Mapping the Environmental Fitness Landscape of a Synthetic Gene Circuit

Fig. 2. (A)Interaction web of top-down and bottom-up effects in the eelgrass study system. The top predator is the sea otter (E. lutris), the mesopredators are crabs (Cancer spp. and Pugettia producta), the epiphyte mesograzers are primarily an isopod (I. resecata) and a sea slug (P. taylori), and algal epiphyte competitors of eelgrass primarily consist of chain-forming diatoms, and the red alga Smithora naiadum. Solid arrows indicate direct effects, dashed arrows indicate indirect effects, and the plus and minus symbols indicate positive and/or negative effects on trophic guilds and eelgrass condition. C, competitive interaction; T, trophic interaction. (Original artwork by A. C. Hughes.) (B-E) Survey results testing for the effects of sea otter density on eelgrass bed community properties (Tables S2 and S3). Elkhorn Slough (sea otters present and high nutrients) eelgrass beds (n = 4) are coded in red, and the Tomales Bay reference site (no sea otters, low nutrients) beds (n = 4) are coded in blue. (B) Crab biomass and size structure of two species of Cancer crabs; (C) grazer biomass per shoot and large grazer density; (D) algal epiphyte loading; and (E) aboveground and belowground eelgrass biomass. DW, dry weight; FW, fresh weight.

mentioning

confidence: 99%

Programmed Allee effect in bacteria causes a tradeoff between population spread and survival

Smith

Tan

Srimani

et al. 2014

“…S1). Finally, we note that our gene-centric methodology does not incorporate effects of cell growth and division and, hence, cannot account for growth phenotypes that have been described (25).…”

Section: Discussionmentioning

confidence: 99%

Phenotypic switching in gene regulatory networks

Thomas

Popović

Grima

2014

178

168

Noise in gene expression can lead to reversible phenotypic switching. Several experimental studies have shown that the abundance distributions of proteins in a population of isogenic cells may display multiple distinct maxima. Each of these maxima may be associated with a subpopulation of a particular phenotype, the quantification of which is important for understanding cellular decision-making. Here, we devise a methodology which allows us to quantify multimodal gene expression distributions and single-cell power spectra in gene regulatory networks. Extending the commonly used linear noise approximation, we rigorously show that, in the limit of slow promoter dynamics, these distributions can be systematically approximated as a mixture of Gaussian components in a wide class of networks. The resulting closed-form approximation provides a practical tool for studying complex nonlinear gene regulatory networks that have thus far been amenable only to stochastic simulation. We demonstrate the applicability of our approach in a number of genetic networks, uncovering previously unidentified dynamical characteristics associated with phenotypic switching. Specifically, we elucidate how the interplay of transcriptional and translational regulation can be exploited to control the multimodality of gene expression distributions in two-promoter networks. We demonstrate how phenotypic switching leads to birhythmical expression in a genetic oscillator, and to hysteresis in phenotypic induction, thus highlighting the ability of regulatory networks to retain memory. (5), and cancer cells (6). It has been argued that such stochastic transitions in gene activity can affect stem cell lineage decisions (7,8). Similarly, they may present advantageous strategies when cells make decisions in changing environments (9). Here, we develop a quantitative methodology which allows us to explore the implications of phenotypic switching, and the phenomena associated with it.It is known that gene regulatory networks involving slow promoter switching may lead to distinct expression levels having significant lifetimes; hence, overall expression levels are characterized by bimodal distributions (10-12) or, more generally, by mixture distributions. However, it remains to be resolved how modeling can generally describe and parameterize these distributions. A positive resolution is crucial for the development of testable quantitative and predictive models, e.g., when investigating the sensitivity of bimodality against variation of model parameters, for estimating rate constants from experimentally measured distributions, in the design of synthetic circuitry with tunable gene expression profiles, but, most importantly, when determining the implications of phenotypic decision-making.A class of theoretical models based on the Chemical Master Equation (CME) predicts bimodal protein distributions in the absence of bistability in the corresponding deterministic model (12, 13), some of which have been verified experimentally (1,4,14). Recent efforts to quant...

“…Multiple studies have implicated positive feedback loops in amplifying and maintaining stochastic fluctuations, creating heritable diversity in genetically homogenous cell populations (12,13). This heritable nongenetic diversity then generates random subpopulations that can survive during various forms of environmental stress (14,15), enabling subsequent evolutionary adaptation (16).…”

mentioning

confidence: 99%

Network of mutually repressive metastasis regulators can promote cell heterogeneity and metastatic transitions

Lee

Farquhar

et al. 2014

Self Cite

135

166

The sources and consequences of nongenetic variability in metastatic progression are largely unknown. To address these questions, we characterized a transcriptional regulatory network for the metastasis suppressor Raf kinase inhibitory protein (RKIP). We previously showed that the transcription factor BACH1 is negatively regulated by RKIP and promotes breast cancer metastasis. Here we demonstrate that BACH1 acts in a double-negative (overall positive) feedback loop to inhibit RKIP transcription in breast cancer cells. BACH1 also negatively regulates its own transcription. Analysis of the BACH1 network reveals the existence of an inverse relationship between BACH1 and RKIP involving both monostable and bistable transitions that can potentially give rise to nongenetic variability. Single-cell analysis confirmed monostable and bistablelike behavior. Treatment with histone deacetylase inhibitors or depletion of the polycomb repressor enhancer of zeste homolog 2 altered relative RKIP and BACH1 levels in a manner consistent with a prometastatic state. Together, our results suggest that the mutually repressive relationship between metastatic regulators such as RKIP and BACH1 can play a key role in determining metastatic progression in cancer.ancer progression is an evolutionary process of variant cells competing to expand first locally and then distally within the human body. Ultimately, tumor evolution generates metastatic lesions that account for over 90% of cancer deaths (1). Whereas the requirement of heritably variant cells for tumor progression is undisputed, how they emerge is much less clear. Traditionally, genetic mutations causing oncogene activation or tumor suppressor loss were considered essential (2). However, accumulating evidence for tumor-promoting epigenetic, microenvironmental, and stochastic forms of heritable variation is challenging the traditional view (3, 4). Genetically identical tumor cells show highly variable responses to apoptosis-inducing ligands (5) and chemotherapeutic drugs (6, 7) attributable to cell-cell differences in gene expression and pathway activity. Tumor cells diversify in vitro (8) and in vivo (9) showing different levels of stem cell marker expression. However, the molecular mechanisms and interactions controlling such nongenetic forms of diversity are not fully known (10, 11).Multiple studies have implicated positive feedback loops in amplifying and maintaining stochastic fluctuations, creating heritable diversity in genetically homogenous cell populations (12, 13). This heritable nongenetic diversity then generates random subpopulations that can survive during various forms of environmental stress (14, 15), enabling subsequent evolutionary adaptation (16). Understanding how feedback loops and other network structures may affect nongenetic heterogeneity and contribute to metastatic cancer progression will be crucial for combating the disease (3, 10). However, the regulation of metastasis-related genes is incompletely understood, and the role of regulatory network-mediated n...