Sensitivity analysis of Repast computational ecology models with R/Repast

Computational ecology is an emerging interdisciplinary discipline founded mainly on modeling and simulation methods for studying ecological systems. Among the existing modeling formalisms, the individual‐based modeling is particularly well suited for capturing the complex temporal and spatial dynamics as well as the nonlinearities arising in ecosystems, communities, or populations due to individual variability. In addition, being a bottom‐up approach, it is useful for providing new insights on the local mechanisms which are generating some observed global dynamics. Of course, no conclusions about model results could be taken seriously if they are based on a single model execution and they are not analyzed carefully. Therefore, a sound methodology should always be used for underpinning the interpretation of model results. The sensitivity analysis is a methodology for quantitatively assessing the effect of input uncertainty in the simulation output which should be incorporated compulsorily to every work based on in‐silico experimental setup. In this article, we present R/Repast a GNU R package for running and analyzing Repast Simphony models accompanied by two worked examples on how to perform global sensitivity analysis and how to interpret the results.     

Protein-protein interactions between two-component system transmitter and receiver domains of Myxococcus xanthus

We present a novel dataset assessing the specificity of protein-protein interactions between 69 transmitter and receiver domains from two-component system (TCS)-signalling pathways. TCS require a conserved protein-protein interaction between partner transmitter and receiver domains for signal transduction. The complex prokaryote Myxococcus xanthus possesses an unusually large number of TCS genes, many of which have no obvious interaction partners. Interactions between TCS domains of M. xanthus were assessed using a yeast two-hybrid assay, in which domains were expressed as both bait and prey translational fusions. LacZ production was monitored as an indicator of protein-protein interaction, and the strength of interactions classified as weak, medium or strong. Two-hundred and fifty-five transmitter-receiver domain interactions were observed (46 strong), allowing identification of potential signalling partners for individual M. xanthus TCS proteins. In addition, the dataset provides interesting 'meta' information. For instance, many strong interactions were identified between different transmitter domain pairs (34) and receiver domain pairs (23), suggesting a surprisingly large degree of heterodimerisation of these domains. Proteins in our dataset that exhibited similar 'profiles' of interactions, often shared a similar biological function, suggesting that interaction profiles can provide information on biological function, even considering sets of homologous domains.     

Local spin dynamics in magnetic molecular chains studied by NMR and μSR

We present NMR and μ⁺SR study of spin dynamics in one-dimensional and quasi-one-dimensional molecular magnets of recent synthesis. In particular, we focus on the so called Gd(hfac)₃NIT-R and CoPhOMe magnetic chains families. For Gd-R helimagnets we show some differences between “weakly frustrated systems” and “fully frustrated systems”. The different behaviour is due to the different radical inserted in the chains (R = Me, Ph for “weakly frustrated systems” and R = iPr, Et for “fully frustrated systems”). The existence of different phase transitions, particularly to 3D long-range magnetic order in Gd-Ph and to chiral order in Gd-iPr, is remarked together with a comparison between results obtained from macroscopic and microscopic investigating techniques. As regards CoPhOMe slowly relaxing chain, the ¹H NMR measurements confirm the freezing of the spins at low temperature, which prevents the 3D long-range order, and display the presence of two relaxation mechanisms related to distinct contributions to the local spin relaxation.     

Colloidal stability of carbon nanotubes in an aqueous dispersion of phospholipid

Within the family of nanomaterials, carbon nanotubes (CNTs) have emerged as a new efficient scaffold for studying molecular interactions at interfaces. Poor dispersability of CNTs in any solvent presents a considerable drawback for the development of novel functional composite structures. Previous studies have demonstrated that the solubility of CNTs can be greatly enhanced by employing appropriate surfactants, some of them being biological molecules. In this work, we study the noncovalent wrapping of lipid chains onto the graphitic surface of single-walled material (SWCNTs) by electron microscopy and Raman spectroscopy. Stable and homogenous aqueous suspensions of SWCNTs in the presence of lipids have been prepared, whereas their electrophoretic mobility was confirmed by zeta-potential measurements. Raman measurements revealed that smaller diameter SWCNTs are preferentially dispersed by lipid molecules in the aqueous supernatant part of the prepared suspension.     

Microbial metabolomics: toward a platform with full metabolome coverage

Achieving metabolome data with satisfactory coverage is a formidable challenge in metabolomics because metabolites are a chemically highly diverse group of compounds. Here we present a strategy for the development of an advanced analytical platform that allows the comprehensive analysis of microbial metabolomes. Our approach started with in silico metabolome information from three microorganisms-Escherichia coli, Bacillus subtilis, and Saccharomyces cerevisiae-and resulted in a list of 905 different metabolites. Subsequently, these metabolites were classified based on their physicochemical properties, followed by the development of complementary gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry methods, each of which analyzes different metabolite classes. This metabolomics platform, consisting of six different analytical methods, was applied for the analysis of the metabolites for which commercial standards could be purchased (399 compounds). Of these 399 metabolites, 380 could be analyzed with the platform. To demonstrate the potential of this metabolomics platform, we report on its application to the analysis of the metabolome composition of mid-logarithmic E. coli cells grown on a mineral salts medium using glucose as the carbon source. Of the 431 peaks detected, 235 (=176 unique metabolites) could be identified. These include 61 metabolites that were not previously identified or annotated in existing E. coli databases.     

Process for the integrated extraction, identification and quantification of metabolites, proteins and RNA to reveal their co-regulation in biochemical networks

A novel extraction protocol is described with which metabolites, proteins and RNA are sequentially extracted from the same sample, thereby providing a convenient procedure for the analysis of replicates as well as exploiting the inherent biological variation of independent samples for multivariate data analysis. A detection of 652 metabolites, 297 proteins and clear RNA bands in a single Arabidopsis thaliana leaf sample was validated by analysis with gas chromatography coupled to a time of flight mass spectrometer for metabolites, two-dimensional liquid chromatography coupled to mass spectrometry for proteins, and Northern blot analysis for RNA. A subset of the most abundant proteins and metabolites from replicate analysis of different Arabidopsis accessions was merged to form an integrative dataset allowing both classification of different genotypes and the unbiased analysis of the hierarchical organization of proteins and metabolites within a real biochemical network.     

Genome-scale dynamic modeling of the competition between Rhodoferax and Geobacter in anoxic subsurface environments

The advent of rapid complete genome sequencing, and the potential to capture this information in genome-scale metabolic models, provide the possibility of comprehensively modeling microbial community interactions. For example, Rhodoferax and Geobacter species are acetate-oxidizing Fe(III)-reducers that compete in anoxic subsurface environments and this competition may have an influence on the in situ bioremediation of uranium-contaminated groundwater. Therefore, genome-scale models of Geobacter sulfurreducens and Rhodoferax ferrireducens were used to evaluate how Geobacter and Rhodoferax species might compete under diverse conditions found in a uranium-contaminated aquifer in Rifle, CO. The model predicted that at the low rates of acetate flux expected under natural conditions at the site, Rhodoferax will outcompete Geobacter as long as sufficient ammonium is available. The model also predicted that when high concentrations of acetate are added during in situ bioremediation, Geobacter species would predominate, consistent with field-scale observations. This can be attributed to the higher expected growth yields of Rhodoferax and the ability of Geobacter to fix nitrogen. The modeling predicted relative proportions of Geobacter and Rhodoferax in geochemically distinct zones of the Rifle site that were comparable to those that were previously documented with molecular techniques. The model also predicted that under nitrogen fixation, higher carbon and electron fluxes would be diverted toward respiration rather than biomass formation in Geobacter, providing a potential explanation for enhanced in situ U(VI) reduction in low-ammonium zones. These results show that genome-scale modeling can be a useful tool for predicting microbial interactions in subsurface environments and shows promise for designing bioremediation strategies.     

An unbiased sensitivity analysis reveals important parameters controlling periodicity of circadian clock

To assess the importance of model parameters in kinetic models, sensitivity analysis is generally employed to provide key measures. However, it is quite often that no information is available for a significant number of parameters in biochemical models. Therefore, the results of sensitivity analysis that heavily rely on the accuracy of parameters are largely ambiguous. In this study, we propose a computational approach to determine the relative importance of parameters controlling the performance of the circadian clock in Drosophila. While previous attempts to sensitivity analysis largely depend on the knowledge of model parameters which are generally unknown, our study depicts a consistent picture of sensitivity assessment for a large number of parameters, even when the values of these parameters are not available in vivo. The resulting parametric sensitivity analysis suggests that PER/TIM negative loop is critical to maintain the stable periodicity of the circadian clock, which is consistent to the previously experimental and computational findings. Furthermore, our analysis generates a rich hypothesis of important parameters in the circadian clock that can be further tested experimentally. This approach can also be extended to assess the sensitivity of parameters in any biochemical system where a large number of parameters have unknown values. Biotechnol. Bioeng. 2010; 105: 250–259. © 2009 Wiley Periodicals, Inc.     

Hepatitis C Virus Experimental Model Systems and Antiviral drug Research

An estimated 130 million people worldwide are chronically infected with hepatitis C virus (HCV) making it a leading cause of liver disease worldwide. Because the currently available therapy of pegylated interferon-alpha and ribavirin is only effective in a subset of patients, the development of new HCV antivirals is a healthcare imperative. This review discusses the experimental models available for HCV antiviral drug research, recent advances in HCV antiviral drug development, as well as active research be...