Programming stress-induced altruistic death in engineered bacteria.

Programmed death is often associated with a bacterial stress response. This behavior appears paradoxical, as it offers no benefit to the individual. This paradox can be explained if the death is 'altruistic': the killing of some cells can benefit the survivors through release of 'public goods'. However, the conditions where bacterial programmed death becomes advantageous have not been unambiguously demonstrated experimentally. Here, we determined such conditions by engineering tunable, stress-induced altruistic death in the bacterium Escherichia coli. Using a mathematical model, we predicted the existence of an optimal programmed death rate that maximizes population growth under stress. We further predicted that altruistic death could generate the 'Eagle effect', a counter-intuitive phenomenon where bacteria appear to grow better when treated with higher antibiotic concentrations. In support of these modeling insights, we experimentally demonstrated both the optimality in programmed death rate and the Eagle effect using our engineered system. Our findings fill a critical conceptual gap in the analysis of the evolution of bacterial programmed death, and have implications for a design of antibiotic treatment

A forward genetic screen identifies host factors that influence the lysis-lysogeny decision in phage lambda

The lysis‐lysogeny decision made by bacteriophage lambda is one of the classic problems of molecular biology. Shortly after infecting a cell, the virus can either go down the lytic pathway and make more viruses, or go down the lysogenic pathway and integrate itself into the host genome. While much is known about how this decision takes place, the extent to which host physiology influences this decision and the mechanisms by which this influence takes place has remained mysterious. To answer this question, we performed a forward genetic screen to systematically identify all of the genes in E. coli that influence the lysis‐lysogeny decision. Our results demonstrate previously unknown links between host physiology and viral decision making and shed new light on this classic system

A forward genetic screen identifies host factors that influence the lysis-lysogeny decision in phage lambda

The lysis‐lysogeny decision made by bacteriophage lambda is one of the classic problems of molecular biology. Shortly after infecting a cell, the virus can either go down the lytic pathway and make more viruses, or go down the lysogenic pathway and integrate itself into the host genome. While much is known about how this decision takes place, the extent to which host physiology influences this decision and the mechanisms by which this influence takes place has remained mysterious. To answer this question, we performed a forward genetic screen to systematically identify all of the genes in E. coli that influence the lysis‐lysogeny decision. Our results demonstrate previously unknown links between host physiology and viral decision making and shed new light on this classic system

Computation of Steady-State Probability Distributions in Stochastic Models of Cellular Networks

Cellular processes are “noisy”. In each cell, concentrations of molecules are subject to random fluctuations due to the small numbers of these molecules and to environmental perturbations. While noise varies with time, it is often measured at steady state, for example by flow cytometry. When interrogating aspects of a cellular network by such steady-state measurements of network components, a key need is to develop efficient methods to simulate and compute these distributions. We describe innovations in stochastic modeling coupled with approaches to this computational challenge: first, an approach to modeling intrinsic noise via solution of the chemical master equation, and second, a convolution technique to account for contributions of extrinsic noise. We show how these techniques can be combined in a streamlined procedure for evaluation of different sources of variability in a biochemical network. Evaluation and illustrations are given in analysis of two well-characterized synthetic gene circuits, as well as a signaling network underlying the mammalian cell cycle entry

Timing Cellular Decision Making Under Noise via Cell–Cell Communication

Many cellular processes require decision making mechanisms, which must act reliably even in the unavoidable presence of substantial amounts of noise. However, the multistable genetic switches that underlie most decision-making processes are dominated by fluctuations that can induce random jumps between alternative cellular states. Here we show, via theoretical modeling of a population of noise-driven bistable genetic switches, that reliable timing of decision-making processes can be accomplished for large enough population sizes, as long as cells are globally coupled by chemical means. In the light of these results, we conjecture that cell proliferation, in the presence of cell–cell communication, could provide a mechanism for reliable decision making in the presence of noise, by triggering cellular transitions only when the whole cell population reaches a certain size. In other words, the summation performed by the cell population would average out the noise and reduce its detrimental impact

Heterogeneous Response to a Quorum-Sensing Signal in the Luminescence of Individual Vibrio fischeri

The marine bacterium Vibrio fischeri regulates its bioluminescence through a quorum sensing mechanism: the bacterium releases diffusible small molecules (autoinducers) that accumulate in the environment as the population density increases. This accumulation of autoinducer (AI) eventually activates transcriptional regulators for bioluminescence as well as host colonization behaviors. Although V.fischeri quorum sensing has been extensively characterized in bulk populations, far less is known about how it performs at the level of the individual cell, where biochemical noise is likely to limit the precision of luminescence regulation. We have measured the time-dependence and AI-dependence of light production by individual V.fischeri cells that are immobilized in a perfusion chamber and supplied with a defined concentration of exogenous AI. We use low-light level microscopy to record and quantify the photon emission from the cells over periods of several hours as they respond to the introduction of AI. We observe an extremely heterogeneous response to the AI signal. Individual cells differ widely in the onset time for their luminescence and in their resulting brightness, even in the presence of high AI concentrations that saturate the light output from a bulk population. The observed heterogeneity shows that although a given concentration of quorum signal may determine the average light output from a population of cells, it provides far weaker control over the luminescence output of each individual cell

Oscillations by Minimal Bacterial Suicide Circuits Reveal Hidden Facets of Host-Circuit Physiology

Synthetic biology seeks to enable programmed control of cellular behavior though engineered biological systems. These systems typically consist of synthetic circuits that function inside, and interact with, complex host cells possessing pre-existing metabolic and regulatory networks. Nevertheless, while designing systems, a simple well-defined interface between the synthetic gene circuit and the host is frequently assumed. We describe the generation of robust but unexpected oscillations in the densities of bacterium Escherichia coli populations by simple synthetic suicide circuits containing quorum components and a lysis gene. Contrary to design expectations, oscillations required neither the quorum sensing genes (luxR and luxI) nor known regulatory elements in the PluxI promoter. Instead, oscillations were likely due to density-dependent plasmid amplification that established a population-level negative feedback. A mathematical model based on this mechanism captures the key characteristics of oscillations, and model predictions regarding perturbations to plasmid amplification were experimentally validated. Our results underscore the importance of plasmid copy number and potential impact of “hidden interactions” on the behavior of engineered gene circuits - a major challenge for standardizing biological parts. As synthetic biology grows as a discipline, increasing value may be derived from tools that enable the assessment of parts in their final context

Noise regulation by quorum sensing in low mRNA copy number systems

<p>Abstract</p> <p>Background</p> <p>Cells must face the ubiquitous presence of noise at the level of signaling molecules. The latter constitutes a major challenge for the regulation of cellular functions including communication processes. In the context of prokaryotic communication, the so-called quorum sensing (QS) mechanism relies on small diffusive molecules that are produced and detected by cells. This poses the intriguing question of how bacteria cope with the fluctuations for setting up a reliable information exchange.</p> <p>Results</p> <p>We present a stochastic model of gene expression that accounts for the main biochemical processes that describe the QS mechanism close to its activation threshold. Within that framework we study, both numerically and analytically, the role that diffusion plays in the regulation of the dynamics and the fluctuations of signaling molecules. In addition, we unveil the contribution of different sources of noise, intrinsic and transcriptional, in the QS mechanism.</p> <p>Conclusions</p> <p>The interplay between noisy sources and the communication process produces a repertoire of dynamics that depends on the diffusion rate. Importantly, the total noise shows a non-monotonic behavior as a function of the diffusion rate. QS systems seems to avoid values of the diffusion that maximize the total noise. These results point towards the direction that bacteria have adapted their communication mechanisms in order to improve the signal-to-noise ratio.</p

Stochastic E2F Activation and Reconciliation of Phenomenological Cell-Cycle Models

A new, stochastic model of entry into the mammalian cell cycle provides a mechanistic understanding of the temporal variability observed across populations of cells and reconciles previously proposed phenomenological cell-cycle models