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Recent progresses in intravital imaging have enabled highly-resolved measurements of periarteriolar oxygen gradients (POGs) within the brain parenchyma. POGs are increasingly used as proxies to estimate the local baseline oxygen consumption, which is a hallmark of cell activity. However, the oxygen profile around a given arteriole arises from an interplay between oxygen consumption and delivery, not only by this arteriole but also by distant capillaries. Integrating such interactions across scales while accounting for the complex architecture of the microvascular network remains a challenge from a modelling perspective. This limits our ability to interpret the experimental oxygen maps and constitutes a key bottleneck toward the inverse determination of metabolic rates of oxygen. We revisit the problem of parenchymal oxygen transport and metabolism and introduce a simple, conservative, accurate and scalable direct numerical method going beyond canonical Krogh-type models and their associated geometrical simplifications. We focus on a two-dimensional formulation, and introduce the concepts needed to combine an operator-splitting and a Green's function approach. Oxygen concentration is decomposed into a slowly-varying contribution, discretized by Finite Volumes over a coarse cartesian grid, and a rapidly-varying contribution, approximated analytically in grid-cells surrounding each vessel. Starting with simple test cases, we thoroughly analyze the resulting errors by comparison with highly-resolved simulations of the original transport problem, showing considerable improvement of the computational-cost/accuracy balance compared to previous work. We then demonstrate the model ability to flexibly generate synthetic data reproducing the spatial dynamics of oxygen in the brain parenchyma, with sub-grid resolution. Based on these synthetic data, we show that capillaries distant from the arteriole cannot be overlooked when interpreting POGs, thus reconciling recent measurements of POGs across cortical layers with the fundamental idea that variations of vascular density within the depth of the cortex may reveal underlying differences in neuronal organization and metabolic load., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Pastor-Alonso et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Polydimethylsiloxane (PDMS) microfluidic systems have been instrumental in better understanding couplings between physical mechanisms and bacterial biofilm processes, such as hydrodynamic effects. However, precise control of the growth conditions, for example, the initial distribution of cells on the substrate or the boundary conditions in a flow system, has remained challenging. Furthermore, undesired bacterial colonization in crucial parts of the systems, in particular, in mixing zones or tubing, is an important factor that strongly limits the duration of the experiments and, therefore, impedes our ability to study the biophysics of biofilm evolving over long periods of time, as found in the environment, in engineering, or in medicine. Here, we develop a new approach that uses ultraviolet-C (UV-C) light-emitting diodes (LEDs) to confine bacterial development to specific zones of interest in the flow channels. The LEDs are integrated into a 3D printed light guide that is positioned upon the chip and used to irradiate germicidal UV-C directly through the PDMS. We first demonstrate that this system is successful in controlling undesired growth of Pseudomonas aeruginosa biofilm in inlet and outlet mixing zones during 48 h. We further illustrate how this can be used to define the initial distribution of bacteria to perturb already formed biofilms during an experiment and to control colonization for seven days-and possibly longer periods of time-therefore opening the way toward long-term biofilm experiments in microfluidic devices. Our approach is easily generalizable to existing devices at low cost and may, thus, become a standard in biofilm experiments in PDMS microfluidics., Competing Interests: The authors have no conflicts to disclose., (© 2023 Author(s).)

Tit-for-tat is a familiar principle from animal behavior: individuals respond in kind to being helped or harmed by others. Remarkably some bacteria appear to display tit-for-tat behavior, but how this evolved is not understood. Here we combine evolutionary game theory with agent-based modelling of bacterial tit-for-tat, whereby cells stab rivals with poisoned needles (the type VI secretion system) after being stabbed themselves. Our modelling shows tit-for-tat retaliation is a surprisingly poor evolutionary strategy, because tit-for-tat cells lack the first-strike advantage of preemptive attackers. However, if cells retaliate strongly and fire back multiple times, we find that reciprocation is highly effective. We test our predictions by competing Pseudomonas aeruginosa (a tit-for-tat species) with Vibrio cholerae (random-firing), revealing that P. aeruginosa does indeed fire multiple times per incoming attack. Our work suggests bacterial competition has led to a particular form of reciprocation, where the principle is that of strong retaliation, or 'tits-for-tat'.

Despite the key role of the capillaries in neurovascular function, a thorough characterization of cerebral capillary network properties is currently lacking. Here, we define a range of metrics (geometrical, topological, flow, mass transfer, and robustness) for quantification of structural differences between brain areas, organs, species, or patient populations and, in parallel, digitally generate synthetic networks that replicate the key organizational features of anatomical networks (isotropy, connectedness, space-filling nature, convexity of tissue domains, characteristic size). To reach these objectives, we first construct a database of the defined metrics for healthy capillary networks obtained from imaging of mouse and human brains. Results show that anatomical networks are topologically equivalent between the two species and that geometrical metrics only differ in scaling. Based on these results, we then devise a method which employs constrained Voronoi diagrams to generate 3D model synthetic cerebral capillary networks that are locally randomized but homogeneous at the network-scale. With appropriate choice of scaling, these networks have equivalent properties to the anatomical data, demonstrated by comparison of the defined metrics. The ability to synthetically replicate cerebral capillary networks opens a broad range of applications, ranging from systematic computational studies of structure-function relationships in healthy capillary networks to detailed analysis of pathological structural degeneration, or even to the development of templates for fabrication of 3D biomimetic vascular networks embedded in tissue-engineered constructs.

Bacteria commonly live in dense and genetically diverse communities associated with surfaces. In these communities, competition for resources and space is intense, and yet we understand little of how this affects the spread of antibiotic-resistant strains. Here, we study interactions between antibiotic-resistant and susceptible strains using in vitro competition experiments in the opportunistic pathogen Pseudomonas aeruginosa and in silico simulations. Selection for intracellular resistance to streptomycin is very strong in colonies, such that resistance is favoured at very low antibiotic doses. In contrast, selection for extracellular resistance to carbenicillin is weak in colonies, and high doses of antibiotic are required to select for resistance. Manipulating the density and spatial structure of colonies reveals that this difference is partly explained by the fact that the local degradation of carbenicillin by β-lactamase-secreting cells protects neighbouring sensitive cells from carbenicillin. In addition, we discover a second unexpected effect: the inducible elongation of cells in response to carbenicillin allows sensitive cells to better compete for the rapidly growing colony edge. These combined effects mean that antibiotic treatment can select against antibiotic-resistant strains, raising the possibility of treatment regimes that suppress sensitive strains while limiting the rise of antibiotic resistance. We argue that the detailed study of bacterial interactions will be fundamental to understanding and overcoming antibiotic resistance.

Aging or cerebral diseases may induce architectural modifications in human brain microvascular networks, such as capillary rarefaction. Such modifications limit blood and oxygen supply to the cortex, possibly resulting in energy failure and neuronal death. Modelling is key in understanding how these architectural modifications affect blood flow and mass transfers in such complex networks. However, the huge number of vessels in the human brain-tens of billions-prevents any modelling approach with an explicit architectural representation down to the scale of the capillaries. Here, we introduce a hybrid approach to model blood flow at larger scale in the brain microcirculation, based on its multiscale architecture. The capillary bed, which is a space-filling network, is treated as a porous medium and modelled using a homogenized continuum approach. The larger arteriolar and venular trees, which cannot be homogenized because of their fractal-like nature, are treated as a network of interconnected tubes with a detailed representation of their spatial organization. The main contribution of this work is to devise a proper coupling model at the interface between these two components. This model is based on analytical approximations of the pressure field that capture the strong pressure gradients building up in the capillaries connected to arterioles or venules. We evaluate the accuracy of this model for both very simple architectures with one arteriole and/or one venule and for more complex ones, with anatomically realistic tree-like vessels displaying a large number of coupling sites. We show that the hybrid model is very accurate in describing blood flow at large scales and further yields a significant computational gain by comparison with a classical network approach. It is therefore an important step towards large scale simulations of cerebral blood flow and lays the groundwork for introducing additional levels of complexity in the future.

The clearest phenotypic characteristic of microbial cells is their shape, but we do not understand how cell shape affects the dense communities, known as biofilms, where many microbes live. Here, we use individual-based modeling to systematically vary cell shape and study its impact in simulated communities. We compete cells with different cell morphologies under a range of conditions and ask how shape affects the patterning and evolutionary fitness of cells within a community. Our models predict that cell shape will strongly influence the fate of a cell lineage: we describe a mechanism through which coccal (round) cells rise to the upper surface of a community, leading to a strong spatial structuring that can be critical for fitness. We test our predictions experimentally using strains of Escherichia coli that grow at a similar rate but differ in cell shape due to single amino acid changes in the actin homolog MreB. As predicted by our model, cell types strongly sort by shape, with round cells at the top of the colony and rod cells dominating the basal surface and edges. Our work suggests that cell morphology has a strong impact within microbial communities and may offer new ways to engineer the structure of synthetic communities., Competing Interests: The authors declare no conflict of interest.

Chaste - Cancer, Heart And Soft Tissue Environment - is an open source C++ library for the computational simulation of mathematical models developed for physiology and biology. Code development has been driven by two initial applications: cardiac electrophysiology and cancer development. A large number of cardiac electrophysiology studies have been enabled and performed, including high-performance computational investigations of defibrillation on realistic human cardiac geometries. New models for the initiation and growth of tumours have been developed. In particular, cell-based simulations have provided novel insight into the role of stem cells in the colorectal crypt. Chaste is constantly evolving and is now being applied to a far wider range of problems. The code provides modules for handling common scientific computing components, such as meshes and solvers for ordinary and partial differential equations (ODEs/PDEs). Re-use of these components avoids the need for researchers to 're-invent the wheel' with each new project, accelerating the rate of progress in new applications. Chaste is developed using industrially-derived techniques, in particular test-driven development, to ensure code quality, re-use and reliability. In this article we provide examples that illustrate the types of problems Chaste can be used to solve, which can be run on a desktop computer. We highlight some scientific studies that have used or are using Chaste, and the insights they have provided. The source code, both for specific releases and the development version, is available to download under an open source Berkeley Software Distribution (BSD) licence at http://www.cs.ox.ac.uk/chaste, together with details of a mailing list and links to documentation and tutorials.