Bacteria determine fate by playing dice with controlled odds

Ben‐Jacob, Eshel; Schultz, Daniel

doi:10.1073/pnas.1008254107

Cited by 23 publications

(27 citation statements)

References 19 publications

(39 reference statements)

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“…In this section we investigate, the dependence of the gate performance on its capacity to manage the effect of both external and internal noise and on its robustness with regard to changes in the circuit parameters. Our investigation are motivated by previous studies of the relationships between circuit architecture, noise behaviors and the circuit task performance11617.…”

Section: Resultsmentioning

confidence: 99%

“…This hedge survival strategy allows the population to continuously deploy specialized cells in response to, and in anticipation of, possible drastic changes in conditions1234567891011. The stochastic phenotype differentiations (or stochastic fate determinations) involve cell-cell communication and coordination and provide each cell with the flexibility and freedom to select its own phenotype according to the specific conditions it encounters but in harmony with the other cells.…”

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confidence: 99%

“…And how, at the same time, these same individuals leave the final decision to chance in order to avoid the choice of the same phenotype by the whole population? To do so, the decision circuits must have a special capacity (yet not understood) for noise managment, allowing the bacteria to detemine fate by “playing dice with controled odds”1. Cellular capacity to manage the odds should entail both means to program and regulate the noise level and means to program the effect of the noise on the gene circuit performance567891014.…”

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confidence: 99%

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Turning Oscillations Into Opportunities: Lessons from a Bacterial Decision Gate

Schultz

Stavropoulos

et al. 2013

Sci Rep

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Sporulation vs. competence provides a prototypic example of collective cell fate determination. The decision is performed by the action of three modules: 1) A stochastic competence switch whose transition probability is regulated by population density, population stress and cell stress. 2) A sporulation timer whose clock rate is regulated by cell stress and population stress. 3) A decision gate that is coupled to the timer via a special repressilator-like loop. We show that the distinct circuit architecture of this gate leads to special dynamics and noise management characteristics: The gate opens a time-window of opportunity for competence transitions during which it generates oscillations that are turned into a chain of transition opportunities – each oscillation opens a short interval with high transition probability. The special architecture of the gate also leads to filtering of external noise and robustness against internal noise and variations in the circuit parameters.

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Section: Resultsmentioning

confidence: 99%

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Turning Oscillations Into Opportunities: Lessons from a Bacterial Decision Gate

Schultz

Stavropoulos

et al. 2013

Sci Rep

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“…Therefore, only single-cell measurements can unambiguously answer these questions. Until recently, it was believed that cells strongly cross-regulate these pathways, and once one pathway is selected, the other one is automatically repressed Schultz et al (2009); Ben-Jacob and Schultz (2010). However, recent work Kuchina et al (2011) showed that slow progression to sporulation and excursions to competence occurs independently and concurrently up to a certain irreversible decision point, and the choice of the phenotype depends on the outcome of a “molecular race” between the two independently progressing differentiation programs: whichever program reaches the decision point first, wins.…”

Section: Stochasticity In Cell Biologymentioning

confidence: 99%

Noise in biology

2014

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Noise permeates biology on all levels, from the most basic molecular, sub-cellular processes to the dynamics of tissues, organs, organisms, and populations. The functional roles of noise in biological processes can vary greatly. Along with standard, entropy-increasing effects of producing random mutations, diversifying phenotypes in isogenic populations, limiting information capacity of signaling relays, it occasionally plays more surprising constructive roles by accelerating the pace of evolution, providing selective advantage in dynamic environments, enhancing intracellular transport of biomolecules and increasing information capacity of signaling pathways. This short review covers the recent progress in understanding mechanisms and effects of fluctuations in biological systems of different scales and the basic approaches to their mathematical modeling.

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“…the columnar structures emerging in expanding living colonies) in which microscopic self-interactions result in cooperative migration [7,10]. Although each individual in a population can determine its own fate, recent findings highlighted that single behaviours and abilities are adjusted, thanks to stochastic differentiation, in order to fit the needs of the population as a whole [11,12].…”

Section: Introductionmentioning

confidence: 99%

Branching instability in expanding bacterial colonies

Giverso

Verani

Ciarletta

2015

J. R. Soc. Interface.

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Self-organization in developing living organisms relies on the capability of cells to duplicate and perform a collective motion inside the surrounding environment. Chemical and mechanical interactions coordinate such a cooperative behaviour, driving the dynamical evolution of the macroscopic system. In this work, we perform an analytical and computational analysis to study pattern formation during the spreading of an initially circular bacterial colony on a Petri dish. The continuous mathematical model addresses the growth and the chemotactic migration of the living monolayer, together with the diffusion and consumption of nutrients in the agar. The governing equations contain four dimensionless parameters, accounting for the interplay among the chemotactic response, the bacteria-substrate interaction and the experimental geometry. The spreading colony is found to be always linearly unstable to perturbations of the interface, whereas branching instability arises in finite-element numerical simulations. The typical length scales of such fingers, which align in the radial direction and later undergo further branching, are controlled by the size parameters of the problem, whereas the emergence of branching is favoured if the diffusion is dominant on the chemotaxis. The model is able to predict the experimental morphologies, confirming that compact (resp. branched) patterns arise for fast (resp. slow) expanding colonies. Such results, while providing new insights into pattern selection in bacterial colonies, may finally have important applications for designing controlled patterns.

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Bacteria determine fate by playing dice with controlled odds

Cited by 23 publications

References 19 publications

Turning Oscillations Into Opportunities: Lessons from a Bacterial Decision Gate

Turning Oscillations Into Opportunities: Lessons from a Bacterial Decision Gate

Noise in biology

Branching instability in expanding bacterial colonies

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