Dataset Information

Ueda2001_CircClock

ABSTRACT: Bruce Shapiro: Generated by Cellerator Version 1.0 update 3.0303 using Mathematica 4.1 for Microsoft Windows (June 13, 2001), April 2, 2003 16:49:13, using (PC,x86, Microsoft Windows,WindowsNT,Windows) Bruce Shapiro: Corrected 29 March 2005 Nicolas Le Novère: Added Dbt and Cyc species, and the corresponding reactions. 23 April 2005 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. In summary, you are entitled to use this encoded model in absolutely any manner you deem suitable, verbatim, or with modification, alone or embedded it in a larger context, redistribute it, commercially or not, in a restricted way or not. To cite BioModels Database, please use: Li C, Donizelli M, Rodriguez N, Dharuri H, Endler L, Chelliah V, Li L, He E, Henry A, Stefan MI, Snoep JL, Hucka M, Le Novère N, Laibe C (2010) BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models. BMC Syst Biol., 4:92.

SUBMITTER: Nicolas Le Novère

PROVIDER: BIOMD0000000022 | BioModels | 2024-09-02

REPOSITORIES: BioModels

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Dataset's files

Source:

			Action	DRS
	BIOMD0000000022?filename=BIOMD0000000022-biopax2.owl	Owl
	BIOMD0000000022?filename=BIOMD0000000022-biopax3.owl	Owl
	BIOMD0000000022?filename=BIOMD0000000022-matlab.m	Other
	BIOMD0000000022?filename=BIOMD0000000022-octave.m	Other
	BIOMD0000000022?filename=BIOMD0000000022.m	Other

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Publications

Robust oscillations within the interlocked feedback model of Drosophila circadian rhythm.

Ueda H R HR Hagiwara M M Kitano H H

Journal of theoretical biology 20010601 4

A mechanism for generating circadian rhythms has been of major interest in recent years. After the discovery of per and tim, a model with a simple feedback loop involving per and tim has been proposed. However, it is recognized that the simple feedback model cannot account for phenotypes generated by various mutants. A recent report by Glossop, Lyons & Hardin [Science286, 766 (1999)] on Drosophila suggests involvement of another feedback loop by dClk that is interlocked with per-tim feedback loo ...[more]

PMID: 11403560

Publication: 1/2

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Project description:Elowitz2000 - Repressilator This model describes the deterministic version of the repressilator system. The authors of this model (see reference) use three transcriptional repressor systems that are not part of any natural biological clock to build an oscillating network that they called the repressilator. The model system was induced in Escherichia coli. In this system, LacI (variable X is the mRNA, variable PX is the protein) inhibits the tetracycline-resistance transposon tetR (Y, PY describe mRNA and protein). Protein tetR inhibits the gene Cl from phage Lambda (Z, PZ: mRNA, protein),and protein Cl inhibits lacI expression. With the appropriate parameter values this system oscillates. This model is described in the article: A synthetic oscillatory network of transcriptional regulators. Elowitz MB, Leibler S. Nature. 2000 Jan; 403(6767):335-338 Abstract: Networks of interacting biomolecules carry out many essential functions in living cells, but the 'design principles' underlying the functioning of such intracellular networks remain poorly understood, despite intensive efforts including quantitative analysis of relatively simple systems. Here we present a complementary approach to this problem: the design and construction of a synthetic network to implement a particular function. We used three transcriptional repressor systems that are not part of any natural biological clock to build an oscillating network, termed the repressilator, in Escherichia coli. The network periodically induces the synthesis of green fluorescent protein as a readout of its state in individual cells. The resulting oscillations, with typical periods of hours, are slower than the cell-division cycle, so the state of the oscillator has to be transmitted from generation to generation. This artificial clock displays noisy behaviour, possibly because of stochastic fluctuations of its components. Such 'rational network design may lead both to the engineering of new cellular behaviours and to an improved understanding of naturally occurring networks. The model is based upon the equations in Box 1 of the paper; however, these equations as printed are dimensionless, and the correct dimensions have been returned to the equations, and the parameters set to reproduce Figure 1C (left). The original model was generated by B.E. Shapiro using Cellerator version 1.0 update 2.1127 using Mathematica 4.2 for Mac OS X (June 4, 2002), November 27, 2002 12:15:32, using (PowerMac,PowerPC, Mac OS X,MacOSX,Darwin). Nicolas Le Novere provided a corrected version generated by SBMLeditor on Sun Aug 20 00:44:05 BST 2006. This removed the EmptySet species. Ran fine on COPASI 4.0 build 18. Bruce Shapiro revised the model with SBMLeditor on 23 October 2006 20:39 PST. This defines default units and correct reactions. The original Cellerator reactions while being mathematically correct did not accurately reflect the intent of the authors. The original notes were mostly removed because they were mostly incorrect in the revised version. Tested with MathSBML 2.6.0. Nicolas Le Novere changed the volume to 1 cubic micrometre, to allow for stochastic simulation. Changed by Lukas Endler to use the average livetime of mRNA instead of its halflife and a corrected value of alpha and alpha0. Moreover, the equations used in this model were clarified, cf. below. The equations given in box 1 of the original publication are rescaled in three respects (lowercase letters denote the rescaled, uppercase letters the unscaled number of molecules per cell): the time is rescaled to the average mRNA lifetime, t_ave: τ = t/t_ave the mRNA concentration is rescaled to the translation efficiency eff: m = M/eff the protein concentration is rescaled to Km: p = P/Km α in the equations should be in units of rescaled proteins per promotor and cell, and β is the ratio of the protein to the mRNA decay rates or the ratio of the mRNA to the protein halflife. In this version of the model α and β are calculated correspondingly to the article, while p and m where just replaced by P/Km resp. M/eff and all equations multiplied by 1/t_ave . Also, to make the equations easier to read, commonly used variables derived from the parameters given in the article by simple rules were introduced. 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First, one could use a deterministic method ( KISAO_0000035 ) with continuous variables ( KISAO_0000018 ). One sample algorithm to use is the CVODE solver ( KISAO_0000019 ). Second, one could simulate the system using Gillespie's direct method ( KISAO_0000029 ), which is a stochastic method ( KISAO_0000036 ) supporting adaptive timesteps ( KISAO_0000041 ) and using discrete variables ( KISAO_0000016 ). This model is hosted on BioModels Database and identified by: BIOMD0000000012 . 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:A commercially available product containing three probiotic bacterial strains (Lactobacillus helveticus R0052, Bifidobacterium longum subsp. infantis R0033, and Bifidobacterium bifidum R0071) was previously shown in animal trials to modulate both TH1 and TH2 immune responses. Clinical studies on this combination of bacteria have also shown positive health effects against seasonal winter diseases and rotavirus infection. The goal of this study was to use a well-established in vitro intestinal epithelial (HT-29) cell model that has been shown to constitutively express double-stranded RNA (dsRNA) sensors (Toll-like receptor 3[TLR3], retinoic acid-inducible gene I, melanoma differentiation-associated gene 5, and dsRNA-activated protein kinase). By using the HT-29 cell model, we wanted to evaluate whether or not this combination of three bacteria had the capacity to immune modulate the host cell response to a dsRNA ligand, poly(IM-bM-^@M-"C). Using a custom-designed, two-color expression microarray targeting genes of the human immune system, we investigated the response of HT-29 cells challenged with poly(IM-bM-^@M-"C) both in the presence and in the absence of the three probiotic bacteria. We observed that the combination of the three bacteria had a major impact on attenuating the expression of genes connected to proinflammatory TH1 and antiviral innate immune responses compared to that obtained by the poly(IM-bM-^@M-"C)-only challenge. Major pathways through which the multistrain combination may be eliciting its immune-modulatory effect include the TLR3-TRIF, mitogen-activated protein kinase, and NF-KB signaling pathways.Such a model may be useful for selecting potential biomarkers for the design of future clinical trials The overall design consisted of 3 samples of HT-29 cells treated with poly (I:C)-only, probiotic combination (PC) and poly (I:C) + PC versus unchallenged HT-29 cells. A minimum of four dye-swap hybridizations (4 biological replicates) were performed for each of the 3 samples analyzed. Unchallenged HT-29 cells were controls and challenged HT-29 cells with poly (I:C)-only, PC-only and poly (I:C) + PC were treated samples.

Project description:Minimal Model for Circadian Oscillations Citation Vilar JMG, Kueh HY, Barkai N, Leibler S, (2002) . Mechanisms of noise resistance in genetic oscillators, PNAS, 99(9):5988-5992. http://www.pnas.org/cgi/content/abstract/ 99/9/5988 Description A minimal model of genomically based oscillation, based on two mutually interacting genes, an activator and a repressor. Postive feedback is provided by the activator protein, which binds to the promotors of both the activator and the repressor genes. Negative feedback is provided by the repressor protein which binds to the activator protein. Rate constant Reaction alphaA = 50 DA -> DA + MA alphaAp = 500 DAp -> DAp + MA alphaR = 0.01 DR -> DR + MR alphaRp = 50 DRp -> DRp + MR betaA = 50 MA -> A + MA betaR = 5 MR -> MR + R gammaA = 1 A + DA -> DAp gammaC = 2 A + R -> C gammaR = 1 A + DR -> DRp deltaA = 1 A -> EmptySet deltaA = 1 C -> R deltaMA = 10 MA -> EmptySet deltaMR = 0.5 MR -> EmptySet deltaR = 0.2 R -> EmptySet thetaA = 50 DAp -> A + DA thetaR = 100 DRp -> A + DR Variable IC ODE A 0 A'[t] == -(deltaA*A[t]) - gammaA*A[t]*DA[t] + thetaA*DAp[ t] - gammaR*A[t]*DR[t] + thetaR*DRp[t] + betaA*MA[t] - gammaC*A[t]*R[t] C 0 C'[t] == -(deltaA*C[t]) + gammaC*A[t]*R[t] DA 1 DA'[t] == -(gammaA*A[t]*DA[t]) + thetaA*DAp[t] DAp 0 DAp'[t] == gammaA*A[t]*DA[t] - thetaA*DAp[t] DR 1 DR'[t] == -(gammaR*A[t]*DR[t]) + thetaR*DRp[t] DRp 0 DRp'[t] == gammaR*A[t]*DR[t] - thetaR*DRp[t] MA 0 MA'[t] == alphaA*DA[t] + alphaAp*DAp[t] - deltaMA*MA[t] MR 0 MR'[t] == alphaR*DR[t] + alphaRp*DRp[t] - deltaMR*MR[t] R 0 R'[t] == deltaA*C[t] + betaR*MR[t] - deltaR*R[t] - gammaC*A[t]*R[t] Generated by Cellerator Version 1.0 update 2.1127 using Mathematica 4.2 for Mac OS X (June 4, 2002), November 27, 2002 12:17:46, using (PowerMac,PowerPC, Mac OS X,MacOSX,Darwin) author=B.E.Shapiro This model originates from BioModels Database: A Database of Annotated Published Models. It is copyright (c) 2005-2009 The BioModels Team. For more information see the terms of use . To cite BioModels Database, please use Le Novère N., Bornstein B., Broicher A., Courtot M., Donizelli M., Dharuri H., Li L., Sauro H., Schilstra M., Shapiro B., Snoep J.L., Hucka M. (2006) BioModels Database: A Free, Centralized Database of Curated, Published, Quantitative Kinetic Models of Biochemical and Cellular Systems Nucleic Acids Res., 34: D689-D691.

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