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Background
Host-microbe and microbe-microbe interactions are often governed by the complex exchange of metabolites. Such interactions play a key role in determining the way pathogenic and commensal species impact their host and in the assembly of complex microbial communities. Recently, several studies have demonstrated how such interactions are reflected in the organization of the metabolic networks of the interacting species, and introduced various graph theory-based methods to predict host-microbe and microbe-microbe interactions directly from network topology. Using these methods, such studies have revealed evolutionary and ecological processes that shape species interactions and community assembly, highlighting the potential of this reverse-ecology research paradigm.Results
NetCooperate is a web-based tool and a software package for determining host-microbe and microbe-microbe cooperative potential. It specifically calculates two previously developed and validated metrics for species interaction: the Biosynthetic Support Score which quantifies the ability of a host species to supply the nutritional requirements of a parasitic or a commensal species, and the Metabolic Complementarity Index which quantifies the complementarity of a pair of microbial organisms’ niches. NetCooperate takes as input a pair of metabolic networks, and returns the pairwise metrics as well as a list of potential syntrophic metabolic compounds.Conclusions
The Biosynthetic Support Score and Metabolic Complementarity Index provide insight into host-microbe and microbe-microbe metabolic interactions. NetCooperate determines these interaction indices from metabolic network topology, and can be used for small- or large-scale analyses. NetCooperate is provided as both a web-based tool and an open-source Python module; both are freely available online at http://elbo.gs.washington.edu/software_netcooperate.html. 相似文献2.
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An open-source tool capable of converting SD files (and virtually any other format through OpenBabel) into MMFF-typed XYZ
coordinate files to be used with TINKER is described. SDF2XYZ2SDF allows including the power of TINKER molecular mechanics
computations in automated cheminformatics workflows, such as conformational searches and virtual screening protocols. 相似文献
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The brain mechanism of extracting visual features for recognizing various objects has consistently been a controversial issue in computational models of object recognition. To extract visual features, we introduce a new, biologically motivated model for facial categorization, which is an extension of the Hubel and Wiesel simple-to-complex cell hierarchy. To address the synaptic stability versus plasticity dilemma, we apply the Adaptive Resonance Theory (ART) for extracting informative intermediate level visual features during the learning process, which also makes this model stable against the destruction of previously learned information while learning new information. Such a mechanism has been suggested to be embedded within known laminar microcircuits of the cerebral cortex. To reveal the strength of the proposed visual feature learning mechanism, we show that when we use this mechanism in the training process of a well-known biologically motivated object recognition model (the HMAX model), it performs better than the HMAX model in face/non-face classification tasks. Furthermore, we demonstrate that our proposed mechanism is capable of following similar trends in performance as humans in a psychophysical experiment using a face versus non-face rapid categorization task. 相似文献
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Shiri Klein Ron Avrahami Eyal Zussman Michael Beliavski Sheldon Tarre Michal Green 《Journal of industrial microbiology & biotechnology》2012,39(11):1605-1613
Electrospun hollow polymeric microfibers (microtubes) were evaluated as an encapsulation method for the atrazine degrading bacterium Pseudomonas sp. ADP. Pseudomonas sp. ADP cells were successfully incorporated in a formulation containing a core solution of polyethylene oxide dissolved in water and spun with an outer shell solution made of polycaprolactone and polyethylene glycol dissolved in a chloroform and dimethylformamide. The resulting microtubes, collected as mats, were partially collapsed with a ribbon-like structure. Following encapsulation, the atrazine degradation rate was low (0.03?±?0.01?mg atrazine/h/g fiber) indicating that the electrospinning process negatively affected cell activity. Atrazine degradation was restored to 0.5?±?0.1?mg atrazine/h/g fiber by subjecting the microtubes to a period of growth. After 3 and 7?days growth periods, encapsulated cells were able to remove 20.6?±?3 and 47.6?±?5.9?mg atrazine/g mat, respectively, in successive batches under non-growth conditions (with no additional electron donor) until atrazine was detected in the medium. The loss of atrazine degrading capacity was regained following an additional cell-growth period. 相似文献
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Alterations in gene expression resulting from Alzheimer’s disease have received considerable attention in recent years. Although expression has been investigated separately in whole brain tissue, in astrocytes and in neurons, a rigorous comparative study quantifying the relative utility of these sources in predicting the progression of Alzheimer’s disease has been lacking. Here we analyze gene expression from neurons, astrocytes and whole tissues across different brain regions, and compare their ability to predict Alzheimer’s disease progression by building pertaining classification models based on gene expression sets annotated to different biological processes. Remarkably, we find that predictions based on neuronal gene expression are significantly more accurate than those based on astrocyte or whole tissue expression. The findings explicate the central role of neurons, particularly as compared to glial cells, in the pathogenesis of Alzheimer’s disease, and emphasize the importance of measuring gene expression in the most relevant (pathogenically ‘proximal’) single cell types. 相似文献
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Fereshteh Shiri Maryam Salahinejad Rahmatollah Dijoor Massoud Nejati-Yazdinejad 《Journal of receptor and signal transduction research》2018,38(2):151-165
Pathogenic Gram-negative bacteria are responsible for nearly half of the serious human infections. Hologram quantitative structure–activity relationships (HQSAR), comparative molecular field analysis (CoMFA), and comparative molecular similarity index analysis (CoMSIA) were implemented on a group of 32 of potent Gram-negative LpxC inhibitors. The most effective HQSAR model was obtained using atoms, bonds, donor, and acceptor as fragment distinction. The cross-validated correlation coefficient (q2), non-cross-validated correlation coefficient (r2), and predictive correlation coefficient (r2Pred) for test set of HQSAR model were 0.937, 0.993, and 0.892, respectively. The generated models were found to be statistically significant as the CoMFA model had (r2?=?0.967, q2?=?0.804, r2Pred?=?0.827); the CoMSIA model had (r2?=?0.963, q2?=?0.752, r2Pred?=?0.857). Molecular docking was employed to validate the results of the HQSAR, CoMFA, and CoMSIA models. Based on the obtained information, six new LpxC inhibitors have been designed. 相似文献
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