Dataset Information

Field1974_Oregonator

ABSTRACT: Field-Noyes Model of BZ Reaction Citation R.J.Field and R.M.Noyes,J.Chem.Phys.60,1877 (1974) Description Field Noyes Version of Belousov-Zhabotinsky Reaction. BrO3 is held constant; HOBr is typically ignored, and can be replaced by an empty-set. The stoichiometry f is typically taken as 1/2 or 1. . Initially Generated by Cellerator Version 1.0 update 2.1220 using Mathematica 4.2 for Mac OS X (June 4, 2002), December 26, 2002 10:43:53, using (PowerMac,PowerPC, Mac OS X,MacOSX,Darwin). author=B.E.Shapiro Modified with SBMLeditor by Nicolas Le Novère, to fit the original article. 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: BIOMD0000000040 | BioModels | 2024-09-02

REPOSITORIES: BioModels

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	BIOMD0000000040?filename=BIOMD0000000040-biopax2.owl	Owl
	BIOMD0000000040?filename=BIOMD0000000040-biopax3.owl	Owl
	BIOMD0000000040?filename=BIOMD0000000040-matlab.m	Other
	BIOMD0000000040?filename=BIOMD0000000040-octave.m	Other
	BIOMD0000000040?filename=BIOMD0000000040.m	Other

Items per page:

1 - 5 of 17

Similar Datasets

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.

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. The parameters given in the article were: promotor strength (repressed) ( tps_repr ): 5*10 -4 transcripts/(promotor*s) promotor strength (full) ( tps_active ): 0.5 transcripts/(promotor*s) mRNA half life, τ 1/2,mRNA : 2 min protein half life, τ 1/2,prot : 10 min K M : 40 monomers/cell Hill coefficient n: 2 From these the following constants can be derived: average mRNA lifetime ( t_ave ): τ 1/2,mRNA /ln(2) = 2.89 min mRNA decay rate ( kd_mRNA ): ln(2)/ τ 1/2,mRNA = 0.347 min -1 protein decay rate ( kd_prot ): ln(2)/ τ 1/2,prot transcription rate ( a_tr ): tps_active*60 = 29.97 transcripts/min transcription rate (repressed) ( a0_tr ): tps_repr*60 = 0.03 transcripts/min translation rate ( k_tl ): eff*kd_mRNA = 6.93 proteins/(mRNA*min) α : a_tr*eff*τ 1/2,prot /(ln(2)*K M ) = 216.4 proteins/(promotor*cell*Km) α 0 : a0_tr*eff*τ 1/2,prot /(ln(2)*K M ) = 0.2164 proteins/(promotor*cell*Km) β : k_dp/k_dm = 0.2 Annotation by the Kinetic Simulation Algorithm Ontology (KiSAO): To reproduce the simulations run published by the authors, the model has to be simulated with any of two different approaches. 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:MAPK cascade in solution (no scaffold) Description This model describes a basic 3- stage Mitogen Activated Protein Kinase (MAPK) cascade in solution. This cascade is typically expressed as RAF= =>MEK==>MAPK (alternative forms are K3==>K2==> K1 and KKK==>KK==>K) . The input signal is RAFK (RAF Kinase) and the output signal is MAPKpp ( doubly phosphorylated form of MAPK) . RAFK phosphorylates RAF once to RAFp. RAFp, the phosphorylated form of RAF induces two phoshporylations of MEK, to MEKp and MEKpp. MEKpp, the doubly phosphorylated form of MEK, induces two phosphorylations of MAPK to MAPKp and MAPKpp. Rate constant Reaction a10 = 5. MAPKPH + MAPKpp -> MAPKppMAPKPH a1 = 1. RAF + RAFK -> RAFRAFK a2 = 0.5 RAFp + RAFPH -> RAFpRAFPH a3 = 3.3 MEK + RAFp -> MEKRAFp a4 = 10. MEKp + MEKPH -> MEKpMEKPH a5 = 3.3 MEKp + RAFp -> MEKpRAFp a6 = 10. MEKPH + MEKpp -> MEKppMEKPH a7 = 20. MAPK + MEKpp -> MAPKMEKpp a8 = 5. MAPKp + MAPKPH -> MAPKpMAPKPH a9 = 20. MAPKp + MEKpp -> MAPKpMEKpp d10 = 0.4 MAPKppMAPKPH -> MAPKPH + MAPKpp d1 = 0.4 RAFRAFK -> RAF + RAFK d2 = 0.5 RAFpRAFPH -> RAFp + RAFPH d3 = 0.42 MEKRAFp -> MEK + RAFp d4 = 0.8 MEKpMEKPH -> MEKp + MEKPH d5 = 0.4 MEKpRAFp -> MEKp + RAFp d6 = 0.8 MEKppMEKPH -> MEKPH + MEKpp d7 = 0.6 MAPKMEKpp -> MAPK + MEKpp d8 = 0.4 MAPKpMAPKPH -> MAPKp + MAPKPH d9 = 0.6 MAPKpMEKpp -> MAPKp + MEKpp k10 = 0.1 MAPKppMAPKPH -> MAPKp + MAPKPH k1 = 0.1 RAFRAFK -> RAFK + RAFp k2 = 0.1 RAFpRAFPH -> RAF + RAFPH k3 = 0.1 MEKRAFp -> MEKp + RAFp k4 = 0.1 MEKpMEKPH -> MEK + MEKPH k5 = 0.1 MEKpRAFp -> MEKpp + RAFp k6 = 0.1 MEKppMEKPH -> MEKp + MEKPH k7 = 0.1 MAPKMEKpp -> MAPKp + MEKpp k8 = 0.1 MAPKpMAPKPH -> MAPK + MAPKPH k9 = 0.1 MAPKpMEKpp -> MAPKpp + MEKpp Variable IC ODE MAPK 0.3 MAPK'[t] == d7*MAPKMEKpp[t] + k8*MAPKpMAPKPH[t] - a7*MAPK[t]*MEKpp[t] MAPKMEKpp 0 MAPKMEKpp'[t] == -(d7*MAPKMEKpp[t]) - k7*MAPKMEKpp[t] + a7*MAPK[t]*MEKpp[t] MAPKp 0 MAPKp'[t] == k7*MAPKMEKpp[t] - a8*MAPKp[t]*MAPKPH[t] + d8*MAPKpMAPKPH[t] + d9*MAPKpMEKpp[t] + k10* MAPKppMAPKPH[t] - a9*MAPKp[t]*MEKpp[t] MAPKPH 0.3 MAPKPH'[t] == -(a8*MAPKp[t]*MAPKPH[t]) + d8*MAPKpMAPKPH[ t] + k8*MAPKpMAPKPH[t] - a10*MAPKPH[t]*MAPKpp[t] + d10*MAPKppMAPKPH[t] + k10*MAPKppMAPKPH[t] MAPKpMAPKPH 0 MAPKpMAPKPH'[t] == a8*MAPKp[t]*MAPKPH[t] - d8* MAPKpMAPKPH[t] - k8*MAPKpMAPKPH[t] MAPKpMEKpp 0 MAPKpMEKpp'[t] == -(d9*MAPKpMEKpp[t]) - k9*MAPKpMEKpp[t] + a9*MAPKp[t]*MEKpp[t] MAPKpp 0 MAPKpp'[t] == k9*MAPKpMEKpp[t] - a10*MAPKPH[t]*MAPKpp[t] + d10*MAPKppMAPKPH[t] MAPKppMAPKPH 0 MAPKppMAPKPH'[t] == a10*MAPKPH[t]*MAPKpp[t] - d10* MAPKppMAPKPH[t] - k10*MAPKppMAPKPH[t] MEK 0.2 MEK'[t] == k4*MEKpMEKPH[t] + d3*MEKRAFp[t] - a3*MEK[t]*RAFp[t] MEKp 0 MEKp'[t] == -(a4*MEKp[t]*MEKPH[t]) + d4*MEKpMEKPH[t] + k6*MEKppMEKPH[t] + d5*MEKpRAFp[t] + k3*MEKRAFp[ t] - a5*MEKp[t]*RAFp[t] MEKPH 0.2 MEKPH'[t] == -(a4*MEKp[t]*MEKPH[t]) + d4*MEKpMEKPH[t] + k4*MEKpMEKPH[t] - a6*MEKPH[t]*MEKpp[t] + d6* MEKppMEKPH[t] + k6*MEKppMEKPH[t] MEKpMEKPH 0 MEKpMEKPH'[t] == a4*MEKp[t]*MEKPH[t] - d4*MEKpMEKPH[t] - k4*MEKpMEKPH[t] MEKpp 0 MEKpp'[t] == d7*MAPKMEKpp[t] + k7*MAPKMEKpp[t] + d9*MAPKpMEKpp[t] + k9*MAPKpMEKpp[t] - a7*MAPK[t]* MEKpp[t] - a9*MAPKp[t]*MEKpp[t] - a6*MEKPH[t]*MEKpp[t] + d6*MEKppMEKPH[t] + k5*MEKpRAFp[t] MEKppMEKPH 0 MEKppMEKPH'[t] == a6*MEKPH[t]*MEKpp[t] - d6*MEKppMEKPH[ t] - k6*MEKppMEKPH[t] MEKpRAFp 0 MEKpRAFp'[t] == -(d5*MEKpRAFp[t]) - k5*MEKpRAFp[t] + a5*MEKp[t]*RAFp[t] MEKRAFp 0 MEKRAFp'[t] == -(d3*MEKRAFp[t]) - k3*MEKRAFp[t] + a3*MEK[t]*RAFp[t] RAF 0.4 RAF'[t] == -(a1*RAF[t]*RAFK[t]) + k2*RAFpRAFPH[t] + d1*RAFRAFK[t] RAFK 0.1 RAFK'[t] == -(a1*RAF[t]*RAFK[t]) + d1*RAFRAFK[t] + k1*RAFRAFK[t] RAFp 0 RAFp'[t] == d5*MEKpRAFp[t] + k5*MEKpRAFp[t] + d3*MEKRAFp[t] + k3*MEKRAFp[t] - a3*MEK[t]*RAFp[t] - a5*MEKp[t]*RAFp[t] - a2*RAFp[t]*RAFPH[t] + d2* RAFpRAFPH[t] + k1*RAFRAFK[t] RAFPH 0.3 RAFPH'[t] == -(a2*RAFp[t]*RAFPH[t]) + d2*RAFpRAFPH[t] + k2*RAFpRAFPH[t] RAFpRAFPH 0 RAFpRAFPH'[t] == a2*RAFp[t]*RAFPH[t] - d2*RAFpRAFPH[t] - k2*RAFpRAFPH[t] RAFRAFK 0 RAFRAFK'[t] == a1*RAF[t]*RAFK[t] - d1*RAFRAFK[t] - k1*RAFRAFK[t] Generated by Cellerator Version 1.4.3 (6-March-2004) using Mathematica 5.0 for Mac OS X (November 19, 2003), March 6, 2004 12:18:07, using (PowerMac, PowerPC,Mac OS X,MacOSX,Darwin) author=B.E.Shapiro This model originates from BioModels Database: A Database of Annotated Published Models (http://www.ebi.ac.uk/biomodels/). It is copyright (c) 2005-2010 The BioModels.net Team. For more information see the terms of use . 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.

Dataset Information

Field1974_Oregonator

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets