Metagenomic analysis of two enhanced biological phosphorus removal (EBPR) sludge communities

Enhanced biological phosphorus removal (EBPR) is one of the best-studied microbially mediated industrial processes because of its ecological and economic relevance. Despite this, it is not well understood at the metabolic level. Here we present a metagenomic analysis of two lab-scale EBPR sludges dominated by the uncultured bacterium, Candidatus Accumulibacter phosphatis.'' The analysis sheds light on several controversies in EBPR metabolic models and provides hypotheses explaining the dominance of A. phosphatis in this habitat, its lifestyle outside EBPR and probable cultivation requirements. Comparison of the same species from different EBPR sludges highlights recent evolutionary dynamics in the A. phosphatis genome that could be linked to mechanisms for environmental adaptation. In spite of an apparent lack of phylogenetic overlap in the flanking communities of the two sludges studied, common functional themes were found, at least one of them complementary to the inferred metabolism of the dominant organism. The present study provides a much needed blueprint for a systems-level understanding of EBPR and illustrates that metagenomics enables detailed, often novel, insights into even well-studied biological systems.

Prediction of protein continuum secondary structure with probabilistic models based on NMR solved structures

Background: The structure of proteins may change as a result of the inherent flexibility of some protein regions. We develop and explore probabilistic machine learning methods for predicting a continuum secondary structure, i.e. assigning probabilities to the conformational states of a residue. We train our methods using data derived from high-quality NMR models. Results: Several probabilistic models not only successfully estimate the continuum secondary structure, but also provide a categorical output on par with models directly trained on categorical data. Importantly, models trained on the continuum secondary structure are also better than their categorical counterparts at identifying the conformational state for structurally ambivalent residues. Conclusion: Cascaded probabilistic neural networks trained on the continuum secondary structure exhibit better accuracy in structurally ambivalent regions of proteins, while sustaining an overall classification accuracy on par with standard, categorical prediction methods.

Predicting population dynamics of weed biological control agents: science or gazing into crystal balls?

Various factors can influence the population dynamics of phytophages post introduction, of which climate is fundamental. Here we present an approach, using a mechanistic modelling package (CLIMEX), that at least enables one to make predictions of likely dynamics based on climate alone. As biological control programs will have minimal funding for basic work (particularly on population dynamics), we show how predictions can be made using a species geographical distribution, relative abundance across its range, seasonal phenology and laboratory rearing data. Many of these data sets are more likely to be available than long-term population data, and some can be incorporated into the exploratory phase of a biocontrol program. Although models are likely to be more robust the more information is available, useful models can be developed using information on species distribution alone. The fitted model estimates a species average response to climate, and can be used to predict likely geographical distribution if introduced, where the agent is likely to be more abundant (i.e. good locations) and more importantly for interpretation of release success, the likely variation in abundance over time due to intra- and inter-year climate variability. The latter will be useful in predicting both the seasonal and long-term impacts of the potential biocontrol agent on the target weed. We believe this tool may not only aid in the agent selection process, but also in the design of release strategies, and for interpretation of post-introduction dynamics and impacts. More importantly we are making testable predictions. If biological control is to become more of a science making and testing such hypothesis will be a key component.

Stochastic models for regulatory networks of the genetic toggle switch

Bistability arises within a wide range of biological systems from the A phage switch in bacteria to cellular signal transduction pathways in mammalian cells. Changes in regulatory mechanisms may result in genetic switching in a bistable system. Recently, more and more experimental evidence in the form of bimodal population distributions indicates that noise plays a very important role in the switching of bistable systems. Although deterministic models have been used for studying the existence of bistability properties under various system conditions, these models cannot realize cell-to-cell fluctuations in genetic switching. However, there is a lag in the development of stochastic models for studying the impact of noise in bistable systems because of the lack of detailed knowledge of biochemical reactions, kinetic rates, and molecular numbers. in this work, we develop a previously undescribed general technique for developing quantitative stochastic models for large-scale genetic regulatory networks by introducing Poisson random variables into deterministic models described by ordinary differential equations. Two stochastic models have been proposed for the genetic toggle switch interfaced with either the SOS signaling pathway or a quorum-sensing signaling pathway, and we have successfully realized experimental results showing bimodal population distributions. Because the introduced stochastic models are based on widely used ordinary differential equation models, the success of this work suggests that this approach is a very promising one for studying noise in large-scale genetic regulatory networks.

Mixture models for detecting differentially expressed genes in microarrays

An important and common problem in microarray experiments is the detection of genes that are differentially expressed in a given number of classes. As this problem concerns the selection of significant genes from a large pool of candidate genes, it needs to be carried out within the framework of multiple hypothesis testing. In this paper, we focus on the use of mixture models to handle the multiplicity issue. With this approach, a measure of the local FDR (false discovery rate) is provided for each gene. An attractive feature of the mixture model approach is that it provides a framework for the estimation of the prior probability that a gene is not differentially expressed, and this probability can subsequently be used in forming a decision rule. The rule can also be formed to take the false negative rate into account. We apply this approach to a well-known publicly available data set on breast cancer, and discuss our findings with reference to other approaches.

On parameter estimation in population models

We describe methods for estimating the parameters of Markovian population processes in continuous time, thus increasing their utility in modelling real biological systems. A general approach, applicable to any finite-state continuous-time Markovian model, is presented, and this is specialised to a computationally more efficient method applicable to a class of models called density-dependent Markov population processes. We illustrate the versatility of both approaches by estimating the parameters of the stochastic SIS logistic model from simulated data. This model is also fitted to data from a population of Bay checkerspot butterfly (Euphydryas editha bayensis), allowing us to assess the viability of this population. (c) 2006 Elsevier Inc. All rights reserved.

Biomechanics of the rostrum in crocodilians: A comparative analysis using finite-element modeling

This article reports the use of simple beam and finite-element models to investigate the relationship between rostral shape and biomechanical performance in living crocodilians under a range of loading conditions. Load cases corresponded to simple biting, lateral head shaking, and twist feeding behaviors. The six specimens were chosen to reflect, as far as possible, the full range of rostral shape in living crocodilians: a juvenile Caiman crocodilus, subadult Alligator mississippiensis and Crocodylus johnstoni, and adult Caiman crocodilus, Melanosuchus niger, and Paleosuchus palpebrosus. The simple beam models were generated using morphometric landmarks from each specimen. Three of the finite-element models, the A. mississippiensis, juvenile Caiman crocodilus, and the Crocodylus johnstoni, were based on CT scan data from respective specimens, but these data were not available for the other models and so these-the adult Caiman crocodilus, M. niger, and P. palpebrosus-were generated by morphing the juvenile Caiman crocodilus mesh with reference to three-dimensional linear distance measured from specimens. Comparison of the mechanical performance of the six finite-element models essentially matched results of the simple beam models: relatively tall skulls performed best under vertical loading and tall and wide skulls performed best under torsional loading. The widely held assumption that the platyrostral (dorsoventrally flattened) crocodilian skull is optimized for torsional loading was not supported by either simple beam theory models or finite-element modeling. Rather than being purely optimized against loads encountered while subduing and processing food, the shape of the crocodilian rostrum may be significantly affected by the hydrodynamic constraints of catching agile aquatic prey. This observation has important implications for our understanding of biomechanics in crocodilians and other aquatic reptiles.

Evaluation of different models to describe egg and pupal development of Xyleborus fornicatus Eichh. (Coleoptera: Scolytidae), the shot-hole borer of tea in Sri Lanka

Abstract Development data of eggs and pupae of Xyleborus fornicatus Eichh. (Coleoptera: Scolytidae), the shot-hole borer of tea in Sri Lanka, at constant temperatures were used to evaluate a linear and seven nonlinear models for insect development. Model evaluation was based on fit to data (residual sum of squares and coefficient of determination or coefficient of nonlinear regression), number of measurable parameters, the biological value of the fitted coefficients and accuracy in the estimation of thresholds. Of the nonlinear models, the Lactin model fitted experimental data well and along with the linear model, can be used to describe the temperature-dependent development of this species.

Determinants of biological activity of nanomaterials in innate immunity cells: the role of aspect ratio, environmental contaminants and protein corona

As defined by the European Union, “ ’Nanomaterial’ (NM) means a natural, incidental or manufactured material containing particles, in an unbound state or as an aggregate or agglomerate, where, for 50 % or more of the particles in the number size distribution, one or more external dimensions is in the size range 1 nm-100 nm ” (2011/696/UE). Given their peculiar physico-chemical features, nanostructured materials are largely used in many industrial fields (e.g. cosmetics, electronics, agriculture, biomedical) and their applications have astonishingly increased in the last fifteen years. Nanostructured materials are endowed with very large specific surface area that, besides making them very useful in many industrial processes, renders them very reactive towards the biological systems and, hence, potentially endowed with significant hazard for human health. For these reasons, in recent years, many studies have been focused on the identification of toxic properties of nanostructured materials, investigating, in particular, the mechanisms behind their toxic effects as well as their determinants of toxicity. This thesis investigates two types of nanostructured TiO2 materials, TiO2 nanoparticles (NP), which are yearly produced in tonnage quantities, and TiO2 nanofibres (NF), a relatively novel nanomaterial. Moreover, several preparations of MultiWalled Carbon Nanotubes (MWCNT), another nanomaterial widely present in many products, are also investigated.- Although many in vitro and in vivo studies have characterized the toxic properties of these materials, the identification of their determinants of toxicity is still incomplete. The aim of this thesis is to identify the structural determinants of toxicity, using several in vitro models. Specific fields of investigation have been a) the role of shape and the aspect ratio in the determination of biological effects of TiO2 nanofibres of different length; b) the synergistic effect of LPS and TiO2 NP on the expression of inflammatory markers and the role played therein by TLR-4; c) the role of functionalization and agglomeration in the biological effects of MWCNT. As far as biological effects elicited by TiO2 NF are concerned, the first part of the thesis demonstrates that long TiO2 nanofibres caused frustrated phagocytosis, cytotoxicity, hemolysis, oxidative stress and epithelial barrier perturbation. All these effects were mitigated by fibre shortening through ball-milling. However, short TiO2 NF exhibited enhanced ability to activate acute pro-inflammatory effects in macrophages, an effect dependent on phagocytosis. Therefore, aspect ratio reduction mitigated toxic effects, while enhanced macrophage activation, likely rendering the NF more prone to phagocytosis. These results suggest that, under in vivo conditions, short NF will be associated with acute inflammatory reaction, but will undergo a relatively rapid clearance, while long NF, although associated with a relatively smaller acute activation of innate immunity cells, are not expected to be removed efficiently and, therefore, may be associated to chronic inflammatory responses. As far as the relationship between the effects of TiO2 NP and LPS, investigated in the second part of the thesis, are concerned, TiO2 NP markedly enhanced macrophage activation by LPS through a TLR-4-dependent intracellular pathway. The adsorption of LPS onto the surface of TiO2 NP led to the formation of a specific bio-corona, suggesting that, when bound to TiO2 NP, LPS exerts a much more powerful pro-inflammatory effect. These data suggest that the inflammatory changes observed upon exposure to TiO2 NP may be due, at least in part, to their capability to bind LPS and, possibly, other TLR agonists, thus enhancing their biological activities. Finally, the last part of the thesis demonstrates that surface functionalization of MWCNT with amino or carboxylic groups mitigates the toxic effects of MWCNT in terms of macrophage activation and capability to perturb epithelial barriers. Interestingly, surface chemistry (in particular surface charge) influenced the protein adsorption onto the MWCNT surface, allowing to the formation of different protein coronae and the tendency to form agglomerates of different size. In particular functionalization a) changed the amount and the type of proteins adsorbed to MWCNT and b) enhanced the tendency of MWCNT to form large agglomerates. These data suggest that the different biological behavior of functionalized and pristine MWCNT may be due, at least in part, to the different tendency to form large agglomerates, which is significantly influenced by their different capability to interact with proteins contained in biological fluids. All together, these data demonstrate that the interaction between physico-chemical properties of nanostructured materials and the environment (cells + biological fluids) in which these materials are present is of pivotal importance for the understanding of the biological effects of NM. In particular, bio-persistence and the capability to elicit an effective inflammatory response are attributable to the interaction between NM and macrophages. However, the interaction NM-cells is heavily influenced by the formation at the nano-bio interface of specific bio-coronae that confer a novel biological identity to the nanostructured materials, setting the basis for their specific biological activities.

Comparing different approach and avoidance models of learning and personality in the prediction of work, university, and leadership outcomes

Jackson (2005) developed a hybrid model of personality and learning, known as the learning styles profiler (LSP) which was designed to span biological, socio-cognitive, and experiential research foci of personality and learning research. The hybrid model argues that functional and dysfunctional learning outcomes can be best understood in terms of how cognitions and experiences control, discipline, and re-express the biologically based scale of sensation-seeking. In two studies with part-time workers undertaking tertiary education (N=137 and 58), established models of approach and avoidance from each of the three different research foci were compared with Jackson's hybrid model in their predictiveness of leadership, work, and university outcomes using self-report and supervisor ratings. Results showed that the hybrid model was generally optimal and, as hypothesized, that goal orientation was a mediator of sensation-seeking on outcomes (work performance, university performance, leader behaviours, and counterproductive work behaviour). Our studies suggest that the hybrid model has considerable promise as a predictor of work and educational outcomes as well as dysfunctional outcomes.