Self-driven jamming in growing microbial populations

Delarue, Morgan; Hartung, Jörn; Schreck, Carl F.; Gniewek, Pawel; Hu, Lucy; Herminghaus, Stephan; Hallatschek, Oskar

doi:10.1038/nphys3741

Cited by 141 publications

(143 citation statements)

References 52 publications

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“…Furthermore, we found that having access to a separate estimate of the dilution rate, B, can resolve 209 unidentifiability issue when only partial data is available. For instance, as their number increases, cells in 210 microfluidic traps can become crowded and their growth can slow as a result [49,50]. To ensure measurements 211 under minimal strains on the cells, sometimes we use only the initial fluorescence measurements before 212 crowding can impact gene expression (e.g.…”

mentioning

confidence: 99%

Bayesian inference of distributed time delay in transcriptional and translational regulation

Choi

Cheng

Cinar

et al. 2019

Preprint

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Motivation: Advances in experimental and imaging techniques have allowed for unprecedented insights into the dynamical processes within individual cells. However, many facets of intracellular dynamics remain hidden, or can be measured only indirectly. This makes it challenging to reconstruct the regulatory networks that govern the biochemical processes underlying various cell functions. Current estimation techniques for inferring reaction rates frequently rely on marginalization over unobserved processes and states. Even in simple systems this approach can be computationally challenging, and can lead to large uncertainties and lack of robustness in parameter estimates. Therefore we will require alternative approaches to efficiently uncover the interactions in complex biochemical networks.Results: We propose a Bayesian inference framework based on replacing uninteresting or unobserved reactions with time delays. Although the resulting models are non-Markovian, recent results on stochastic systems with random delays allow us to rigorously obtain expressions for the likelihoods of model parameters. In turn, this allows us to extend MCMC methods to efficiently estimate reaction rates, and delay distribution parameters, from single-cell assays. We illustrate the advantages, and potential pitfalls, of the approach using a birth-death model with both synthetic and experimental data, and show that we can robustly infer model parameters using a relatively small number of measurements. We demonstrate how to do so even when only the relative molecule count within the cell is measured, as in the case of fluorescence microscopy. Introduction 1The dynamics of intracellular processes are determined by the structure and rates of interactions between 2 different molecular species. However, stochasticity and limitations of experimental methods make it difficult 3 to infer the characteristics of these interactions from data. On the single-cell level, different molecular 4 species can occur in small number, correlate with phenotype, and localize within different parts of the cell. 5The resulting dynamics can thus be highly variable over time, and across the population. Averaging over 6 such fluctuations can lead to inaccurate representations of the underlying biology [1], and inference methods 7 therefore need to account for stochasticity within individual cells, and variability across the population [2-5].8 Different statistical approaches have been developed to fit stochastic models to data from single-cell 9 assays, offering a window into the dynamical processes within individual cells [6][7][8][9][10][11][12][13]. Among these, Bayesian 10 methods have been particularly promising. To apply Bayesian techniques, one typically assumes a model 11 for the network of interactions, postulates a prior over model parameters, and uses experimental data to 12 determine a posterior and estimates of unknown parameters [6,[13][14][15]. However, Bayesian approaches can 13 suffer from the curse of dimensionality [16], and are thus difficult to i...

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confidence: 99%

Bayesian inference of distributed time delay in transcriptional and translational regulation

Choi

Cheng

Cinar

et al. 2019

Preprint

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“…This clogging phenomenon appears in many different systems with typical sizes ranging over several orders of magnitude. At very small spatial scales, clogging leads to intermittent flow when a dense suspension of microbes [1] or microparticles [2][3][4] passes through a constriction in a microchannel. Clogging is also behind dramatic cases of deaths when crowds in panic are evacuated through small emergency exits [5].…”

Section: Introductionmentioning

confidence: 99%

Linking bottleneck clogging with flow kinematics in granular materials: The role of silo width

et al. 2017

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We demonstrate experimentally that clogging in a silo correlates with some features of the particle velocities in the outlet proximities. This finding, that links the formation of clogs with a kinematic property of the system, is obtained by looking at the effect that the position of the lateral walls of the silo has on the flow and clogging behavior. Surprisingly, the avalanche size depends nonmonotonically on the distance of the outlet from the lateral walls. Apart from evidencing the relevance of a parameter that has been traditionally overlooked in bottleneck flow, this nonmonotonicity supposes a benchmark with which to explore the correlation of clogging probability with different variables within the system. Among these, we find that the velocity of the particles above the outlet and their fluctuations seem to be behind the nonmonotonicity in the avalanche size versus wall distance curve.

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“…The mechanical process underlying colony expansion has been recently found to have important evolutionary consequences for the strength of genetic drift [26] and natural selection [31,32]. To gain an intuition into whether mechanical forces can generate the distribution of small clones we observe, we employed 2D mechanical simulations where individually-modeled cells proliferate and repel each other upon contact (see Methods) [31,47]. We introduced an explicit growth layer of finite width λ within which cells grow exponentially at a constant rate (Fig 2a).…”

Section: Mechanical Simulationsmentioning

confidence: 99%

Impact of crowding on the diversity of expanding populations

Fusco

Karita

et al. 2019

Preprint

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Crowding effects are key to the self-organization of densely packed cellular assemblies, such as biofilms, solid tumors, and developing tissues. When cells grow and divide they push each other apart, remodeling the structure and extent of the populations range. It has recently been shown that crowding effects also couple the evolutionary fate of neighboring cells, thereby weakening the strength of natural selection. However, the impact of crowding on neutral processes remains unclear. Here, we quantify the genetic diversity of expanding microbial colonies and uncover signatures of crowding in the site frequency spectrum. By combining fluctuation tests, cell-based simulations, and lineage tracing in a novel microfluidic incubator, we find that the majority of mutations arise behind the expanding frontier, giving rise to clones that are mechanically "pushed out" of the growing region by the proliferating cells in front. These excluded-volume interactions result in a clone size distribution that solely depends on where the mutation first arose relative to the front and is characterized by a simple power-law for sizes below a critical threshold. Our model and simulations predict that the distribution only depends on a single parameter, the characteristic growth layer thickness, and hence allows estimation of the mutation rate in a variety of crowded cellular populations. Combined with previous studies on high-frequency mutations, our finding provides a unified picture of the genetic diversity in expanding populations over the whole frequency range and suggests a practical method to assess growth dynamics by sequencing populations across scales.

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Self-driven jamming in growing microbial populations

Cited by 141 publications

References 52 publications

Bayesian inference of distributed time delay in transcriptional and translational regulation

Bayesian inference of distributed time delay in transcriptional and translational regulation

Linking bottleneck clogging with flow kinematics in granular materials: The role of silo width

Impact of crowding on the diversity of expanding populations

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