Dataset Information

Karapetyan2016 - Genetic oscillatory network - Repressor Titration Circuit (RTC)

ABSTRACT: Karapetyan2016 - Genetic oscillatory network - Repressor Titration Circuit (RTC) This model is described in the article: Role of DNA binding sites and slow unbinding kinetics in titration-based oscillators. Karapetyan S, Buchler NE. Phys Rev E Stat Nonlin Soft Matter Phys 2015 Dec; 92(6-1): 062712 Abstract: Genetic oscillators, such as circadian clocks, are constantly perturbed by molecular noise arising from the small number of molecules involved in gene regulation. One of the strongest sources of stochasticity is the binary noise that arises from the binding of a regulatory protein to a promoter in the chromosomal DNA. In this study, we focus on two minimal oscillators based on activator titration and repressor titration to understand the key parameters that are important for oscillations and for overcoming binary noise. We show that the rate of unbinding from the DNA, despite traditionally being considered a fast parameter, needs to be slow to broaden the space of oscillatory solutions. The addition of multiple, independent DNA binding sites further expands the oscillatory parameter space for the repressor-titration oscillator and lengthens the period of both oscillators. This effect is a combination of increased effective delay of the unbinding kinetics due to multiple binding sites and increased promoter ultrasensitivity that is specific for repression. We then use stochastic simulation to show that multiple binding sites increase the coherence of oscillations by mitigating the binary noise. Slow values of DNA unbinding rate are also effective in alleviating molecular noise due to the increased distance from the bifurcation point. Our work demonstrates how the number of DNA binding sites and slow unbinding kinetics, which are often omitted in biophysical models of gene circuits, can have a significant impact on the temporal and stochastic dynamics of genetic oscillators. This model is hosted on BioModels Database and identified by: BIOMD0000000587. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

SUBMITTER: Sargis Karapetyan

PROVIDER: BIOMD0000000587 | BioModels | 2024-09-02

REPOSITORIES: BioModels

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			Action	DRS
	BIOMD0000000587?filename=BIOMD0000000587-biopax2.owl	Owl
	BIOMD0000000587?filename=BIOMD0000000587-biopax3.owl	Owl
	BIOMD0000000587?filename=BIOMD0000000587-matlab.m	Other
	BIOMD0000000587?filename=BIOMD0000000587-octave.m	Other
	BIOMD0000000587?filename=BIOMD0000000587.m	Other

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Publications

Role of DNA binding sites and slow unbinding kinetics in titration-based oscillators.

Karapetyan Sargis S Buchler Nicolas E NE

Physical review. E, Statistical, nonlinear, and soft matter physics 20151222 6

Genetic oscillators, such as circadian clocks, are constantly perturbed by molecular noise arising from the small number of molecules involved in gene regulation. One of the strongest sources of stochasticity is the binary noise that arises from the binding of a regulatory protein to a promoter in the chromosomal DNA. In this study, we focus on two minimal oscillators based on activator titration and repressor titration to understand the key parameters that are important for oscillations and for ...[more]

PMID: 26764732

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Project description:Karapetyan2016 - Genetic oscillatory network - Activator Titration Circuit (ATC) This model is described in the article: Role of DNA binding sites and slow unbinding kinetics in titration-based oscillators. Karapetyan S, Buchler NE. Phys Rev E Stat Nonlin Soft Matter Phys 2015 Dec; 92(6-1): 062712 Abstract: Genetic oscillators, such as circadian clocks, are constantly perturbed by molecular noise arising from the small number of molecules involved in gene regulation. One of the strongest sources of stochasticity is the binary noise that arises from the binding of a regulatory protein to a promoter in the chromosomal DNA. In this study, we focus on two minimal oscillators based on activator titration and repressor titration to understand the key parameters that are important for oscillations and for overcoming binary noise. We show that the rate of unbinding from the DNA, despite traditionally being considered a fast parameter, needs to be slow to broaden the space of oscillatory solutions. The addition of multiple, independent DNA binding sites further expands the oscillatory parameter space for the repressor-titration oscillator and lengthens the period of both oscillators. This effect is a combination of increased effective delay of the unbinding kinetics due to multiple binding sites and increased promoter ultrasensitivity that is specific for repression. We then use stochastic simulation to show that multiple binding sites increase the coherence of oscillations by mitigating the binary noise. Slow values of DNA unbinding rate are also effective in alleviating molecular noise due to the increased distance from the bifurcation point. Our work demonstrates how the number of DNA binding sites and slow unbinding kinetics, which are often omitted in biophysical models of gene circuits, can have a significant impact on the temporal and stochastic dynamics of genetic oscillators. This model is hosted on BioModels Database and identified by: BIOMD0000000586. To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Project description:Begitt2014 - STAT1 cooperative DNA binding - double GAS polymer model The importance of STAT1-cooperative DNA binding in type 1 and type 2 interferon signalling has been studies using experimental and modelling approaches. The authors have developed two ODE models to describe STAT1 binding to short promoter regions of DNA, namely "single GAS polymer model" and "double GAS polymer model" considering binding to single or double GAS sites, respectively. The length of DNA in the single GAS model was three sites and four sites in double GAS model. This model correspond to the "double GAS polymer model". This model is described in the article: STAT1-cooperative DNA binding distinguishes type 1 from type 2 interferon signaling. Begitt A, Droescher M, Meyer T, Schmid CD, Baker M, Antunes F, Owen MR, Naumann R, Decker T, Vinkemeier U Nat Immunol. 2014 Feb;15(2):168-76. Abstract: STAT1 is an indispensable component of a heterotrimer (ISGF3) and a STAT1 homodimer (GAF) that function as transcription regulators in type 1 and type 2 interferon signaling, respectively. To investigate the importance of STAT1-cooperative DNA binding, we generated gene-targeted mice expressing cooperativity-deficient STAT1 with alanine substituted for Phe77. Neither ISGF3 nor GAF bound DNA cooperatively in the STAT1F77A mouse strain, but type 1 and type 2 interferon responses were affected differently. Type 2 interferon-mediated transcription and antibacterial immunity essentially disappeared owing to defective promoter recruitment of GAF. In contrast, STAT1 recruitment to ISGF3 binding sites and type 1 interferon-dependent responses, including antiviral protection, remained intact. We conclude that STAT1 cooperativity is essential for its biological activity and underlies the cellular responses to type 2, but not type 1 interferon. This model is hosted on BioModels Database and identified by: BIOMD0000000501 . To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.

Project description:Begitt2014 - STAT1 cooperative DNA binding - single GAS polymer model The importance of STAT1-cooperative DNA binding in type 1 and type 2 interferon signalling has been studies using experimental and modelling approaches. The authors have developed two ODE models to describe STAT1 binding to short promoter regions of DNA, namely "single GAS polymer model" and "double GAS polymer model" considering binding to single or double GAS sites, respectively. The length of DNA in the single GAS model was three sites and four sites in double GAS model. This model correspond to the "single GAS polymer model". This model is described in the article: STAT1-cooperative DNA binding distinguishes type 1 from type 2 interferon signaling. Begitt A, Droescher M, Meyer T, Schmid CD, Baker M, Antunes F, Owen MR, Naumann R, Decker T, Vinkemeier U Nat Immunol. 2014 Feb;15(2):168-76. Abstract: STAT1 is an indispensable component of a heterotrimer (ISGF3) and a STAT1 homodimer (GAF) that function as transcription regulators in type 1 and type 2 interferon signaling, respectively. To investigate the importance of STAT1-cooperative DNA binding, we generated gene-targeted mice expressing cooperativity-deficient STAT1 with alanine substituted for Phe77. Neither ISGF3 nor GAF bound DNA cooperatively in the STAT1F77A mouse strain, but type 1 and type 2 interferon responses were affected differently. Type 2 interferon-mediated transcription and antibacterial immunity essentially disappeared owing to defective promoter recruitment of GAF. In contrast, STAT1 recruitment to ISGF3 binding sites and type 1 interferon-dependent responses, including antiviral protection, remained intact. We conclude that STAT1 cooperativity is essential for its biological activity and underlies the cellular responses to type 2, but not type 1 interferon. This model is hosted on BioModels Database and identified by: BIOMD0000000500 . To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models . To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide. Please refer to CC0 Public Domain Dedication for more information.