Radial and spiral stream formation in Proteus mirabilis colonies

Multiscale phenomena and patterns in biological systems: special issue in honour of Hans Othmer

This special issue on "Multiscale phenomena and patterns in biological systems" is an homage to the seminal contributions of Hans Othmer. He has remained at the forefront of multiscale modelling and pattern formation in biology for over half a century, developing models for molecular signalling networks, the mechanics of cellular movements, the interactions between multiple cells and their contributions to tissue patterning and dynamics. The contributions in this special issue follow Hans' legacy in using advanced mathematics to understand complex biological processes.

Spatial pattern formation in reaction-diffusion models: a computational approach

Reaction-diffusion equations have been widely used to describe biological pattern formation. Nonuniform steady states of reaction-diffusion models correspond to stationary spatial patterns supported by these models. Frequently these steady states are not unique and correspond to various spatial patterns observed in biology. Traditionally, time-marching methods or steady state solvers based on Newton's method were used to compute such solutions. However, the solutions that these methods converge to highly depend on the initial conditions or guesses. In this paper, we present a systematic method to compute multiple nonuniform steady states for reaction-diffusion models and determine their dependence on model parameters. The method is based on homotopy continuation techniques and involves mesh refinement, which significantly reduces computational cost. The method generates one-parameter steady state bifurcation diagrams that may contain multiple unconnected components, as well as two-parameter solution maps that divide the parameter space into different regions according to the number of steady states. We applied the method to two classic reaction-diffusion models and compared our results with available theoretical analysis in the literature. The first is the Schnakenberg model which has been used to describe biological pattern formation due to diffusion-driven instability. The second is the Gray-Scott model which was proposed in the 1980s to describe autocatalytic glycolysis reactions. In each case, the method uncovers many, if not all, nonuniform steady states and their stabilities.

Considerations for Modeling Proteus mirabilis Swarming

In this chapter we provide some initial guidance to experimentalists on how they might go about creating mathematical representations of their systems under study. Because the interests and goals of different researchers can differ, we try to provide broad instruction on the creation and use of mathematical models. We provide a brief overview of some modeling that has been done with Proteus mirabilis colonies, and discuss the goals of modeling. We suggest ways that collaborative teams may communicate with one another more effectively, and how they can build more confidence in their model results.

Switches induced by quorum sensing in a model of enzyme-loaded microparticles

Quorum sensing refers to the ability of bacteria and other single-celled organisms to respond to changes in cell density or number with population-wide changes in behaviour. Here, simulations were performed to investigate quorum sensing in groups of diffusively coupled enzyme microparticles using a well-characterized autocatalytic reaction which raises the pH of the medium: hydrolysis of urea by urease. The enzyme urease is found in both plants and microorganisms, and has been widely exploited in engineering processes. We demonstrate how increases in group size can be used to achieve a sigmoidal switch in pH at high enzyme loading, oscillations in pH at intermediate enzyme loading and a bistable, hysteretic switch at low enzyme loading. Thus, quorum sensing can be exploited to obtain different types of response in the same system, depending on the enzyme concentration. The implications for microorganisms in colonies are discussed, and the results could help in the design of synthetic quorum sensing for biotechnology applications such as drug delivery.

Oral immunisation with Taishan Pinus massoniana pollen polysaccharide adjuvant with recombinant Lactococcus lactis-expressing Proteus mirabilis ompA confers optimal protection in mice

BACKGROUND

Proteus mirabilis poses a critical burden on the breeding industry, but no efficient vaccine is available for animals.

METHOD

A recombinant Lactococcus lactis expressing the ompA of P. mirabilis was used to develop a vaccine. The mucosal and systemic immune responses of the recombinant vaccine were evaluated in mice after oral immunisation. The inhibition on P. mirabilis colonisation of vaccines was also determined. Moreover, Taishan Pinus massoniana pollen polysaccharides (TPPPS) were used as adjuvants to examine the immunomodulatory effects.

RESULTS

The pure recombinant L. lactis vaccine significantly induced the production of specific IgA and IgG, IL-2, IL-4, IFN-γ, and T lymphocyte proliferation, and the immunised mice exhibited significant resistance to P. mirabilis colonisation. Notably, the TPPPS adjuvant vaccines induced higher levels of immune responses than the pure L. lactis.

CONCLUSIONS

The L. lactis as a vaccine vehicle combined with TPPPS adjuvant provides a feasible method for preventing P. mirabilis infection.

Chirality in microbial biofilms is mediated by close interactions between the cell surface and the substratum

From microbial biofilms to human migrations, spatial competition is central to the evolutionary history of many species. The boundary between expanding populations is the focal point of competition for space and resources and is of particular interest in ecology. For all Escherichia coli strains studied here, these boundaries move in a counterclockwise direction even when the competing strains have the same fitness. We find that chiral growth of bacterial colonies is strongly suppressed by the expression of extracellular features such as adhesive structures and pili. Experiments with other microbial species show that chiral growth is found in other bacteria and exclude cell wall biosynthesis and anisotropic shape as the primary causes of chirality. Instead, intimate contact with the substratum is necessary for chirality. Our results demonstrate that through a handful of surface molecules cells can fundamentally reorganize their migration patterns, which might affect intra- and interspecific competitions through colony morphology or other mechanisms.

Symmetry and reduction in collectives: low-dimensional cyclic pursuit

We investigate low-dimensional examples of cyclic pursuit in a collective, wherein each agent employs a constant bearing (CB) steering law relative to exactly one other agent. For the case of three agents in the plane, we characterize relative equilibria and pure shape equilibria of associated closed-loop dynamics. Re-scaling time yields a reduction of phase space to two dimensions and effective tools for stability analysis. Study of bifurcation of a family of collinear equilibria dependent on a single CB control parameter reveals the presence of a rich collection of trajectories that are periodic in shape and undergo precession in physical space. For collectives in three dimensions, with an appropriate notion of CB pursuit strategy and corresponding steering law, the two-agent case proves to be explicitly integrable. These results suggest control schemes for small teams of mobile robotic agents engaged in area coverage tasks such as search and rescue, and raise interesting possibilities for behaviour in biological contexts.

Proteus mirabilis and Urinary Tract Infections

Proteus mirabilis is a Gram-negative bacterium and is well known for its ability to robustly swarm across surfaces in a striking bulls'-eye pattern. Clinically, this organism is most frequently a pathogen of the urinary tract, particularly in patients undergoing long-term catheterization. This review covers P. mirabilis with a focus on urinary tract infections (UTI), including disease models, vaccine development efforts, and clinical perspectives. Flagella-mediated motility, both swimming and swarming, is a central facet of this organism. The regulation of this complex process and its contribution to virulence is discussed, along with the type VI-secretion system-dependent intra-strain competition, which occurs during swarming. P. mirabilis uses a diverse set of virulence factors to access and colonize the host urinary tract, including urease and stone formation, fimbriae and other adhesins, iron and zinc acquisition, proteases and toxins, biofilm formation, and regulation of pathogenesis. While significant advances in this field have been made, challenges remain to combatting complicated UTI and deciphering P. mirabilis pathogenesis.

Stochastic analysis of reaction-diffusion processes

Reaction and diffusion processes are used to model chemical and biological processes over a wide range of spatial and temporal scales. Several routes to the diffusion process at various levels of description in time and space are discussed and the master equation for spatially discretized systems involving reaction and diffusion is developed. We discuss an estimator for the appropriate compartment size for simulating reaction-diffusion systems and introduce a measure of fluctuations in a discretized system. We then describe a new computational algorithm for implementing a modified Gillespie method for compartmental systems in which reactions are aggregated into equivalence classes and computational cells are searched via an optimized tree structure. Finally, we discuss several examples that illustrate the issues that have to be addressed in general systems.

Active Brownian agents with concentration-dependent chemotactic sensitivity

We study a biologically motivated model of overdamped, autochemotactic Brownian agents with concentration-dependent chemotactic sensitivity. The agents in our model move stochastically and produce a chemical ligand at their current position. The ligand concentration obeys a reaction-diffusion equation and acts as a chemoattractant for the agents, which bias their motion towards higher concentrations of the dynamically altered chemical field. We explore the impact of concentration-dependent response to chemoattractant gradients on large-scale pattern formation, by deriving a coarse-grained macroscopic description of the individual-based model, and compare the conditions for emergence of inhomogeneous solutions for different variants of the chemotactic sensitivity. We focus primarily on the so-called receptor-law sensitivity, which models a nonlinear decrease of chemotactic sensitivity with increasing ligand concentration. Our results reveal qualitative differences between the receptor law, the constant chemotactic response, and the so-called log law, with respect to stability of the homogeneous solution, as well as the emergence of different patterns (labyrinthine structures, clusters, and bubbles) via spinodal decomposition or nucleation. We discuss two limiting cases, where the model can be reduced to the dynamics of single species: (I) the agent density governed by a density-dependent effective diffusion coefficient and (II) the ligand field with an effective bistable, time-dependent reaction rate. In the end, we turn to single clusters of agents, studying domain growth and determining mean characteristics of the stationary inhomogeneous state. Analytical results are confirmed and extended by large-scale GPU simulations of the individual based model.

Macroscopic equations for bacterial chemotaxis: integration of detailed biochemistry of cell signaling

Chemotaxis of single cells has been extensively studied and a great deal on intracellular signaling and cell movement is known. However, systematic methods to embed such information into continuum PDE models for cell population dynamics are still in their infancy. In this paper, we consider chemotaxis of run-and-tumble bacteria and derive continuum models that take into account of the detailed biochemistry of intracellular signaling. We analytically show that the macroscopic bacterial density can be approximated by the Patlak-Keller-Segel equation in response to signals that change slowly in space and time. We derive, for the first time, general formulas that represent the chemotactic sensitivity in terms of detailed descriptions of single-cell signaling dynamics in arbitrary space dimensions. These general formulas are useful in explaining relations of single cell behavior and population dynamics. As an example, we apply the theory to chemotaxis of bacterium Escherichia coli and show how the structure and kinetics of the intracellular signaling network determine the sensing properties of E. coli populations. Numerical comparison of the derived PDEs and the underlying cell-based models show quantitative agreements for signals that change slowly, and qualitative agreements for signals that change extremely fast. The general theory we develop here is readily applicable to chemotaxis of other run-and-tumble bacteria, or collective behavior of other individuals that move using a similar strategy.

Noise Induced Pattern Switching in Randomly Distributed Delayed Swarms

We study the effects of noise on the dynamics of a system of coupled self-propelling particles in the case where the coupling is time-delayed, and the delays are discrete and randomly generated. Previous work has demonstrated that the stability of a class of emerging patterns depends upon all moments of the time delay distribution, and predicts their bifurcation parameter ranges. Near the bifurcations of these patterns, noise may induce a transition from one type of pattern to another. We study the onset of these noise-induced swarm re-organizations by numerically simulating the system over a range of noise intensities and for various distributions of the delays. Interestingly, there is a critical noise threshold above which the system is forced to transition from a less organized state to a more organized one. We explore this phenomenon by quantifying this critical noise threshold, and note that transition time between states varies as a function of both the noise intensity and delay distribution.

Excitation and adaptation in bacteria-a model signal transduction system that controls taxis and spatial pattern formation

The machinery for transduction of chemotactic stimuli in the bacterium E. coli is one of the most completely characterized signal transduction systems, and because of its relative simplicity, quantitative analysis of this system is possible. Here we discuss models which reproduce many of the important behaviors of the system. The important characteristics of the signal transduction system are excitation and adaptation, and the latter implies that the transduction system can function as a "derivative sensor" with respect to the ligand concentration in that the DC component of a signal is ultimately ignored if it is not too large. This temporal sensing mechanism provides the bacterium with a memory of its passage through spatially- or temporally-varying signal fields, and adaptation is essential for successful chemotaxis. We also discuss some of the spatial patterns observed in populations and indicate how cell-level behavior can be embedded in population-level descriptions.

Statistical multimoment bifurcations in random-delay coupled swarms

We study the effects of discrete, randomly distributed time delays on the dynamics of a coupled system of self-propelling particles. Bifurcation analysis on a mean field approximation of the system reveals that the system possesses patterns with certain universal characteristics that depend on distinguished moments of the time delay distribution. Specifically, we show both theoretically and numerically that although bifurcations of simple patterns, such as translations, change stability only as a function of the first moment of the time delay distribution, more complex patterns arising from Hopf bifurcations depend on all of the moments.

MULTISCALE MODELS OF TAXIS-DRIVEN PATTERNING IN BACTERIAL POPULATIONS

Spatially-distributed populations of various types of bacteria often display intricate spatial patterns that are thought to result from the cellular response to gradients of nutrients or other attractants. In the past decade a great deal has been learned about signal transduction, metabolism and movement in E. coli and other bacteria, but translating the individual-level behavior into population-level dynamics is still a challenging problem. However, this is a necessary step because it is computationally impractical to use a strictly cell-based model to understand patterning in growing populations, since the total number of cells may reach 10(12) - 10(14) in some experiments. In the past phenomenological equations such as the Patlak-Keller-Segel equations have been used in modeling the cell movement that is involved in the formation of such patterns, but the question remains as to how the microscopic behavior can be correctly described by a macroscopic equation. Significant progress has been made for bacterial species that employ a "run-and-tumble" strategy of movement, in that macroscopic equations based on simplified schemes for signal transduction and turning behavior have been derived [14, 15]. Here we extend previous work in a number of directions: (i) we allow for time-dependent signals, which extends the applicability of the equations to natural environments, (ii) we use a more general turning rate function that better describes the biological behavior, and (iii) we incorporate the effect of hydrodynamic forces that arise when cells swim in close proximity to a surface. We also develop a new approach to solving the moment equations derived from the transport equation that does not involve closure assumptions. Numerical examples show that the solution of the lowest-order macroscopic equation agrees well with the solution obtained from a Monte Carlo simulation of cell movement under a variety of temporal protocols for the signal. We also apply the method to derive equations of chemotactic movement that are governed by multiple chemotactic signals.