1,721,027 research outputs found
Translational efficiency in gas-fermenting bacteria: Adding a new layer of regulation to gene expression in acetogens
Full text linkMajor advances in mastering metabolism of single carbon (C1) gaseous feedstocks in acetogenic microorganisms are primed to fuel the transition toward environmentally sustainable and cost-efficient production schemes of biofuels and value-added biochemicals. Since acetogens grow under autotrophic energy-limited conditions, protein synthesis is expected to be controlled. This survey integrated publicly available RNA sequencing and ribosome profiling studies of several acetogens, providing data on genome-scale transcriptional and translational responses of A. woodii, E. limosum, C. drakei, and C. ljungdahlii to autotrophic and heterotrophic growth conditions. The extent of translational efficiency turned out to vary across key functional modules in acetogens’ metabolism. Translational control was confirmed to support stoichiometric protein production in multimeric complexes. Comparing the autotrophic to the heterotrophic growth condition revealed growth-dependent regulation of translational efficiency, pointing at translational buffering as a widespread phenomenon shared by acetogens
A new proposal of power series method to solve the Navier-Stokes equations: application contexts and perspectives
No full textIn this work we investigate the possibility of expressing the solution of the Navier-Stokes equations as a power series. Although there have been many studies on this subject, it is still much debated. The existence of such a solution and its uniqueness are debated, especially in the general case of a non-stationary fluid in two or three dimensions. The greater the complexity of the fluid dynamical phenomenon under consideration, the more controversial and therefore uncertain are the deductions about the existence and uniqueness of the solution as a power series. Here, we ask some crucial questions on the matter and try to give an answer from the point of view of applied mathematics and numerical analysis. In particular, by way of example, we construct the Navier-Stokes equations for a compressible, multicomponent and non-stationary fluid from elementary principles of conservation of mass, momentum and energy, and we introduce a bump function model for the presence of different components and/or different phases in the fluid. The bump function is a function of space, time and physical characteristics of the components (and/or phases) such as density. We present a method to calculate it and discuss about its uniqueness. We show that under certain conditions on the bump function, and the forces acting on and in the fluid, the power series solution is unique. We finally discuss the advantages and the limitations of a solution in power series, concluding that, although plagued by limitations, it is a viable way forward even in highly complex case studies
On the asymptotic stability of advection-diffusion equations of mass transport in bubble column bioreactor
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The theory of active agents for simulating dynamical networks and its π-calculus specification.
No full textDetermination of the latent geometry of atorvastatin pharmacokinetics by transfer entropy to identify bottlenecks
Full text linkBackground: In mathematics, a physical network (e.g. biological network, social network, IT network, communication network) is usually represented by a graph. The determination of the metric space (also referred to as latent geometry) of the graph and the disposition of its nodes on it provide important information on the reaction propensity and consequently on the possible presence of bottlenecks in a system of interacting molecules, such as it happens in pharmacokinetics. To determine the latent geometry and the coordinates of nodes, it is necessary to have the dissimilarity or distance matrix of the network, an input that is not always easy to measure in experiments. Results: The main result of this study is the mathematical and computational procedure for determining the distance/dissimilarity matrix between nodes and for identifying the latent network geometry from experimental time series of node concentrations. Specifically, we show how this matrix can be calculated from the transfer entropy between nodes, which is a measure of the flow of information between nodes and thus indirectly of the reaction propensity between them. We implemented a procedure of spectral graph embedding to embed the distance/dissimilarity matrix in flat and curved metric spaces, and consequently to determine the optimal latent geometry of the network. The distances between nodes in the metric space describing the latent geometry can be analyzed to identify bottlenecks in the reaction system. As a case study for this procedure, we consider the pharmacokinetics of atorvastatin, as described by recent studies and experimental time data. Conclusions: The method of determining distances between nodes from temporal measurements of node concentrations through the calculation of transfer entropy makes it possible to incorporate the information of kinetics (inherent in the time series) in the construction of the distance/dissimilarity matrix, and, consequently, in the determination of the network latent geometry, a characterisation of the network itself that is intimately connected to its dynamics, but which has so far been scarcely investigated and taken into account. The results on the case study of the pharmacokinetics of atorvastatin corroborate the usability and reliability of the method within certain limits of the experimental errors on the data
Determining structural parameter identifiability in biological dynamical models by analysing the statistical properties of the likelihood behaviour
No full textIdentifiability is a fundamental precondition for model identification and concerns the possibility to determine unique parameters sets using a certain model structure and the experimental observations for the model input-output. In this article we show how to a priori assess the identifiability of model parameters analysing the properties of the likelihood function. More precisely, a model is identifiable if the likelihood function shows a unique absolute minimum beyond the confidence intervals. The study of the likelihood function cannot avoid accompanying the likelihood estimates with the assessment of the confidence intervals, which reflect the variability in experimental data. We then show the results of this approach in cases of biotechnological relevance where the identifiability has been previously assessed
Sequence modeling tools to decode the biosynthetic diversity of the human microbiome
Full text linkUnderstanding the biosynthetic potential of the human microbiome remains a significant challenge with far-reaching scientific and translational implications. Analyses of human-associated (meta)genomic sequencing data undeniably show that the biosynthetic diversity encoded in these genomes is largely underexplored. A crucial step in studying specialized metabolites involves the sequence-based identification of genes encoding biosynthetic pathways, typically organized into biosynthetic gene clusters (BGCs). In this review, we provide a concise and updated overview of the widening range of computational approaches that have effectively addressed the sequence-based identification of BGCs across both isolated genomes and complex microbial communities. These advancements are set to deepen our understanding of the biosynthetic potential and diversity of microorganisms residing in different human body sites
Current progress on engineering microbial strains and consortia for production of cellulosic butanol through consolidated bioprocessing
Full text linkIn the last decades, fermentative production of n-butanol has regained substantial interest mainly owing to its use as drop-in-fuel. The use of lignocellulose as an alternative to traditional acetone-butanol-ethanol fermentation feedstocks (starchy biomass and molasses) can significantly increase the economic competitiveness of biobutanol over production from non-renewable sources (petroleum). However, the low cost of lignocellulose is offset by its high recalcitrance to biodegradation which generally requires chemical-physical pre-treatment and multiple bioreactor-based processes. The development of consolidated processing (i.e., single-pot fermentation) can dramatically reduce lignocellulose fermentation costs and promote its industrial application. Here, strategies for developing microbial strains and consortia that feature both efficient (hemi)cellulose depolymerization and butanol production will be depicted, that is, rational metabolic engineering of native (hemi)cellulolytic or native butanol-producing or other suitable microorganisms; protoplast fusion of (hemi)cellulolytic and butanol-producing strains; and co-culture of (hemi)cellulolytic and butanol-producing microbes. Irrespective of the fermentation feedstock, biobutanol production is inherently limited by the severe toxicity of this solvent that challenges process economic viability. Hence, an overview of strategies for developing butanol hypertolerant strains will be provided
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