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.

BackgroundGenome-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.ResultsWe 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.ConclusionOverall, 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.Electronic supplementary materialThe online version of this article (10.1186/s12859-019-2615-x) contains supplementary material, which is available to authorized users.

Following publication of the original article [1], it was noted that due to a typesetting error the figure legends were paired incorrectly.The caption of Fig. 1 belongs to Fig. 2. And the caption of Fig. 2 belongs to Fig. 1.The correct figures and captions have been included in this correction, and the original article has been corrected.

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 large households. For an outbreak with reproductive numbers R between 1.1 and 2, our results suggest that the intervention effect of weekly pooled testing of the 10% largest households in an urban area is on par with the effect of imposing strict lockdown measures. By testing no more than 20% of households every 4 days, the model predicts that one can reduce R from 1.6 to below unity over a few weeks, lowering the prevalence by 75%. Pooled household testing appears to be a powerful alternative to more invasive measures as a localized early response to contain epidemic outbreaks.

BackgroundGenetic 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.ResultsHere, 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 β-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.ConclusionsBased 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.Electronic supplementary materialThe online version of this article (doi:10.1186/s12918-016-0279-y) contains supplementary material, which is available to authorized users.