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Stochastic modeling for the expression of a gene regulated by competing transcription factors.

Yang HT, Ko MS - PLoS ONE (2012)

Bottom Line: 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.Therefore, our approach may help to apply stochastic simulations to broader experimental data.

View Article: PubMed Central - PubMed

Affiliation: Developmental Genomics and Aging Section, Laboratory of Genetics, National Institute on Aging, National Institutes of Health (NIH), Baltimore, Maryland, United States of America.

ABSTRACT
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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Model prediction matches more closely to the experimental observation.(a) Simulated dose-response relationship between [dox] and promoter activity (normalized gene induction levels). (b) Hill functions showing estimated switching probabilities (PA1, PA2, PR1, and PR2) against [dox]. Values of PA1+PR1 against [dox] are also shown. To show the relationship between (a) and (b), these graphs are aligned by the [dox]. (c) Comparisons between model predictions and experimental observations.
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pone-0032376-g004: Model prediction matches more closely to the experimental observation.(a) Simulated dose-response relationship between [dox] and promoter activity (normalized gene induction levels). (b) Hill functions showing estimated switching probabilities (PA1, PA2, PR1, and PR2) against [dox]. Values of PA1+PR1 against [dox] are also shown. To show the relationship between (a) and (b), these graphs are aligned by the [dox]. (c) Comparisons between model predictions and experimental observations.

Mentions: To generate an ensemble Hill coefficient from total population responses in the 3-state MCM in the steady state, we found that the 7 [dox] conditions simulated in Figure 3 were not sufficient. We therefore carried out more extensive stochastic simulations and increased the number of [dox] conditions to 34 for the activator only and repressor only conditions, and averaged them to plot the dose-response relationship between [dox] and normalized promoter activities (Figure 4a). As expected, the dose-response relationship followed a sigmoidal curve. Although the parameter optimizations for the dose-response experiments were carried out to have the Hill coefficients for activator alone or repressor alone be close to 1.6 or 1.8 (numbers in green and red in Figure 4c), the results indicate that the 3-state MCM can retain the dose-response characteristics of either activator alone (1.6) or repressor (1.8) alone.


Stochastic modeling for the expression of a gene regulated by competing transcription factors.

Yang HT, Ko MS - PLoS ONE (2012)

Model prediction matches more closely to the experimental observation.(a) Simulated dose-response relationship between [dox] and promoter activity (normalized gene induction levels). (b) Hill functions showing estimated switching probabilities (PA1, PA2, PR1, and PR2) against [dox]. Values of PA1+PR1 against [dox] are also shown. To show the relationship between (a) and (b), these graphs are aligned by the [dox]. (c) Comparisons between model predictions and experimental observations.
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Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3303788&req=5

pone-0032376-g004: Model prediction matches more closely to the experimental observation.(a) Simulated dose-response relationship between [dox] and promoter activity (normalized gene induction levels). (b) Hill functions showing estimated switching probabilities (PA1, PA2, PR1, and PR2) against [dox]. Values of PA1+PR1 against [dox] are also shown. To show the relationship between (a) and (b), these graphs are aligned by the [dox]. (c) Comparisons between model predictions and experimental observations.
Mentions: To generate an ensemble Hill coefficient from total population responses in the 3-state MCM in the steady state, we found that the 7 [dox] conditions simulated in Figure 3 were not sufficient. We therefore carried out more extensive stochastic simulations and increased the number of [dox] conditions to 34 for the activator only and repressor only conditions, and averaged them to plot the dose-response relationship between [dox] and normalized promoter activities (Figure 4a). As expected, the dose-response relationship followed a sigmoidal curve. Although the parameter optimizations for the dose-response experiments were carried out to have the Hill coefficients for activator alone or repressor alone be close to 1.6 or 1.8 (numbers in green and red in Figure 4c), the results indicate that the 3-state MCM can retain the dose-response characteristics of either activator alone (1.6) or repressor (1.8) alone.

Bottom Line: 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.Therefore, our approach may help to apply stochastic simulations to broader experimental data.

View Article: PubMed Central - PubMed

Affiliation: Developmental Genomics and Aging Section, Laboratory of Genetics, National Institute on Aging, National Institutes of Health (NIH), Baltimore, Maryland, United States of America.

ABSTRACT
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.

Show MeSH