The OR control system of bacteriophage lambda

Shea, Madeline A.; Ackers, Gary K.

doi:10.1016/0022-2836(85)90086-5

Cited by 473 publications

(247 citation statements)

References 26 publications

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“…n is the Hill coefficient and determines the steepness of the repression curve. For example, the cI repressor protein acts on the promoters P R and PRM of phage with a Kd of about 50 and 1,000 nM, respectively (26). Typical biological values for n range from 1 (hyperbolic control) to over 30 (sharp switching).…”

Section: Resultsmentioning

confidence: 99%

“…Interactions between species in a network are embodied by transacting factors such as repressor proteins. Such regulatory proteins tend to act by binding the promoter region and shielding it from RNAP, as is the case for the lac repressor (25) or for the Cro protein of phage (26). These reactions are considered to be in equilibrium and simply change the fraction of RNAP bound as a closed complex, thereby changing the effective rate k R of transcript initiation (25).…”

Section: Appendixmentioning

confidence: 99%

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Intrinsic noise in gene regulatory networks

Thattai

Oudenaarden

2001

Proc. Natl. Acad. Sci. U.S.A.

1,353

1,441

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Cells are intrinsically noisy biochemical reactors: low reactant numbers can lead to significant statistical fluctuations in molecule numbers and reaction rates. Here we use an analytic model to investigate the emergent noise properties of genetic systems. We find for a single gene that noise is essentially determined at the translational level, and that the mean and variance of protein concentration can be independently controlled. The noise strength immediately following single gene induction is almost twice the final steady-state value. We find that fluctuations in the concentrations of a regulatory protein can propagate through a genetic cascade; translational noise control could explain the inefficient translation rates observed for genes encoding such regulatory proteins. For an autoregulatory protein, we demonstrate that negative feedback efficiently decreases system noise. The model can be used to predict the noise characteristics of networks of arbitrary connectivity. The general procedure is further illustrated for an autocatalytic protein and a bistable genetic switch. The analysis of intrinsic noise reveals biological roles of gene network structures and can lead to a deeper understanding of their evolutionary origin.

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Section: Resultsmentioning

confidence: 99%

Section: Appendixmentioning

confidence: 99%

Intrinsic noise in gene regulatory networks

Thattai

Oudenaarden

2001

Proc. Natl. Acad. Sci. U.S.A.

1,353

1,441

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“…Furthermore, for simplicity, the basal expression rates of these three genes are assumed to be zero. We use the Shea-Ackers formalism, which is a widely used thermodynamic approach, to represent the gene expression based on the structure of transcription machinery [38]. All these assumptions led to the following model to realize the genetic switching of the GATA-PU.1 regulatory network, given by…”

Section: Methodsmentioning

confidence: 99%

Mathematical modeling of GATA-switching for regulating the differentiation of hematopoietic stem cell

Tian

Smith‐Miles

2014

BMC Systems Biology

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BackgroundHematopoiesis is a highly orchestrated developmental process that comprises various developmental stages of the hematopoietic stem cells (HSCs). During development, the decision to leave the self-renewing state and selection of a differentiation pathway is regulated by a number of transcription factors. Among them, genes GATA-1 and PU.1 form a core negative feedback module to regulate the genetic switching between the cell fate choices of HSCs. Although extensive experimental studies have revealed the mechanisms to regulate the expression of these two genes, it is still unclear how this simple module regulates the genetic switching.MethodsIn this work we proposed a mathematical model to study the mechanisms of the GATA-PU.1 gene network in the determination of HSC differentiation pathways. We incorporated the mechanisms of GATA switch into the module, and developed a mathematical model that comprises three genes GATA-1, GATA-2 and PU.1. In addition, a novel multiple-objective optimization method was designed to infer unknown parameters in the proposed model by realizing different experimental observations. A stochastic model was also designed to describe the critical function of noise, due to the small copy numbers of molecular species, in determining the differentiation pathways.ResultsThe proposed deterministic model has successfully realized three stable steady states representing the priming and different progenitor cells as well as genetic switching between the genetic states under various experimental conditions. Using different values of GATA-1 synthesis rate for the GATA-1 protein availability in the chromatin sites during the time period of GATA switch, stochastic simulations for the first time have realized different proportions of cells leading to different developmental pathways under various experimental conditions.ConclusionsMathematical models provide testable predictions regarding the mechanisms and conditions for realizing different differentiation pathways of hematopoietic stem cells. This work represents the first attempt at using a discrete stochastic model to realize the decision of HSC differentiation pathways showing a multimodal distribution.

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“…We follow the approach of Shea and Ackers [16] and Buchler et al [17] in modelling gene expression. The approach relies on the idea of "regulated recruitment" [18,19]: gene regulatory proteins control gene expression by modulating the probability P that the enzyme RNA polymerase is bound to the DNA; if the RNA polymerase is bound, then it is assumed that gene expression occurs at a fixed rate β.…”

Section: Supplementary Materials -Feed-forward Loopsmentioning

confidence: 99%

Statistical Analysis of the Spatial Distribution of Operons in the Transcriptional Regulation Network of Escherichia coli

Warren¹,

Wolde²

2004

Journal of Molecular Biology

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We have performed a statistical analysis of the spatial distribution of operons in the transcriptional regulation network of Escherichia coli. The analysis reveals that operons that regulate each other and operons that are coregulated tend to lie next to each other on the genome. Moreover, these pairs of operons tend to be transcribed in diverging directions. This spatial arrangement of operons allows the upstream regulatory regions to interfere with each other. This affords additional regulatory control, as illustrated by a mean-field analysis of a feed-forward loop. Our results suggest that regulatory control can provide a selection pressure that drives operons together in the course of evolution.Most, if not all, organisms can respond and adapt to a changing environment. To this end, they can detect, transmit, and amplify environmental signals, as well as integrate different signals to perform computations analogous to electronic devices. Indeed, all organisms can be considered to be information processing machines. Yet, how the living cell accurately processes information, is still poorly understood. Recent technological developments, however, have made it possible to acquire information on the regulatory architecture of the cell on a massive scale, and extensive databases are now available that catalog biochemical networks. This offers unprecedented possibilities to unravel the design principles by which organisms process information.The current richness of genomic data surrounding Escherichia coli makes it no doubt one of the best characterized of all living organisms. The condensation of genes into operons and the organization of operons into the transcriptional regulation network are now well mapped, and this information has been used to investigate generic features such as the appearance of motifs in the transcriptional regulation network [1]. Here, we present a study of the spatial organization of operons in the transcriptional regulation network of E. coli. Our analysis of the spatial distribution of operons provides two distinct advantages over previous studies on the spatial distribution of genes [2,3,4,5,6,7,8]: firstly, it excludes correlations from genes that belong to the same operon. Secondly, and more importantly, by focusing on the higher-level organisation of operons into the transcriptional regulation network, the analysis allows us to elucidate spatial correlations associated with regulatory control, for instance, by identifying coregulated pairs of operons that are adjacent on the DNA.We find that there is a marked tendency for operons that are related to each other in the transcriptional regulation network to be nearest neighbours, compared to networks in which operons are randomly assigned positions on the DNA. Furthermore, the separations between neighbour pairs have a strong bias towards short distances, which is most pronounced for pairs that are transcribed in diverging directions. In fact, our analysis identifies a new, spatial network motif that consists of pairs of overlapping op...

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The OR control system of bacteriophage lambda

Cited by 473 publications

References 26 publications

Intrinsic noise in gene regulatory networks

Intrinsic noise in gene regulatory networks

Mathematical modeling of GATA-switching for regulating the differentiation of hematopoietic stem cell

Statistical Analysis of the Spatial Distribution of Operons in the Transcriptional Regulation Network of Escherichia coli

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