63,234 research outputs found

    A flexible view of metabolic network

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    Cooling system for removing metabolic heat from an hermetically sealed spacesuit

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    An improved cooling and ventilating system is described for removing metabolic heat, waste gases and water vapor generated by a wearer of an hermetically sealed spacesuit. The cooling system was characterized by a body suit, having a first circuit for simultaneously establishing a cooling flow of water through the thorax and head sections of the body suit. Circulation patches were included mounted in the thorax section and head section of the body suit. A second circuit for discharing a flow of gas throughout the spacesuit and a disconnect unit for coupling the circuits with a life support system externally related to the spacesuit were provided

    MetExploreViz: web component for interactive metabolic network visualization

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    Summary: MetExploreViz is an open source web component that can be easily embedded in any web site. It provides features dedicated to the visualization of metabolic networks and pathways and thus offers a flexible solution to analyse omics data in a biochemical context. Availability and implementation: Documentation and link to GIT code repository (GPL 3.0 license) are available at this URL: http://metexplore.toulouse.inra.fr/metexploreViz/doc

    Rigidity and flexibility of biological networks

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    The network approach became a widely used tool to understand the behaviour of complex systems in the last decade. We start from a short description of structural rigidity theory. A detailed account on the combinatorial rigidity analysis of protein structures, as well as local flexibility measures of proteins and their applications in explaining allostery and thermostability is given. We also briefly discuss the network aspects of cytoskeletal tensegrity. Finally, we show the importance of the balance between functional flexibility and rigidity in protein-protein interaction, metabolic, gene regulatory and neuronal networks. Our summary raises the possibility that the concepts of flexibility and rigidity can be generalized to all networks.Comment: 21 pages, 4 figures, 1 tabl

    Visualization of metabolic interaction networks in microbial communities using VisANT 5.0

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    The complexity of metabolic networks in microbial communities poses an unresolved visualization and interpretation challenge. We address this challenge in the newly expanded version of a software tool for the analysis of biological networks, VisANT 5.0. We focus in particular on facilitating the visual exploration of metabolic interaction between microbes in a community, e.g. as predicted by COMETS (Computation of Microbial Ecosystems in Time and Space), a dynamic stoichiometric modeling framework. Using VisANT's unique metagraph implementation, we show how one can use VisANT 5.0 to explore different time-dependent ecosystem-level metabolic networks. In particular, we analyze the metabolic interaction network between two bacteria previously shown to display an obligate cross-feeding interdependency. In addition, we illustrate how a putative minimal gut microbiome community could be represented in our framework, making it possible to highlight interactions across multiple coexisting species. We envisage that the "symbiotic layout" of VisANT can be employed as a general tool for the analysis of metabolism in complex microbial communities as well as heterogeneous human tissues.This work was supported by the National Institutes of Health, R01GM103502-05 to CD, ZH and DS. Partial support was also provided by grants from the Office of Science (BER), U.S. Department of Energy (DE-SC0004962), the Joslin Diabetes Center (Pilot & Feasibility grant P30 DK036836), the Army Research Office under MURI award W911NF-12-1-0390, National Institutes of Health (1RC2GM092602-01, R01GM089978 and 5R01DE024468), NSF (1457695), and Defense Advanced Research Projects Agency Biological Technologies Office (BTO), Program: Biological Robustness In Complex Settings (BRICS), Purchase Request No. HR0011515303, Program Code: TRS-0 Issued by DARPA/CMO under Contract No. HR0011-15-C-0091. Funding for open access charge: National Institutes of Health. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. (R01GM103502-05 - National Institutes of Health; 1RC2GM092602-01 - National Institutes of Health; R01GM089978 - National Institutes of Health; 5R01DE024468 - National Institutes of Health; DE-SC0004962 - Office of Science (BER), U.S. Department of Energy; P30 DK036836 - Joslin Diabetes Center; W911NF-12-1-0390 - Army Research Office under MURI; 1457695 - NSF; HR0011515303 - Defense Advanced Research Projects Agency Biological Technologies Office (BTO), Program: Biological Robustness In Complex Settings (BRICS); HR0011-15-C-0091 - DARPA/CMO; National Institutes of Health)Published versio

    In Silico Genome-Scale Reconstruction and Validation of the Staphylococcus aureus Metabolic Network

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    A genome-scale metabolic model of the Gram-positive, facultative anaerobic opportunistic pathogen Staphylococcus aureus N315 was constructed based on current genomic data, literature, and physiological information. The model comprises 774 metabolic processes representing approximately 23% of all protein-coding regions. The model was extensively validated against experimental observations and it correctly predicted main physiological properties of the wild-type strain, such as aerobic and anaerobic respiration and fermentation. Due to the frequent involvement of S. aureus in hospital-acquired bacterial infections combined with its increasing antibiotic resistance, we also investigated the clinically relevant phenotype of small colony variants and found that the model predictions agreed with recent findings of proteome analyses. This indicates that the model is useful in assisting future experiments to elucidate the interrelationship of bacterial metabolism and resistance. To help directing future studies for novel chemotherapeutic targets, we conducted a large-scale in silico gene deletion study that identified 158 essential intracellular reactions. A more detailed analysis showed that the biosynthesis of glycans and lipids is rather rigid with respect to circumventing gene deletions, which should make these areas particularly interesting for antibiotic development. The combination of this stoichiometric model with transcriptomic and proteomic data should allow a new quality in the analysis of clinically relevant organisms and a more rationalized system-level search for novel drug targets.

    Augmenting Biogas Process Modeling by Resolving Intracellular Metabolic Activity

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    The process of anaerobic digestion in which waste biomass is transformed to methane by complex microbial communities has been modeled for more than 16 years by parametric gray box approaches that simplify process biology and do not resolve intracellular microbial activity. Information on such activity, however, has become available in unprecedented detail by recent experimental advances in metatranscriptomics and metaproteomics. The inclusion of such data could lead to more powerful process models of anaerobic digestion that more faithfully represent the activity of microbial communities. We augmented the Anaerobic Digestion Model No. 1 (ADM1) as the standard kinetic model of anaerobic digestion by coupling it to Flux-Balance-Analysis (FBA) models of methanogenic species. Steady-state results of coupled models are comparable to standard ADM1 simulations if the energy demand for non-growth associated maintenance (NGAM) is chosen adequately. When changing a constant feed of maize silage from continuous to pulsed feeding, the final average methane production remains very similar for both standard and coupled models, while both the initial response of the methanogenic population at the onset of pulsed feeding as well as its dynamics between pulses deviates considerably. In contrast to ADM1, the coupled models deliver predictions of up to 1,000s of intracellular metabolic fluxes per species, describing intracellular metabolic pathway activity in much higher detail. Furthermore, yield coefficients which need to be specified in ADM1 are no longer required as they are implicitly encoded in the topology of the species’ metabolic network. We show the feasibility of augmenting ADM1, an ordinary differential equation-based model for simulating biogas production, by FBA models implementing individual steps of anaerobic digestion. While cellular maintenance is introduced as a new parameter, the total number of parameters is reduced as yield coefficients no longer need to be specified. The coupled models provide detailed predictions on intracellular activity of microbial species which are compatible with experimental data on enzyme synthesis activity or abundance as obtained by metatranscriptomics or metaproteomics. By providing predictions of intracellular fluxes of individual community members, the presented approach advances the simulation of microbial community driven processes and provides a direct link to validation by state-of-the-art experimental techniques

    Representing and analysing molecular and cellular function in the computer

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    Determining the biological function of a myriad of genes, and understanding how they interact to yield a living cell, is the major challenge of the post genome-sequencing era. The complexity of biological systems is such that this cannot be envisaged without the help of powerful computer systems capable of representing and analysing the intricate networks of physical and functional interactions between the different cellular components. In this review we try to provide the reader with an appreciation of where we stand in this regard. We discuss some of the inherent problems in describing the different facets of biological function, give an overview of how information on function is currently represented in the major biological databases, and describe different systems for organising and categorising the functions of gene products. In a second part, we present a new general data model, currently under development, which describes information on molecular function and cellular processes in a rigorous manner. The model is capable of representing a large variety of biochemical processes, including metabolic pathways, regulation of gene expression and signal transduction. It also incorporates taxonomies for categorising molecular entities, interactions and processes, and it offers means of viewing the information at different levels of resolution, and dealing with incomplete knowledge. The data model has been implemented in the database on protein function and cellular processes 'aMAZE' (http://www.ebi.ac.uk/research/pfbp/), which presently covers metabolic pathways and their regulation. Several tools for querying, displaying, and performing analyses on such pathways are briefly described in order to illustrate the practical applications enabled by the model

    Analysis of the fluctuations of the tumour/host interface

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    In a recent analysis of metabolic scaling in solid tumours we found a scaling law that interpolates between the power laws μ∝V and μ∝V2∕3, where μ is the metabolic rate expressed as the glucose absorption rate and V is the tumour volume. The scaling law fits quite well both in vitro and in vivo data, however we also observed marked fluctuations that are associated with the specific biological properties of individual tumours. Here we analyse these fluctuations, in an attempt to find the population-wide distribution of an important parameter (A) which expresses the total extent of the interface between the solid tumour and the non-cancerous environment. Heuristic considerations suggest that the values of the A parameter follow a lognormal distribution, and, allowing for the large uncertainties of the experimental data, our statistical analysis confirms this
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