SciBERT-based Semantification of Bioassays in the Open Research Knowledge Graph

As a novel contribution to the problem of semantifying bio- logical assays, in this paper, we propose a neural-network-based approach to automatically semantify, thereby structure, unstructured bioassay text descriptions. Experimental evaluations, to this end, show promise as the neural-based semantification significantly outperforms a naive frequencybased baseline approach. Specifically, the neural method attains 72% F1 versus 47% F1 from the frequency-based method. The work in this paper aligns with the present cutting-edge trend of the scholarly knowledge digitalization impetus which aim to convert the long-standing document-based format of scholarly content into knowledge graphs (KG). To this end, our selected data domain of bioassays are a prime candidate for structuring into KGs

SciBERT-based semantification of bioassays in the open research knowledge graph

As a novel contribution to the problem of semantifying biological assays, in this paper, we propose a neural-network-based approach to automatically semantify, thereby structure, unstructured bioassay text descriptions. Experimental evaluations, to this end, show promise as the neural-based semantification significantly outperforms a naive frequencybased baseline approach. Specifically, the neural method attains 72% F1 versus 47% F1 from the frequency-based method. The work in this paper aligns with the present cutting-edge trend of the scholarly knowledge digitalization impetus which aim to convert the long-standing document-based format of scholarly content into knowledge graphs (KG). To this end, our selected data domain of bioassays are a prime candidate for structuring into KG

Metabolic engineering of Pseudomonas putida KT2440 for medium-chain-length fatty alcohol and ester production from fatty acids

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Regulation of three virulence strategies of Mycobacterium tuberculosis : A success story

Tuberculosis remains one of the deadliest diseases. Emergence of drug-resistant and multidrug-resistant M. tuberculosis strains makes treating tuberculosis increasingly challenging. In order to develop novel intervention strategies, detailed understanding of the molecular mechanisms behind the success of this pathogen is required. Here, we review recent literature to provide a systems level overview of the molecular and cellular components involved in divalent metal homeostasis and their role in regulating the three main virulence strategies of M. tuberculosis: immune modulation, dormancy and phagosomal rupture. We provide a visual and modular overview of these components and their regulation. Our analysis identified a single regulatory cascade for these three virulence strategies that respond to limited availability of divalent metals in the phagosome

Risk-Based Bioengineering Strategies for Reliable Bacterial Vaccine Production

Design of a reliable process for bacterial antigen production requires understanding of and control over critical process parameters. Current methods for process design use extensive screening experiments for determining ranges of critical process parameters yet fail to give clear insights into how they influence antigen potency. To address this gap, we propose to apply constraint-based, genome-scale metabolic models to reduce the need of experimental screening for strain selection and to optimize strains based on model driven iterative Design–Build–Test–Learn (DBTL) cycles. Application of these systematic methods has not only increased the understanding of how metabolic network properties influence antigen potency, but also allows identification of novel critical process parameters that need to be controlled to achieve high process reliability.</p

Metabolomics in systems medicine: an overview of methods and applications

Patient-derived metabolomics offers valuable insights into the metabolic phenotype underlying diseases with a strong metabolic component. Thus, these data sets will be pivotal to the implementation of personalized medicine strategies in health and disease. However, to take full advantage of such data sets, they must be integrated with other omics within a coherent pathophysiological framework to enable improved diagnostics, to identify therapeutic interventions, and to accurately stratify patients. Herein, we provide an overview of the state-of-the-art data analysis and modeling approaches applicable to metabolomics data and of their potential for systems medicine

SyNDI : Synchronous network data integration framework

Background: Systems biology takes a holistic approach by handling biomolecules and their interactions as big systems. Network based approach has emerged as a natural way to model these systems with the idea of representing biomolecules as nodes and their interactions as edges. Very often the input data come from various sorts of omics analyses. Those resulting networks sometimes describe a wide range of aspects, for example different experiment conditions, species, tissue types, stimulating factors, mutants, or simply distinct interaction features of the same network produced by different algorithms. For these scenarios, synchronous visualization of more than one distinct network is an excellent mean to explore all the relevant networks efficiently. In addition, complementary analysis methods are needed and they should work in a workflow manner in order to gain maximal biological insights. Results: In order to address the aforementioned needs, we have developed a Synchronous Network Data Integration (SyNDI) framework. This framework contains SyncVis, a Cytoscape application for user-friendly synchronous and simultaneous visualization of multiple biological networks, and it is seamlessly integrated with other bioinformatics tools via the Galaxy platform. We demonstrated the functionality and usability of the framework with three biological examples - we analyzed the distinct connectivity of plasma metabolites in networks associated with high or low latent cardiovascular disease risk; deeper insights were obtained from a few similar inflammatory response pathways in Staphylococcus aureus infection common to human and mouse; and regulatory motifs which have not been reported associated with transcriptional adaptations of Mycobacterium tuberculosis were identified. Conclusions: Our SyNDI framework couples synchronous network visualization seamlessly with additional bioinformatics tools. The user can easily tailor the framework for his/her needs by adding new tools and datasets to the Galaxy platform.</p

Evaluation of diurnal responses of Tetradesmus obliquus under nitrogen limitation

Tetradesmus obliquus is an oleaginous microalga with high potential for triacylglycerol production. We characterized the biochemical composition and the transcriptional landscape of T. obliquus wild-type and the starchless mutant (slm1), adapted to 16:8 h light dark (LD) cycles under nitrogen limitation. In comparison to the nitrogen replete conditions, the diurnal RNA samples from both strains also displayed a cyclic pattern, but with much less variation which could be related to a reduced transcription activity in at least the usually highly active processes. During nitrogen limitation, the wild-type continued to use starch as the preferred storage compound to store energy and carbon. Starch was accumulated to an average content of 0.25 g·gDW−1, which is higher than the maximum observed under nitrogen replete conditions. Small oscillations were observed, indicating that starch was being used as a diurnal energy storage compound, but to a lesser extent than under nitrogen replete conditions. For the slm1 mutant, TAG content was higher than for the wild-type (average steady state value was 0.26 g·gDW−1 for slm1 compared to 0.06 g·gDW−1 for the wild-type). Despite the higher TAG content in the slm1, the conversion efficiency of photons into biomass components for the slm1 was only half of the one obtained for the wild-type. This is related to the observed decrease in biomass productivity (from 1.29 gDW·L−1·day−1 for the wild-type to 0.52 gDW·L−1·day−1 for the slm1). While the transcriptome of slm1 displayed clear signs of energy generation by degrading TAG and amino-acids during the dark period, no significant variation of these metabolites could be measured. When looking through the diurnal cycle, the photosynthetic efficiency was lower for the slm1 mutant compared to the wild-type especially during the second half of the light period, where starch accumulation occurred in the wild-type.publishedVersionPaid Open Acces

SALARECON connects the Atlantic salmon genome to growth and feed efficiency

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Computational Approaches for Peroxisomal Protein Localization

Computational approaches are practical when investigating putative peroxisomal proteins and for sub-peroxisomal protein localization in unknown protein sequences. Nowadays, advancements in computational methods and Machine Learning (ML) can be used to hasten the discovery of novel peroxisomal proteins and can be combined with more established computational methodologies. Here, we explain and list some of the most used tools and methodologies for novel peroxisomal protein detection and localization