Noise Expands the Response Range of the Bacillus subtilis Competence Circuit

Gene regulatory circuits must contend with intrinsic noise that arises due to finite numbers of proteins. While some circuits act to reduce this noise, others appear to exploit it. A striking example is the competence circuit in Bacillus subtilis, which exhibits much larger noise in the duration of its competence events than a synthetically constructed analog that performs the same function. Here, using stochastic modeling and fluorescence microscopy, we show that this larger noise allows cells to exit terminal phenotypic states, which expands the range of stress levels to which cells are responsive and leads to phenotypic heterogeneity at the population level. This is an important example of how noise confers a functional benefit in a genetic decision-making circuit

Community standards for open cell migration data

Cell migration research has become a high-content field. However, the quantitative information encapsulated in these complex and high-dimensional datasets is not fully exploited owing to the diversity of experimental protocols and non-standardized output formats. In addition, typically the datasets are not open for reuse. Making the data open and Findable, Accessible, Interoperable, and Reusable (FAIR) will enable meta-analysis, data integration, and data mining. Standardized data formats and controlled vocabularies are essential for building a suitable infrastructure for that purpose but are not available in the cell migration domain. We here present standardization efforts by the Cell Migration Standardisation Organisation (CMSO), an open community-driven organization to facilitate the development of standards for cell migration data. This work will foster the development of improved algorithms and tools and enable secondary analysis of public datasets, ultimately unlocking new knowledge of the complex biological process of cell migration

Computation of Steady-State Probability Distributions in Stochastic Models of Cellular Networks

Cellular processes are “noisy”. In each cell, concentrations of molecules are subject to random fluctuations due to the small numbers of these molecules and to environmental perturbations. While noise varies with time, it is often measured at steady state, for example by flow cytometry. When interrogating aspects of a cellular network by such steady-state measurements of network components, a key need is to develop efficient methods to simulate and compute these distributions. We describe innovations in stochastic modeling coupled with approaches to this computational challenge: first, an approach to modeling intrinsic noise via solution of the chemical master equation, and second, a convolution technique to account for contributions of extrinsic noise. We show how these techniques can be combined in a streamlined procedure for evaluation of different sources of variability in a biochemical network. Evaluation and illustrations are given in analysis of two well-characterized synthetic gene circuits, as well as a signaling network underlying the mammalian cell cycle entry

Quantitative Analysis of Nuclear Lamins Imaged by Super-Resolution Light Microscopy

The nuclear lamina consists of a dense fibrous meshwork of nuclear lamins, Type V intermediate filaments, and is ~14 nm thick according to recent cryo-electron tomography studies. Recent advances in light microscopy have extended the resolution to a scale allowing for the fine structure of the lamina to be imaged in the context of the whole nucleus. We review quantitative approaches to analyze the imaging data of the nuclear lamina as acquired by structured illumination microscopy (SIM) and single molecule localization microscopy (SMLM), as well as the requisite cell preparation techniques. In particular, we discuss the application of steerable filters and graph-based methods to segment the structure of the four mammalian lamin isoforms (A, C, B1, and B2) and extract quantitative information

Computational analyses reveal spatial relationships between nuclear pore complexes and specific lamins

Nuclear lamin isoforms form fibrous meshworks associated with nuclear pore complexes (NPCs). Using datasets prepared from subpixel and segmentation analyses of 3D-structured illumination microscopy images of WT and lamin isoform knockout mouse embryo fibroblasts, we determined with high precision the spatial association of NPCs with specific lamin isoform fibers. These relationships are retained in the enlarged lamin meshworks of Lmna-/- and Lmnb1-/- fibroblast nuclei. Cryo-ET observations reveal that the lamin filaments composing the fibers contact the nucleoplasmic ring of NPCs. Knockdown of the ring-associated nucleoporin ELYS induces NPC clusters that exclude lamin A/C fibers but include LB1 and LB2 fibers. Knockdown of the nucleoporin TPR or NUP153 alters the arrangement of lamin fibers and NPCs. Evidence that the number of NPCs is regulated by specific lamin isoforms is presented. Overall the results demonstrate that lamin isoforms and nucleoporins act together to maintain the normal organization of lamin meshworks and NPCs within the nuclear envelope

silx-kit/hdf5plugin: 4.2.0: 12/09/2023

What's Changed Updated libraries: c-blosc (v1.12.5), c-blosc2 (v2.10.2) (PR #273) Updated filter H5Z-ZFP (v1.1.1) (PR #273) Updated build dependencies: Added wheel>=0.34.0 requirement (PR #272) Removed distutils usage (PR #276) Updated documentation (PR #271, #278) Fixed Continuous integration (PR #275) Debian packaging (PR #277): Added Debian 12 Removed Debian 10 Full Changelog: https://github.com/silx-kit/hdf5plugin/compare/v4.1.3...v4.2.

Stochastic oscillations persist outside the deterministic oscillatory regime.

<p>The deterministic oscillatory regime is defined by for the induction rate <i>α</i>_<i>k</i>. (A) At low induction rate , where the deterministic model predicts excitable dynamics, the stochastic dynamics are oscillatory. The oscillations arise from repeated noise-induced excitations. (B) At high induction rate , where the deterministic model predicts mono-stable dynamics, the stochastic dynamics are also oscillatory. The oscillations here arise because noise prevents damping to the mono-stable state (see the deterministic curves in the right panels). The effect is much stronger for the native circuit (notice that the left panel is 15 times outside the deterministically oscillatory regime) because, unlike in the SynEx circuit, one of the species, ComS, is at low copy number and therefore subject to significant intrinsic noise. The deterministic model is given in Eqs <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004793#pcbi.1004793.e021" target="_blank">6</a> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004793#pcbi.1004793.e022" target="_blank">7</a>, while the stochastic model is given in Eqs <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004793#pcbi.1004793.e016" target="_blank">1</a>–<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004793#pcbi.1004793.e020" target="_blank">5</a>. In A, the deterministic initial conditions are ComK molecules and ComS molecules (native), and ComK molecules and MecA molecules (SynEx). In B, the deterministic initial conditions are ComK molecules and ComS molecules (native), and ComK molecules and MecA molecules (SynEx). In the excitable regime (A), the initial conditions are chosen to demonstrate the single, transient excitation.</p

Stochastic modeling of <i>B. subtilis</i> competence.

<p>(A) The deterministic model of each circuit (see Eqs <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004793#pcbi.1004793.e021" target="_blank">6</a> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004793#pcbi.1004793.e022" target="_blank">7</a>) exhibits three dynamic regimes (excitable, oscillatory, and mono-stable), depending on the ComK induction rate <i>α</i>_<i>k</i>, which models stress level. (B) The stochastic model (see Eqs <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004793#pcbi.1004793.e016" target="_blank">1</a>–<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004793#pcbi.1004793.e020" target="_blank">5</a>) reveals the ensuing distribution of ComK levels in each of the three dynamic regimes (excitable, oscillatory, and mono-stable). The fraction of the distribution in the responsive state <i>f</i> (determined by the inflection points, see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004793#sec007" target="_blank">Materials and Methods</a>) is shaded. (C) Whereas the deterministic model exhibits sharp transitions between the dynamic regimes (dashed lines), the stochastic model exhibits a continuous dependence of <i>f</i> on induction rate. We see that for both circuits, stochasticity extends the viable response range (0 < <i>f</i> < 1) beyond the transitions predicted by the deterministic model, in both directions, by the factors given above the arrows (see <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004793#sec007" target="_blank">Materials and Methods</a>). Parameters are as in [<a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004793#pcbi.1004793.ref016" target="_blank">16</a>] and are given in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1004793#pcbi.1004793.s001" target="_blank">S1 Text</a>. In A and B, from left to right, the values of the control parameter are <i>α</i>_<i>k</i> = {0.072, 1.15, 36}/hour (native) and <i>α</i>_<i>k</i> = {0.036, 1.8, 36}/hour (SynEx). In A, from left to right, the initial conditions are ComK molecules and ComS molecules (native), and ComK molecules and MecA molecules (SynEx); in the excitable regime (left), the initial conditions are chosen to demonstrate the single, transient excitation.</p

Architectures and model parameters of the native and SynEx circuits.

<p>The top row summarizes the regulatory interactions, while the bottom row depicts the model details. (A) In the native circuit, ComK is produced with the induction rate <i>α</i>_<i>k</i> and activates its own expression with Hill function parameters <i>β</i>_<i>k</i>, <i>k</i>_<i>k</i>, and <i>h</i>. ComS is expressed at the basal rate <i>α</i>_<i>s</i> and is repressed by ComK with Hill function parameters <i>β</i>_<i>s</i>, <i>k</i>_<i>s</i>, and <i>p</i>. ComK and ComS are degraded at rates <i>λ</i>_<i>k</i> and <i>λ</i>_<i>s</i>, respectively, and, additionally, both compete for binding to the degradation enzyme MecA. MecA degrades ComK and ComS with maximal rates <i>δ</i>_<i>k</i> and <i>δ</i>_<i>s</i>, respectively, and with Michaelis-Menten constants Γ_<i>k</i> and Γ_<i>s</i>, respectively. (B) In the SynEx circuit, ComK is produced with the induction rate <i>α</i>_<i>k</i> and activates its own expression with Hill function parameters <i>β</i>_<i>k</i>, <i>k</i>_<i>k</i>, and <i>h</i>. MecA is expressed at the basal rate <i>α</i>_<i>m</i> and is activated by ComK with Hill function parameters <i>β</i>_<i>m</i>, <i>k</i>_<i>m</i>, and <i>p</i>. ComK and MecA are degraded at rates <i>λ</i>_<i>k</i> and <i>λ</i>_<i>m</i>, respectively, and MecA enzymatically degrades ComK with rate <i>δ</i>.</p