84 research outputs found
Mechanism for collective cell alignment in Myxococcus xanthus bacteria
Myxococcus xanthus cells self-organize into aligned groups, clusters, at
various stages of their lifecycle. Formation of these clusters is crucial for
the complex dynamic multi-cellular behavior of these bacteria. However, the
mechanism underlying the cell alignment and clustering is not fully understood.
Motivated by studies of clustering in self-propelled rods, we hypothesized that
M. xanthus cells can align and form clusters through pure mechanical
interactions among cells and between cells and substrate. We test this
hypothesis using an agent-based simulation framework in which each agent is
based on the biophysical model of an individual M. xanthus cell. We show that
model agents, under realistic cell flexibility values, can align and form cell
clusters but only when periodic reversals of cell directions are suppressed.
However, by extending our model to introduce the observed ability of cells to
deposit and follow slime trails, we show that effective trail-following leads
to clusters in reversing cells. Furthermore, we conclude that mechanical cell
alignment combined with slime-trail-following is sufficient to explain the
distinct clustering behaviors observed for wild-type and non-reversing M.
xanthus mutants in recent experiments. Our results are robust to variation in
model parameters, match the experimentally observed trends and can be applied
to understand surface motility patterns of other bacterial species.Comment: Added paragraph on high cell density simulations (new Supp. Figure
S6) in Discussion section; Moved cell model and simulation procedure from
Supplementary methods to Methods section in Main Tex
Interplay of gene expression noise and ultrasensitive dynamics affects bacterial operon organization
This is the publisher's version, also available electronically from "http://journals.plos.org".Bacterial chromosomes are organized into polycistronic cotranscribed operons, but the evolutionary pressures maintaining them are unclear. We hypothesized that operons alter gene expression noise characteristics, resulting in selection for or against maintaining operons depending on network architecture. Mathematical models for 6 functional classes of network modules showed that three classes exhibited decreased noise and 3 exhibited increased noise with same-operon cotranscription of interacting proteins. Noise reduction was often associated with a decreased chance of reaching an ultrasensitive threshold. Stochastic simulations of the lac operon demonstrated that the predicted effects of transcriptional coupling hold for a complex network module. We employed bioinformatic analysis to find overrepresentation of noise-minimizing operon organization compared with randomized controls. Among constitutively expressed physically interacting protein pairs, higher coupling frequencies appeared at lower expression levels, where noise effects are expected to be dominant. Our results thereby suggest an important role for gene expression noise, in many cases interacting with an ultrasensitive switch, in maintaining or selecting for operons in bacterial chromosomes
A mean-field model for nematic alignment of self-propelled rods
In this paper we develop a model for nematic alignment of self-propelled rods
interacting through binary collisions. We avoid phenomenological descriptions
of rod interaction in favor of rigorously using a set of microscopic-level
rules. Under the assumption that each collision results in a small change to a
rod's orientation, we derive the Fokker-Planck equation for the evolution of
the kinetic density function. Using analytical and numerical methods, we study
the emergence of the nematic order from a homogeneous, uniform steady-state of
the mean-field equation.Comment: https://journals.aps.org/pre/abstract/10.1103/PhysRevE.106.03461
Interplay of Gene Expression Noise and Ultrasensitive Dynamics Affects Bacterial Operon Organization
Bacterial chromosomes are organized into polycistronic cotranscribed operons, but the evolutionary pressures maintaining
them are unclear. We hypothesized that operons alter gene expression noise characteristics, resulting in selection for or
against maintaining operons depending on network architecture. Mathematical models for 6 functional classes of network
modules showed that three classes exhibited decreased noise and 3 exhibited increased noise with same-operon
cotranscription of interacting proteins. Noise reduction was often associated with a decreased chance of reaching an
ultrasensitive threshold. Stochastic simulations of the lac operon demonstrated that the predicted effects of transcriptional
coupling hold for a complex network module. We employed bioinformatic analysis to find overrepresentation of noiseminimizing
operon organization compared with randomized controls. Among constitutively expressed physically
interacting protein pairs, higher coupling frequencies appeared at lower expression levels, where noise effects are
expected to be dominant. Our results thereby suggest an important role for gene expression noise, in many cases
interacting with an ultrasensitive switch, in maintaining or selecting for operons in bacterial chromosomes
Myxococcus xanthus gliding motors are elastically coupled to the substrate as predicted by the focal adhesion model of gliding motility
Myxococcus xanthus is a model organism for studying bacterial social
behaviors due to its ability to form complex multi-cellular structures.
Knowledge of M. xanthus surface gliding motility and the mechanisms that
coordinate it are critically important to our understanding of collective cell
behaviors. Although the mechanism of gliding motility is still under
investigation, recent experiments suggest that there are two possible
mechanisms underlying force production for cell motility: the focal adhesion
mechanism and the helical rotor mechanism which differ in the biophysics of the
cell-substrate interactions. Whereas the focal adhesion model predicts an
elastic coupling, the helical rotor model predicts a viscous coupling. Using a
combination of computational modeling, imaging, and force microscopy, we find
evidence for elastic coupling in support of the focal adhesion model. Using a
biophysical model of the M. xanthus cell, we investigated how the mechanical
interactions between cells are affected by interactions with the substrate.
Comparison of modeling results with experimental data for cell-cell collision
events pointed to a strong, elastic attachment between the cell and substrate.
These results are robust to variations in the mechanical and geometrical
parameters of the model. We then directly measured the motor-substrate coupling
by monitoring the motion of optically trapped beads and find that motor
velocity decreases exponentially with opposing load. At high loads, motor
velocity approaches zero velocity asymptotically and motors remain bound to
beads indicating a strong, elastic attachment
Modeling mechanical interactions in growing populations of rod-shaped bacteria
Advances in synthetic biology allow us to engineer bacterial collectives with pre-specified characteristics. However, the behavior of these collectives is difficult to understand, as cellular growth and division as well as extra-cellular fluid flow lead to complex, changing arrangements of cells within the population. To rationally engineer and control the behavior of cell collectives we need theoretical and computational tools to understand their emergent spatiotemporal dynamics. Here, we present an agent-based model that allows growing cells to detect and respond to mechanical interactions. Crucially, our model couples the dynamics of cell growth to the cell's environment: Mechanical constraints can affect cellular growth rate and a cell may alter its behavior in response to these constraints. This coupling links the mechanical forces that influence cell growth and emergent behaviors in cell assemblies. We illustrate our approach by showing how mechanical interactions can impact the dynamics of bacterial collectives growing in microfluidic traps
Coupling between feedback loops in autoregulatory networks affects bistability range, open-loop gain and switching times
Biochemical regulatory networks governing diverse cellular processes such as stress-response,
differentiation and cell cycle often contain coupled feedback loops. We aim at understanding
how features of feedback architecture, such as the number of loops, the sign of the loops and
the type of their coupling, affect network dynamical performance. Specifically, we investigate
how bistability range, maximum open-loop gain and switching times of a network with
transcriptional positive feedback are affected by additive or multiplicative coupling with
another positive- or negative-feedback loop. We show that a network's bistability range is
positively correlated with its maximum open-loop gain and that both quantities depend on the
sign of the feedback loops and the type of feedback coupling. Moreover, we find that the
addition of positive feedback could decrease the bistability range if we control the basal level
in the signal-response curves of the two systems. Furthermore, the addition of negative
feedback has the capacity to increase the bistability range if its dissociation constant is much
lower than that of the positive feedback. We also find that the addition of a positive feedback to
a bistable network increases the robustness of its bistability range, whereas the addition of a
negative feedback decreases it. Finally, we show that the switching time for a transition from a
high to a low steady state increases with the effective fold change in gene regulation. In
summary, we show that the effect of coupled feedback loops on the bistability range and
switching times depends on the underlying mechanistic details
Breakdown of Boltzmann-type Models for Nematic Alignment of Self-propelled Rods
Studies in active matter systems and in the collective motility of organisms
utilize a range of analytical approaches to formulate continuous kinetic models
of collective dynamics from the rules or equations describing agent
interactions. However, the derivation of these models often relies on
Boltzmann's hypothesis of "molecular chaos", often simply called statistical
independence. While it is often the simplest way to derive tractable models it
is not clear whether the statistical independence assumption is valid in
practice. In this work, we develop a Boltzmann-type kinetic model for the
nematic alignment of self-propelled rods where rod reorientation occurs upon
binary collisions. We identify relevant parameters and derive kinetic equations
for the corresponding asymptotic regime. By comparing numerical solutions of
the kinetic equations to an agent-based model that implements our microscopic
alignment rules, we examine the accuracy of the continuous model. The results
indicate that our kinetic model fails to replicate the underlying dynamics due
to the formation of clusters that violate statistical independence.
Additionally, we show that a mechanism limiting cluster formation helps to
improve the agreement between the analytical model and agent simulations. These
results highlight the need to improve modeling approaches for active matter
systems
The Mechanistic Basis of Myxococcus xanthus Rippling Behavior and Its Physiological Role during Predation
Myxococcus xanthus cells self-organize into periodic bands of traveling waves, termed ripples, during multicellular fruiting
body development and predation on other bacteria. To investigate the mechanistic basis of rippling behavior and its
physiological role during predation by this Gram-negative soil bacterium, we have used an approach that combines
mathematical modeling with experimental observations. Specifically, we developed an agent-based model (ABM) to
simulate rippling behavior that employs a new signaling mechanism to trigger cellular reversals. The ABM has demonstrated
that three ingredients are sufficient to generate rippling behavior: (i) side-to-side signaling between two cells that causes
one of the cells to reverse, (ii) a minimal refractory time period after each reversal during which cells cannot reverse again,
and (iii) physical interactions that cause the cells to locally align. To explain why rippling behavior appears as a consequence
of the presence of prey, we postulate that prey-associated macromolecules indirectly induce ripples by stimulating side-toside
contact-mediated signaling. In parallel to the simulations, M. xanthus predatory rippling behavior was experimentally
observed and analyzed using time-lapse microscopy. A formalized relationship between the wavelength, reversal time, and
cell velocity has been predicted by the simulations and confirmed by the experimental data. Furthermore, the results
suggest that the physiological role of rippling behavior during M. xanthus predation is to increase the rate of spreading over
prey cells due to increased side-to-side contact-mediated signaling and to allow predatory cells to remain on the prey
longer as a result of more periodic cell motility
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