models that link MC composition to function is a key emerging challenge in microbial ecology. Here, we argue that addressing this challenge requires close coordination of experimental data collection and method development with mathematical model building. We discuss specific examples where model-experiment integration has already resulted in important insights into MC function and structure. We also highlight key research questions that still demand better integration of experiments and models. We argue that such integration is needed to achieve significant progress in our understanding of MC dynamics and function, and we make specific practical suggestions as to how this could be achieved