Nanopore analysis of amyloid fibrils formed by lysozyme aggregation.

The measurement of single particle size distributions of amyloid fibrils is crucial for determining mechanisms of growth and toxicity. Nanopore sensing is an attractive solution for this problem since it gives information on aggregates' shapes with relatively high throughput for a single particle technology. In this paper we study the translocation of lysozyme fibrils through quartz glass nanopores. We demonstrate that, under appropriate salt and pH conditions, lysozyme fibrils translocate through bare quartz nanopores without causing significant clogging. This enables us to measure statistics on tens of thousands of translocations of lysozyme fibrils with the same nanopore and track their development over a time course of aggregation spanning 24 h. Analysis of our events shows that the statistics are consistent with a simple bulk conductivity model for the passage of rods with a fixed cross sectional area through a conical glass nanopore.N.A.W.B. acknowledges funding from the EPSRC NanoDTC program and an EPSRC doctoral prize award and U.F.K. acknowledges funding from an ERC starting grant, PassMembrane (261101).This is the final version of the article. It first appeared from RSC via http://dx.doi.org/10.1039/C5AN00530

Hypothetical biomolecular probe based on a genetic switch with tunable symmetry and stability

Background: Genetic switches are ubiquitous in nature, frequently associated with the control of cellular functions and developmental programs. In the realm of synthetic biology, it is of great interest to engineer genetic circuits that can change their mode of operation from monostable to bistable, or even to multistable, based on the experimental fine-tuning of readily accessible parameters. In order to successfully design robust, bistable synthetic circuits to be used as biomolecular probes, or understand modes of operation of such naturally occurring circuits, we must identify parameters that are key in determining their characteristics. Results: Here, we analyze the bistability properties of a general, asymmetric genetic toggle switch based on a chemical-reaction kinetic description. By making appropriate approximations, we are able to reduce the system to two coupled differential equations. Their deterministic stability analysis and stochastic numerical simulations are in excellent agreement. Drawing upon this general framework, we develop a model of an experimentally realized asymmetric bistable genetic switch based on the LacI and TetR repressors. By varying the concentrations of two synthetic inducers, doxycycline and isopropyl ??-D-1-thiogalactopyranoside, we predict that it will be possible to repeatedly fine-tune the mode of operation of this genetic switch from monostable to bistable, as well as the switching rates over many orders of magnitude, in an experimental setting. Furthermore, we find that the shape and size of the bistability region is closely connected with plasmid copy number. Conclusions: Based on our numerical calculations of the LacI-TetR asymmetric bistable switch phase diagram, we propose a generic work-flow for developing and applying biomolecular probes: Their initial state of operation should be specified by controlling inducer concentrations, and dilution due to cellular division would turn the probes into memory devices in which information could be preserved over multiple generations. Additionally, insights from our analysis of the LacI-TetR system suggest that this particular system is readily available to be employed in this kind of probe.clos

ErrorTracer: an algorithm for identifying the origins of inconsistencies in genome-scale metabolic models

Motivation: The number and complexity of genome-scale metabolic models is steadily increasing, empowered by automated model-generation algorithms. The quality control of the models, however, has always remained a significant challenge, the most fundamental being reactions incapable of carrying flux. Numerous automated gap-filling algorithms try to address this problem, but can rarely resolve all of a model’s inconsistencies. The need for fast inconsistency checking algorithms has also been emphasized with the recent community push for automated modelvalidation before model publication. Previously, we wrote a graphical software to allow the modeller to solve the remaining errors manually. Nevertheless, model size and complexity remained a hindrance to efficiently tracking origins of inconsistency. Results: We developed the ErrorTracer algorithm in order to address the shortcomings of existing approaches: ErrorTracer searches for inconsistencies, classifies them and identifies their origins. The algorithm is 2 orders of magnitude faster than current community standard methods, using only seconds even for large-scale models. This allows for interactive exploration in direct combination with model visualization, markedly simplifying the whole error-identification and correction work flow. Availability and implementation: Windows and Linux executables and source code are available under the EPL 2.0. Licence at https://github.com/TheAngryFox/ModelExplorer and https://www.ntnu.edu/almaaslab/downloads. Contact: [email protected] or [email protected]. Supplementary information: Supplementary data are available at Bioinformatics onlin

ModelExplorer - software for visual inspection and inconsistency correction of genome-scale metabolic reconstructions

Abstract Background Genome-scale metabolic network reconstructions are low level chemical representations of biological organisms. These models allow the system-level investigation of metabolic phenotypes using a variety of computational approaches. The link between a metabolic network model and an organisms’ higher-level behaviour is usually found using a constraint-based analysis approach, such as FBA (Flux Balance Analysis). However, the process of model reconstruction rarely proceeds without error. Often, considerable parts of a model cannot carry flux under any condition. This is termed model inconsistency and is caused by faulty topology and/or stoichiometry of the underlying reconstructed network. While there exist several automated gap-filling tools that may solve some of the inconsistencies, much of the work still needs to be carried out manually. The common “linear list” format of writing biochemical reactions makes it difficult to intuit what is at the root of the inconsistent behaviour. Unfortunately, we have frequently observed that model builders do not correct their models past the abilities of automated tools, leaving many widely used models significantly inconsistent. Results We have developed the software ModelExplorer, which main purpose is to fill this gap by providing an intuitive and visual framework that allows the user to explore and correct inconsistencies in genome-scale metabolic models. The software will automatically visualize metabolic networks as graphs with distinct separation and delineation of cellular compartments. ModelExplorer highlights reactions and species that are unable to carry flux (blocked), with several different consistency checking modes available. Our software also allows the automatic identification of neighbours and production pathways of any species or reaction. Additionally, the user may focus on any chosen inconsistent part of the model on its own. This facilitates a rapid and visual identification of reactions and species responsible for model inconsistencies. Finally, ModelExplorer lets the user freely edit, add or delete model elements, allowing straight-forward correction of discovered issues. Conclusion Overall, ModelExplorer is currently the fastest real-time metabolic network visualization program available. It implements several consistency checking algorithms, which in combination with its set of tracking tools, gives an efficient and systematic model-correction process

Additional file 2 of ModelExplorer - software for visual inspection and inconsistency correction of genome-scale metabolic reconstructions

∙ModelExplorer - Windows binary file (ModelExplorer v1.0 executable) ∙ A set of library files - (COIN-OR Clp and Graphviz) ∙arial.ttf - Font file ∙test.xml - Test model (iTO977) ∙ModelExplorer_User_Manual.pdf - User manual that contains information about software installation procedures, usage and licence. ∙LICENSE.txt - A license notice. (ZIP 4801 kb

Additional file 1 of ModelExplorer - software for visual inspection and inconsistency correction of genome-scale metabolic reconstructions

∙ModelExplorer - Linux binary file (ModelExplorer v1.0 executable) ∙modelEpxlorerLibs and extraLibs - Folders with libraries (COIN-OR Clp and Graphviz) ∙install.sh - a Linux installation script ∙arial.ttf - Font file ∙test.xml - Test model (iTO977) ∙ModelExplorer_User_Manual.pdf - User manual that contains information about software installation procedures, usage and licence. ∙LICENSE.txt - A license notice. (ZIP 13,984 kb

ErrorTracer: an algorithm for identifying the origins of inconsistencies in genome-scale metabolic models

Motivation: The number and complexity of genome-scale metabolic models is steadily increasing, empowered by automated model-generation algorithms. The quality control of the models, however, has always remained a significant challenge, the most fundamental being reactions incapable of carrying flux. Numerous automated gap-filling algorithms try to address this problem, but can rarely resolve all of a model’s inconsistencies. The need for fast inconsistency checking algorithms has also been emphasized with the recent community push for automated modelvalidation before model publication. Previously, we wrote a graphical software to allow the modeller to solve the remaining errors manually. Nevertheless, model size and complexity remained a hindrance to efficiently tracking origins of inconsistency. Results: We developed the ErrorTracer algorithm in order to address the shortcomings of existing approaches: ErrorTracer searches for inconsistencies, classifies them and identifies their origins. The algorithm is 2 orders of magnitude faster than current community standard methods, using only seconds even for large-scale models. This allows for interactive exploration in direct combination with model visualization, markedly simplifying the whole error-identification and correction work flow. Availability and implementation: Windows and Linux executables and source code are available under the EPL 2.0. Licence at https://github.com/TheAngryFox/ModelExplorer and https://www.ntnu.edu/almaaslab/downloads. Contact: [email protected] or [email protected]. Supplementary information: Supplementary data are available at Bioinformatics onlinepublishedVersion© The Author(s) 2019. Published by Oxford University Press. 1 This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact [email protected]

Containing pandemics through targeted testing of households

While invasive social distancing measures have proven efficient to control the spread of pandemics in the absence of a vaccine, they carry vast societal costs. Guided by the finding that large households function as hubs for the propagation of COVID-19, we developed a data-driven individual-based epidemiological network-model to assess the intervention efficiency of targeted testing of larger households. For an outbreak with reproductive number R = 1.5, we find that weekly testing of just the 15% largest households is capable of forcing R below unity. For the case of R = 1.2, our results suggest that the same testing regime with the largest 20% of households in an urban area is as effective as imposing strict lockdown measures and will curb the outbreak in a few weeks. Pooled household testing appears to be a powerful alternative to more invasive measures as a localized early response to contain epidemic outbreaks.</jats:p

Additional file 1 of Hypothetical biomolecular probe based on a genetic switch with tunable symmetry and stability

Equation system describing asymmetric toggle-switch. (PDF 106 kb