scholarly journals Bursty Expression Enhances Reliability of Messaging in Gene Networks

2020 ◽  
Author(s):  
Leonardo R. Gama ◽  
Guilherme Giovanini ◽  
Gábor Balázsi ◽  
Alexandre F. Ramos
Keyword(s):  
Transcription Factors ◽  
Mutual Information ◽  
Gene Networks ◽  
Gene Network ◽  
The State ◽  
Expression Levels ◽  
Temporal Precision ◽  
Spatio Temporal ◽  
Steady State Solutions ◽  
Slow Switching

AbstractThe promoter state of a gene and its expression levels are modulated by the amounts of transcription factors interacting with its regulatory regions. Hence, one may interpret a gene network as a communicating system in which the state of the promoter of a gene (the source) is communicated by the amounts of transcription factors that it expresses (the message) to modulate the state of the promoter and expression levels of another gene (the receptor). The reliability of the gene network dynamics can be quantified by the Shannon’s entropy of the message and the mutual information between the message and the promoter state. Here we consider a stochastic model for a binary gene and use its exact steady state solutions to calculate the entropy and mutual information. We show that a slow switching promoter having long and equally standing ON and OFF states maximizes the mutual information and reduces entropy. That is a bursty regime generating a high variance message governed by a bimodal probability distribution with peaks of the same height. Our results indicate that Shannon’s theory can be a powerful framework for understanding how bursty gene expression conciliates with the striking spatio-temporal precision exhibited in pattern formation of developing organisms.

scholarly journals Binary Expression Enhances Reliability of Messaging in Gene Networks

Entropy ◽  
2020 ◽  
Vol 22 (4) ◽  
pp. 479
Author(s):  
Leonardo R. Gama ◽  
Guilherme Giovanini ◽  
Gábor Balázsi ◽  
Alexandre F. Ramos
Keyword(s):  
Gene Expression ◽  
Transcription Factors ◽  
Mutual Information ◽  
Gene Networks ◽  
Gene Network ◽  
The State ◽  
Expression Levels ◽  
Temporal Precision ◽  
Spatio Temporal ◽  
Steady State Solutions

The promoter state of a gene and its expression levels are modulated by the amounts of transcription factors interacting with its regulatory regions. Hence, one may interpret a gene network as a communicating system in which the state of the promoter of a gene (the source) is communicated by the amounts of transcription factors that it expresses (the message) to modulate the state of the promoter and expression levels of another gene (the receptor). The reliability of the gene network dynamics can be quantified by Shannon’s entropy of the message and the mutual information between the message and the promoter state. Here we consider a stochastic model for a binary gene and use its exact steady state solutions to calculate the entropy and mutual information. We show that a slow switching promoter with long and equally standing ON and OFF states maximizes the mutual information and reduces entropy. That is a binary gene expression regime generating a high variance message governed by a bimodal probability distribution with peaks of the same height. Our results indicate that Shannon’s theory can be a powerful framework for understanding how bursty gene expression conciliates with the striking spatio-temporal precision exhibited in pattern formation of developing organisms.

EFFECTIVE MODELS FOR GENE NETWORKS AND THEIR APPLICATIONS

2012 ◽  
Vol 07 (01n02) ◽  
pp. 41-70 ◽  
Author(s):  
JASON SHULMAN ◽  
LARS SEEMANN ◽  
GREGG W. ROMAN ◽  
GEMUNU H. GUNARATNE
Keyword(s):  
Gene Expression ◽  
Gene Networks ◽  
Gene Network ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Stationary Solutions ◽  
The State ◽  
Effective Model ◽  
Knockout Mutant ◽  
Double Knockout

Networks are used to abstract large, highly-coupled sets of objects. Their analyses have included network classification into a few broad classes and selection of small substructures that perform simple yet common tasks. One issue that has received little attention is how the state of a network can be moved according to a pre-specified set of guidelines. In this paper, we address this question in the context of gene networks. In general, neither the full membership of the gene network associated with a biological process nor the precise form of interactions between nodes is known. What is available, through microarrays or sequencing, are gene expression profiles of an organism or its viable mutants. Our approach relies only on these expression profiles, and not on the availability of an accurate model for the network. The first step is to select a small set of core- or master- nodes, such as transcription factors or microRNAs, that can be used to alter the levels of many of the remaining genes in the network. We ask how the state — or solution — of the gene network changes as the levels of these master nodes are altered externally. The object of our study is, not the network, but the surface of these solutions. We argue that it can be approximated using gene expression profiles of the organism and single manipulation of master node activity. This is done through an "effective model." The effective model as well as error estimates for its predictions can be derived from experimental data. The method is validated using synthetic gene networks that have stationary solutions and those that are periodically driven, e.g., circadian networks. An effective model for the oxygen-deprivation network of E.coli is constructed using previously published gene expression profiles, and used to predict the expression levels in a double knockout mutant. Less that 30% of the predictions lie outside the 5% confidence level. We propose the use of the effective model methodology to compute how Drosophila melanogaster in the normal state can be genetically altered into a pre-defined sleep deprived-like state.

scholarly journals Morphogenesis at criticality

2014 ◽  
Vol 111 (10) ◽  
pp. 3683-3688 ◽  
Author(s):  
Dmitry Krotov ◽  
Julien O. Dubuis ◽  
Thomas Gregor ◽  
William Bialek
Keyword(s):  
Transcription Factors ◽  
Spatial Patterns ◽  
Gaussian Distribution ◽  
Gene Network ◽  
Fruit Fly ◽  
Genetic Network ◽  
Strong Correlations ◽  
Expression Levels ◽  
Gap Gene ◽  
Gap Genes

Spatial patterns in the early fruit fly embryo emerge from a network of interactions among transcription factors, the gap genes, driven by maternal inputs. Such networks can exhibit many qualitatively different behaviors, separated by critical surfaces. At criticality, we should observe strong correlations in the fluctuations of different genes around their mean expression levels, a slowing of the dynamics along some but not all directions in the space of possible expression levels, correlations of expression fluctuations over long distances in the embryo, and departures from a Gaussian distribution of these fluctuations. Analysis of recent experiments on the gap gene network shows that all these signatures are observed, and that the different signatures are related in ways predicted by theory. Although there might be other explanations for these individual phenomena, the confluence of evidence suggests that this genetic network is tuned to criticality.

scholarly journals Pioneer Transcription Factors Initiating Gene Network Changes

2020 ◽  
Vol 54 (1) ◽  
pp. 367-385 ◽  
Author(s):  
Kenneth S. Zaret
Keyword(s):  
Transcription Factors ◽  
Gene Networks ◽  
Gene Network ◽  
Control Cell ◽  
Cellular Reprogramming ◽  
Regulatory Sequence ◽  
Sequence Motifs ◽  
Nucleosome Remodeling ◽  
Pioneer Factors

Pioneer transcription factors have the intrinsic biochemical ability to scan partial DNA sequence motifs that are exposed on the surface of a nucleosome and thus access silent genes that are inaccessible to other transcription factors. Pioneer factors subsequently enable other transcription factors, nucleosome remodeling complexes, and histone modifiers to engage chromatin, thereby initiating the formation of an activating or repressive regulatory sequence. Thus, pioneer factors endow the competence for fate changes in embryonic development, are essential for cellular reprogramming, and rewire gene networks in cancer cells. Recent studies with reconstituted nucleosomes in vitro and chromatin binding in vivo reveal that pioneer factors can directly perturb nucleosome structure and chromatin accessibility in different ways. This review focuses on our current understanding of the mechanisms by which pioneer factors initiate gene network changes and will ultimately contribute to our ability to control cell fates at will.

scholarly journals Reactive oxygen species regulate gene networks of stress response

2004 ◽  
Vol 2 (1) ◽  
pp. 4-12
Author(s):  
Irina L Stepanenko
Keyword(s):  
Reactive Oxygen Species ◽  
Transcription Factors ◽  
Gene Networks ◽  
Gene Network ◽  
Redox Regulation ◽  
Reactive Oxygen ◽  
Regulation Of Transcription ◽  
Oxygen Species ◽  
Integrative System ◽  
Regulate Gene

The GeneNet (gnw/genenet/) accumulate information on reactive oxygen species (ROS) signals and reduction/oxidation (redox) regulation of transcription factors. Redox-regulation gene network is the adaptation to oxidative stress and integrative system of local gene networks via key transcription factors. The cross-talk of signals and the interference of gene networks occur in the integrative gene network

Elucidating cyclic AMP signaling in subcellular domains with optogenetic tools and fluorescent biosensors

2019 ◽  
Vol 47 (6) ◽  
pp. 1733-1747 ◽  
Author(s):  
Christina Klausen ◽  
Fabian Kaiser ◽  
Birthe Stüven ◽  
Jan N. Hansen ◽  
Dagmar Wachten
Keyword(s):  
Cyclic Amp ◽  
Adenosine Monophosphate ◽  
Adenylyl Cyclases ◽  
Temporal Precision ◽  
Fluorescent Biosensors ◽  
Subcellular Domains ◽  
Spatio Temporal ◽  
Hydrolysis Of ◽  
Shed Light ◽  
Cyclic Amp Signaling

The second messenger 3′,5′-cyclic nucleoside adenosine monophosphate (cAMP) plays a key role in signal transduction across prokaryotes and eukaryotes. Cyclic AMP signaling is compartmentalized into microdomains to fulfil specific functions. To define the function of cAMP within these microdomains, signaling needs to be analyzed with spatio-temporal precision. To this end, optogenetic approaches and genetically encoded fluorescent biosensors are particularly well suited. Synthesis and hydrolysis of cAMP can be directly manipulated by photoactivated adenylyl cyclases (PACs) and light-regulated phosphodiesterases (PDEs), respectively. In addition, many biosensors have been designed to spatially and temporarily resolve cAMP dynamics in the cell. This review provides an overview about optogenetic tools and biosensors to shed light on the subcellular organization of cAMP signaling.

“Se non scritto, almen dipinto”: The ethos of becoming in Purgatorio 28–33

2021 ◽  
pp. 001458582110225
Author(s):  
Thomas E Peterson
Keyword(s):  
Learning Process ◽  
Perceptual Experience ◽  
The State ◽  
Central Question ◽  
Central Focus ◽  
Cognitive Limitations ◽  
Before And After ◽  
Analogia Entis ◽  
Spatio Temporal ◽  
Divine Beauty

A central question facing the reader of the Paradiso terrestre (Pg 28–33) concerns the selfhood of the protagonist, the character Dante. While the state of Dante’s soul was critical to the poem’s beginning in the dark wood, and remained implicit through the intervening cantos, it is only in the Paradiso terrestre that it becomes the poem’s central focus. This question is explored in cognitive and theological terms in a sequential reading of the six cantos that elucidates the learning process occurring in the character before and after his confession in Pg 31: in his encounter with Matelda, his sensory and perceptual experience of the procession, his dialogues with Beatrice, and his witnessing of her divine beauty as the analogia entis reflecting the beauty of God. The analysis acknowledges the changes in Dante’s style in this interval, which serves as a fulcrum of the entire Commedia, a spatio-temporal threshold in which the transition of one soul, from confession to redemption to instruction on the divine word, is linked to the destiny of humankind and the prospect of universal salvation. Throughout this process of becoming, the character’s cognitive limitations are exposed, not simply as flaws but as signs of his intrinsic humanity.

Anti-glioma Efficacy and Mechanism of Action of Tripolinolate A from Tripolium pannonicum

Planta Medica ◽  
2018 ◽  
Vol 84 (11) ◽  
pp. 786-794
Author(s):  
Weiyun Chai ◽  
Lu Chen ◽  
Xiao-Yuan Lian ◽  
Zhizhen Zhang
Keyword(s):  
Cell Cycle ◽  
Transcription Factors ◽  
Glioma Cells ◽  
Inhibitory Effect ◽  
Cell Cycle Regulators ◽  
Expression Levels ◽  
Multiple Tumor ◽  
Glioma Growth

AbstractTripolinolate A as a new bioactive phenolic ester was previously isolated from a halophyte of Tripolium pannonicum. However, the in vitro and in vivo anti-glioma effects and mechanism of tripolinolate A have not been investigated. This study has demonstrated that (1) tripolinolate A inhibited the proliferation of different glioma cells with IC50 values of 7.97 to 14.02 µM and had a significant inhibitory effect on the glioma growth in U87MG xenograft nude mice, (2) tripolinolate A induced apoptosis in glioma cells by downregulating the expressions of antiapoptotic proteins and arrested glioma cell cycle at the G2/M phase by reducing the expression levels of cell cycle regulators, and (3) tripolinolate A also remarkably reduced the expression levels of several glioma metabolic enzymes and transcription factors. All data together suggested that tripolinolate A had significant in vitro and in vivo anti-glioma effects and the regulation of multiple tumor-related regulators and transcription factors might be responsible for the activities of tripolinolate A against glioma.

A study of structural properties of gene network graphs for mathematical modeling of integrated mosaic gene networks

2017 ◽  
Vol 15 (02) ◽  
pp. 1650045 ◽  
Author(s):  
Olga V. Petrovskaya ◽  
Evgeny D. Petrovskiy ◽  
Inna N. Lavrik ◽  
Vladimir A. Ivanisenko
Keyword(s):  
Mathematical Modeling ◽  
Gene Regulatory Networks ◽  
Gene Networks ◽  
Gene Network ◽  
Regulatory Networks ◽  
Network Modeling ◽  
Computer Experiments ◽  
Linear Control ◽  
Expression Of Genes ◽  
Gene Regulatory

Gene network modeling is one of the widely used approaches in systems biology. It allows for the study of complex genetic systems function, including so-called mosaic gene networks, which consist of functionally interacting subnetworks. We conducted a study of a mosaic gene networks modeling method based on integration of models of gene subnetworks by linear control functionals. An automatic modeling of 10,000 synthetic mosaic gene regulatory networks was carried out using computer experiments on gene knockdowns/knockouts. Structural analysis of graphs of generated mosaic gene regulatory networks has revealed that the most important factor for building accurate integrated mathematical models, among those analyzed in the study, is data on expression of genes corresponding to the vertices with high properties of centrality.

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