From the volcano effect to banding: a minimal model for bacterial behavioral transitions near chemoattractant sources

Sharp chemoattractant (CA) gradient variations near food sources may give rise to dramatic behavioral changes of bacteria neighboring these sources. For instance, marine bacteria exhibiting run-reverse motility are known to form distinct bands around patches (large sources) of chemoattractant such as nutrient-soaked beads while run-and-tumble bacteria have been predicted to exhibit a 'volcano effect' (spherical shell-shaped density) around a small (point) source of food. Here we provide the first minimal model of banding for run-reverse bacteria and show that, while banding and the volcano effect may appear superficially similar, they are different physical effects manifested under different source emission rate (and thus effective source size). More specifically, while the volcano effect is known to arise around point sources from a bacterium's temporal differentiation of signal (and corresponding finite integration time), this effect alone is insufficient to account for banding around larger patches as bacteria would otherwise cluster around the patch without forming bands at some fixed radial distance. In particular, our model demonstrates that banding emerges from the interplay of run-reverse motility and saturation of the bacterium's chemoreceptors to CA molecules and our model furthermore predicts that run-reverse bacteria susceptible to banding behavior should also exhibit a volcano effect around sources with smaller emission rates

The volcano effect in bacterial chemotaxis

A population-level model of bacterial chemotaxis is derived from a simple bacterial-level model of behavior. This model, to be contrasted with the Keller–Segel equations, exhibits behavior we refer to as the “volcano effect”: steady-state bacterial aggregation forming a ring of higher density some distance away from an optimal environment. The model is derived, as in Erban and Othmer (2004) [1] R. Erban and H.G. Othmer, From individual to collective behavior in bacterial chemotaxis. SIAM J. Appl. Math, 65 (2004), pp. 361–391. Full Text via CrossRef[1], from a transport equation in a state space including the internal biochemical variables of the bacteria and then simplified with a truncation at low moments with respect to these variables. We compare the solutions of the model to stochastic simulations of many bacteria, as well as the classic Keller–Segel model. This model captures behavior that the Keller–Segel model is unable to resolve, and sheds light on two different mechanisms that can cause a volcano effect

Inferring models of bacterial dynamics toward point sources

Experiments have shown that bacteria can be sensitive to small variations in chemoattractant (CA) concentrations. Motivated by these findings, our focus here is on a regime rarely studied in experiments: bacteria tracking point CA sources (such as food patches or even prey). In tracking point sources, the CA detected by bacteria may show very large spatiotemporal fluctuations which vary with distance from the source. We present a general statistical model to describe how bacteria locate point sources of food on the basis of stochastic event detection, rather than CA gradient information. We show how all model parameters can be directly inferred from single cell tracking data even in the limit of high detection noise. Once parameterized, our model recapitulates bacterial behavior around point sources such as the "volcano effect". In addition, while the search by bacteria for point sources such as prey may appear random, our model identifies key statistical signatures of a targeted search for a point source given any arbitrary source configuration

A pathway-based mean-field model for E. coli chemotaxis: Mathematical derivation and Keller-Segel limit

A pathway-based mean-field theory (PBMFT) was recently proposed for E. coli chemotaxis in [G. Si, T. Wu, Q. Quyang and Y. Tu, Phys. Rev. Lett., 109 (2012), 048101]. In this paper, we derived a new moment system of PBMFT by using the moment closure technique in kinetic theory under the assumption that the methylation level is locally concentrated. The new system is hyperbolic with linear convection terms. Under certain assumptions, the new system can recover the original model. Especially the assumption on the methylation difference made there can be understood explicitly in this new moment system. We obtain the Keller-Segel limit by taking into account the different physical time scales of tumbling, adaptation and the experimental observations. We also present numerical evidence to show the quantitative agreement of the moment system with the individual based E. coli chemotaxis simulator.Comment: 21 pages, 3 figure

Travelling waves in hyperbolic chemotaxis equations

Mathematical models of bacterial populations are often written as systems of partial differential equations for the densities of bacteria and concentrations of extracellular (signal) chemicals. This approach has been employed since the seminal work of Keller and Segel in the 1970s [Keller and Segel, J. Theor. Biol., 1971]. The system has been shown to permit travelling wave solutions which correspond to travelling band formation in bacterial colonies, yet only under specific criteria, such as a singularity in the chemotactic sensitivity function as the signal approaches zero. Such a singularity generates infinite macroscopic velocities which are biologically unrealistic. In this paper, we formulate a model that takes into consideration relevant details of the intracellular processes while avoiding the singularity in the chemotactic sensitivity. We prove the global existence of solutions and then show the existence of travelling wave solutions both numerically and analytically

Volcano effect in chemotactic aggregation and an extended Keller-Segel mode

Aggregation of chemotactic bacteria under a unimodal distribution of chemical cues was investigated by Monte Carlo simulation and asymptotic analysis based on a kinetic transport equation, which considers an internal adaptation dynamics as well as a finite tumbling duration. It was found that there exist two different regimes of the adaptation time, between which the effect of the adaptation time on the aggregation behavior is reversed; that is, when the adaptation time is as small as the running duration, the aggregation becomes increasingly steeper as the adaptation time increases, while, when the adaptation time is as large as the diffusion time of the population density, the aggregation becomes more diffusive as the adaptation time increases. Moreover, notably, the aggregation profile becomes bimodal (volcano) at the large adaptation-time regime while it is always unimodal at the small adaptation-time regime. The volcano effect occurs in such a way that the population of tumbling cells considerably decreases in a diffusion layer which is created near the peak of the external chemical cues due to the coupling of diffusion and internal adaptation of the bacteria. Two different continuum-limit models are derived by the asymptotic analysis according to the scaling of the adaptation time; that is, at the small adaptation-time regime, the Keller-Segel model while, at the large adaptation-time regime, an extension of KS model, which involves both the internal variable and the tumbling duration. Importantly, either of the models can accurately reproduce the MC results at each adaptation-time regime, involving the volcano effect. Thus, we conclude that the coupling of diffusion, adaptation, and finite tumbling duration is crucial for the volcano effect

Human Pleural Fluid Elicits Pyruvate and Phenylalanine Metabolism in Acinetobacter baumannii to Enhance Cytotoxicity and Immune Evasion

The CCAAT box-harboring proteins represent a family of heterotrimeric transcription factors which is highly conserved in eukaryotes. In fungi, one of the particularly important homologs of this family is the Hap complex that separates the DNA-binding domain from the activation domain and imposes essential impacts on regulation of a wide range of cellular functions. So far, a comprehensive summary of this complex has been described in filamentous fungi but not in the yeast. In this review, we summarize a number of studies related to the structure and assembly mode of the Hap complex in a list of representative yeasts. Furthermore, we emphasize recent advances in understanding the regulatory functions of this complex, with a special focus on its role in regulating respiration, production of reactive oxygen species (ROS) and iron homeostasis.Fil: Nyah, Rodman. California State University; Estados UnidosFil: Martinez, Jasmine. California State University; Estados UnidosFil: Fung, Sammie. California State University; Estados UnidosFil: Nakanouchi, Jun. California State University; Estados UnidosFil: Myers, Amber L.. California State University; Estados UnidosFil: Harris, Caitlin M.. California State University; Estados UnidosFil: Dang, Emily. California State University; Estados UnidosFil: Fernandez, Jennifer. California State University; Estados UnidosFil: Liu, Christine. California State University; Estados UnidosFil: Mendoza, Anthony M.. California State University; Estados UnidosFil: Jimenez, Verónica. California State University; Estados UnidosFil: Nikolaidis, Nikolas. California State University; Estados UnidosFil: Brennan, Catherine A.. California State University; Estados UnidosFil: Bonomo, Robert A.. Louis Stokes Cleveland Department of Veterans Affairs Medical Cente; Estados Unidos. Center for Antimicrobial Resistance and Epidemiology; Estados Unidos. Case Western Reserve University School of Medicine; Estados UnidosFil: Sieira, Rodrigo. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Instituto de Investigaciones Bioquímicas de Buenos Aires. Fundación Instituto Leloir. Instituto de Investigaciones Bioquímicas de Buenos Aires; ArgentinaFil: Ramirez, Maria Soledad. California State University; Estados Unido