An Analytical Rate Expression for the Kinetics of Gene Transcription Mediated by Dimeric Transcription Factors

Yang, Hsih-Te; Hsu, Chao‐Ping; Hwang, Ming‐Jing

doi:10.1093/jb/mvm151

Cited by 11 publications

(10 citation statements)

References 31 publications

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“…Gene-regulatory function (GRF) is proposed to quantify promoter activity or gene expression by formulating the non-linear function of TF concentration [27], [28], [36]. In general, GRF is experimentally measured as a sigmoidal dose-response curve, which can be mathematically expressed as the Hill function (Eq.…”

Section: Methodsmentioning

confidence: 99%

Stochastic Modeling for the Expression of a Gene Regulated by Competing Transcription Factors

Yang¹,

2012

PLoS ONE

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It is widely accepted that gene expression regulation is a stochastic event. The common approach for its computer simulation requires detailed information on the interactions of individual molecules, which is often not available for the analyses of biological experiments. As an alternative approach, we employed a more intuitive model to simulate the experimental result, the Markov-chain model, in which a gene is regulated by activators and repressors, which bind the same site in a mutually exclusive manner. Our stochastic simulation in the presence of both activators and repressors predicted a Hill-coefficient of the dose-response curve closer to the experimentally observed value than the calculated value based on the simple additive effects of activators alone and repressors alone. The simulation also reproduced the heterogeneity of gene expression levels among individual cells observed by Fluorescence Activated Cell Sorting analysis. Therefore, our approach may help to apply stochastic simulations to broader experimental data.

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Section: Methodsmentioning

confidence: 99%

Stochastic Modeling for the Expression of a Gene Regulated by Competing Transcription Factors

Yang¹,

2012

PLoS ONE

Self Cite

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“…A typical choice of sigmoid is the Hill function C .x/ D 1=OE1 C .Á=x/ d [19,20]. For any choice of the cooperativity parameter d > 1 and the threshold parameter Á > 0, this function increases monotonically from 0 to 1, satisfies C .Á/ D 1=2, and d C dx .Á/ > 0 increases with d .…”

Section: Definitionmentioning

confidence: 99%

Invalidation of the structure of genetic network dynamics: a geometric approach

Porreca

Cinquemani

Lygeros

et al. 2012

Intl J Robust & Nonlinear

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International audienceThis work concerns the identification of the structure of a genetic network model from measurements of gene product concentrations and synthesis rates. In earlier work, we developed a data preprocessing algorithm that is able to reject many hypotheses on the network structure by testing certain monotonicity properties for a wide family of network models. Here, we develop a geometric interpretation of the method. Then, for a relevant subclass of genetic network models, we extend our approach to the combined testing of monotonicity and convexity-like properties associated with the network structures. The theoretical aspects and practical performance of the enhanced methods are illustrated by way of numerical results

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“…At this stage, we are not concerned with the form of γ i (x). In general [5], b i (x) is a combination (weighted sums and products) of so-called Hill functions [29], [30], [31], i.e. sigmoid-shaped functions…”

Section: B Kinetic Network Models With Unate Structurementioning

confidence: 99%

Structural identification of unate-like genetic network models from time-lapse protein concentration measurements

Porreca

Cinquemani

Lygeros

et al. 2010

49th IEEE Conference on Decision and Control (CDC)

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We consider the problem of learning dynamical models of genetic regulatory networks from time-lapse measurements of gene expression. In our previous work [1], we described a method for the structural and parametric identification of ODE models that makes use of concurrent measurements of concentrations and synthesis rates of the gene products, and requires the knowledge of the noise statistics. In this paper we assume all these pieces of information are not simultaneously available. In particular we propose extensions of [1] that make the method applicable to protein concentration measurements only. We discuss the performance of the method on experimental data from the network IRMA, a benchmark synthetic network engineered in yeast Saccharomices cerevisiae. Abstract-We consider the problem of learning dynamical models of genetic regulatory networks from time-lapse measurements of gene expression. In our previous work [1], we described a method for the structural and parametric identification of ODE models that makes use of concurrent measurements of concentrations and synthesis rates of the gene products, and requires the knowledge of the noise statistics. In this paper we assume all these pieces of information are not simultaneously available. In particular we propose extensions of [1] that make the method applicable to protein concentration measurements only. We discuss the performance of the method on experimental data from the network IRMA, a benchmark synthetic network engineered in yeast Saccharomices cerevisiae.

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An Analytical Rate Expression for the Kinetics of Gene Transcription Mediated by Dimeric Transcription Factors

Cited by 11 publications

References 31 publications

Stochastic Modeling for the Expression of a Gene Regulated by Competing Transcription Factors

Stochastic Modeling for the Expression of a Gene Regulated by Competing Transcription Factors

Invalidation of the structure of genetic network dynamics: a geometric approach

Structural identification of unate-like genetic network models from time-lapse protein concentration measurements

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