Zoology of a non-local cross-diffusion model for two species

We study a non-local two species cross-interaction model with cross-diffusion. We propose a positivity preserving finite volume scheme based on the numerical method introduced in Ref. [15] and explore this new model numerically in terms of its long-time behaviours. Using the so gained insights, we compute analytical stationary states and travelling pulse solutions for a particular model in the case of attractive-attractive/attractive-repulsive cross-interactions. We show that, as the strength of the cross-diffusivity decreases, there is a transition from adjacent solutions to completely segregated densities, and we compute the threshold analytically for attractive-repulsive cross-interactions. Other bifurcating stationary states with various coexistence components of the support are analysed in the attractive-attractive case. We find a strong agreement between the numerically and the analytically computed steady states in these particular cases, whose main qualitative features are also present for more general potentials

Randomly Evolving Idiotypic Networks: Structural Properties and Architecture

We consider a minimalistic dynamic model of the idiotypic network of B-lymphocytes. A network node represents a population of B-lymphocytes of the same specificity (idiotype), which is encoded by a bitstring. The links of the network connect nodes with complementary and nearly complementary bitstrings, allowing for a few mismatches. A node is occupied if a lymphocyte clone of the corresponding idiotype exists, otherwise it is empty. There is a continuous influx of new B-lymphocytes of random idiotype from the bone marrow. B-lymphocytes are stimulated by cross-linking their receptors with complementary structures. If there are too many complementary structures, steric hindrance prevents cross-linking. Stimulated cells proliferate and secrete antibodies of the same idiotype as their receptors, unstimulated lymphocytes die. Depending on few parameters, the autonomous system evolves randomly towards patterns of highly organized architecture, where the nodes can be classified into groups according to their statistical properties. We observe and describe analytically the building principles of these patterns, which allow to calculate number and size of the node groups and the number of links between them. The architecture of all patterns observed so far in simulations can be explained this way. A tool for real-time pattern identification is proposed.Comment: 19 pages, 15 figures, 4 table

A major population of mucosal memory CD4⁺ T cells, coexpressing IL-18Rα and DR3, display innate lymphocyte functionality

Mucosal tissues contain large numbers of memory CD4(+) T cells that, through T-cell receptor-dependent interactions with antigen-presenting cells, are believed to have a key role in barrier defense and maintenance of tissue integrity. Here we identify a major subset of memory CD4(+) T cells at barrier surfaces that coexpress interleukin-18 receptor alpha (IL-18Rα) and death receptor-3 (DR3), and display innate lymphocyte functionality. The cytokines IL-15 or the DR3 ligand tumor necrosis factor (TNF)-like cytokine 1A (TL1a) induced memory IL-18Rα(+)DR3(+)CD4(+) T cells to produce interferon-γ, TNF-α, IL-6, IL-5, IL-13, granulocyte-macrophage colony-stimulating factor (GM-CSF), and IL-22 in the presence of IL-12/IL-18. TL1a synergized with IL-15 to enhance this response, while suppressing IL-15-induced IL-10 production. TL1a- and IL-15-mediated cytokine induction required the presence of IL-18, whereas induction of IL-5, IL-13, GM-CSF, and IL-22 was IL-12 independent. IL-18Rα(+)DR3(+)CD4(+) T cells with similar functionality were present in human skin, nasal polyps, and, in particular, the intestine, where in chronic inflammation they localized with IL-18-producing cells in lymphoid aggregates. Collectively, these results suggest that human memory IL-18Rα(+)DR3(+) CD4(+) T cells may contribute to antigen-independent innate responses at barrier surfaces.Mucosal Immunology advance online publication, 1 October 2014; doi:10.1038/mi.2014.87

On a novel gradient flow structure for the aggregation equation

The aggregation equation arises naturally in kinetic theory in the study of granular media, and its interpretation as a 2-Wasserstein gradient flow for the nonlocal interaction energy is well-known. Starting from the spatially homogeneous inelastic Boltzmann equation, a formal Taylor expansion reveals a link between this equation and the aggregation equation with an appropriately chosen interaction potential. Inspired by this formal link and the fact that the associated aggregation equation also dissipates the kinetic energy, we present a novel way of interpreting the aggregation equation as a gradient flow, in the sense of curves of maximal slope, of the kinetic energy, rather than the usual interaction energy, with respect to an appropriately constructed transportation metric on the space of probability measures.Comment: 37 pages. Restructured version. Comments welcom

Convergence of a Fully Discrete and Energy-Dissipating Finite-Volume Scheme for Aggregation-Diffusion Equations

We study an implicit finite-volume scheme for non-linear, non-local aggregation-diffusion equations which exhibit a gradient-flow structure, recently introduced by Bailo, Carrillo, and Hu (2019). Crucially, this scheme keeps the dissipation property of an associated fully discrete energy, and does so unconditionally with respect to the time step. Our main contribution in this work is to show the convergence of the method under suitable assumptions on the diffusion functions and potentials involved

Linking discrete and continuous models of cell birth and migration

Self-organisation of individuals within large collectives occurs throughout biology. Mathematical models can help elucidate the individual-level mechanisms behind these dynamics, but analytical tractability often comes at the cost of biological intuition. Discrete models provide straightforward interpretations by tracking each individual yet can be computationally expensive. Alternatively, continuous models supply a large-scale perspective by representing the "effective" dynamics of infinite agents, but their results are often difficult to translate into experimentally relevant insights. We address this challenge by quantitatively linking spatio-temporal dynamics of continuous models and individual-based data in settings with biologically realistic, time-varying cell numbers. Specifically, we introduce and fit scaling parameters in continuous models to account for discrepancies that can arise from low cell numbers and localised interactions. We illustrate our approach on an example motivated by zebrafish-skin pattern formation, in which we create a continuous framework describing the movement and proliferation of a single cell population by upscaling rules from a discrete model. Our resulting continuous models accurately depict ensemble average agent-based solutions when migration or proliferation act alone. Interestingly, the same parameters are not optimal when both processes act simultaneously, highlighting a rich difference in how combining migration and proliferation affects discrete and continuous dynamics.Comment: 25 pages, 11 figures in main manuscript. 24 pages, 14 figures in supplementary informatio

A Rapid and Sensitive Method for Measuring NAcetylglucosaminidase Activity in Cultured Cells

A rapid and sensitive method to quantitatively assess N-acetylglucosaminidase (NAG) activity in cultured cells is highly desirable for both basic research and clinical studies. NAG activity is deficient in cells from patients with Mucopolysaccharidosis type IIIB (MPS IIIB) due to mutations in NAGLU, the gene that encodes NAG. Currently available techniques for measuring NAG activity in patient-derived cell lines include chromogenic and fluorogenic assays and provide a biochemical method for the diagnosis of MPS IIIB. However, standard protocols require large amounts of cells, cell disruption by sonication or freeze-thawing, and normalization to the cellular protein content, resulting in an error-prone procedure that is material- and time-consuming and that produces highly variable results. Here we report a new procedure for measuring NAG activity in cultured cells. This procedure is based on the use of the fluorogenic NAG substrate, 4- Methylumbelliferyl-2-acetamido-2-deoxy-alpha-D-glucopyranoside (MUG), in a one-step cell assay that does not require cell disruption or post-assay normalization and that employs a low number of cells in 96-well plate format. We show that the NAG one-step cell assay greatly discriminates between wild-type and MPS IIIB patient-derived fibroblasts, thus providing a rapid method for the detection of deficiencies in NAG activity. We also show that the assay is sensitive to changes in NAG activity due to increases in NAGLU expression achieved by either overexpressing the transcription factor EB (TFEB), a master regulator of lysosomal function, or by inducing TFEB activation chemically. Because of its small format, rapidity, sensitivity and reproducibility, the NAG one-step cell assay is suitable for multiple procedures, including the high-throughput screening of chemical libraries to identify modulators of NAG expression, folding and activity, and the investigation of candidate molecules and constructs for applications in enzyme replacement therapy, gene therapy, and combination therapies

Metal-Organic Frameworks and Metal-Organic Cages – A Perspective

© 2020 Wiley-VCH GmbH The fields of metal-organic cages (MOCs) and metal-organic frameworks (MOFs) are both highly topical and continue to develop at a rapid pace. Despite clear synergies between the two fields, overlap is rarely observed. This article discusses the peculiarities and similarities of MOCs and MOFs in terms of synthetic strategies and approaches to system characterisation. The stability of both classes of material is compared, particularly in relation to their applications in guest storage and catalysis. Lastly, suggestions are made for opportunities for each field to learn and develop in partnership with the other

Randomly Evolving Idiotypic Networks: Modular Mean Field Theory

We develop a modular mean field theory for a minimalistic model of the idiotypic network. The model comprises the random influx of new idiotypes and a deterministic selection. It describes the evolution of the idiotypic network towards complex modular architectures, the building principles of which are known. The nodes of the network can be classified into groups of nodes, the modules, which share statistical properties. Each node experiences only the mean influence of the groups to which it is linked. Given the size of the groups and linking between them the statistical properties such as mean occupation, mean life time, and mean number of occupied neighbors are calculated for a variety of patterns and compared with simulations. For a pattern which consists of pairs of occupied nodes correlations are taken into account.Comment: 14 pages, 8 figures, 4 table