{"id":552,"date":"2013-01-10T10:00:28","date_gmt":"2013-01-10T09:00:28","guid":{"rendered":"https:\/\/project.inria.fr\/plasma-lab\/?page_id=552"},"modified":"2015-03-17T17:48:43","modified_gmt":"2015-03-17T16:48:43","slug":"genetic-oscillator","status":"publish","type":"page","link":"https:\/\/project.inria.fr\/plasma-lab\/examples\/genetic-oscillator\/","title":{"rendered":"Genetic oscillator"},"content":{"rendered":"

<\/p>\n

Introduction<\/h3>\n

To measure the performance of PLASMA’s simulation engine applied to systems biology, we constructed plausible biological models of increasing complexity using the genetic oscillator of (Vilar, Kueh, Barkai, Leibler 2002<\/a>) as a building block.<\/p>\n

\"\"<\/a>

Genetic oscillator<\/p><\/div>\n

\n
\n

<\/h3>\n

Algorithm<\/h3>\n

Here is the genosc1 model<\/strong>. This model is in the Reactive Module Languages.<\/p>\n<\/div>\n

Important!<\/strong><\/p><\/p>\n

\/\/ Based on genetic oscillator of Vilar, Kueh, Barkai, Leibler PNAS 2002<\/p>\n

ctmc
\nconst int MAX_VALUE = 10000;
\nconst int alpha_A = 50;
\nconst int Alpha_A = 500;
\nconst double alpha_R = 0.01;
\nconst int Alpha_R = 50;
\nconst int beta_A = 50;
\nconst int beta_R = 5;
\nconst int delta_MA = 10;
\nconst double delta_MR = 0.5;
\nconst int delta_A = 1;
\nconst double delta_R = 0.2;
\nconst int gamma_A = 1;
\nconst int gamma_R = 1;
\nconst int gamma_C = 2;
\nconst int theta_A = 50;
\nconst int theta_R = 100;<\/p>\n

module genosc1
\nDA : [0..MAX_VALUE] init 1;
\nD_A : [0..MAX_VALUE] init 0;
\nDR : [0..MAX_VALUE] init 1;
\nD_R : [0..MAX_VALUE] init 0;
\nMA : [0..MAX_VALUE] init 0;
\nMR : [0..MAX_VALUE] init 0;
\nA : [0..MAX_VALUE] init 0;
\nR : [0..MAX_VALUE] init 0;
\nC : [0..MAX_VALUE] init 0;<\/p>\n

[] true -> DA*alpha_A : (MA’=MA+1);
\n[] true -> A*DA*gamma_A : (DA’=DA-1) & (D_A’=D_A+1) & (A’=A-1);
\n[] true -> D_A*Alpha_A : (MA’=MA+1);
\n[] true -> DR*alpha_R : (MR’=MR+1);
\n[] true -> D_R*Alpha_R : (MR’=MR+1);
\n[] true -> MA*beta_A : (A’=A+1);
\n[] true -> MR*beta_R : (R’=R+1);
\n[] true -> A*R*gamma_C : (A’=A-1) & (R’=R-1) & (C’=C+1);
\n[] true -> C*delta_A : (R’=R+1) & (C’=C-1);
\n[] true -> A*delta_A : (A’=A-1);
\n[] true -> R*delta_R : (R’=R-1);
\n[] true -> MA*delta_MA : (MA’=MA-1);
\n[] true -> MR*delta_MR : (MR’=MR-1);
\n[] true -> D_R*theta_R : (DR’=DR+1) & (D_R’=D_R-1) & (A’=A+1);
\n[] true -> A*DR*gamma_R : (DR’=DR-1) & (D_R’=D_R+1) & (A’=A-1);
\n[] true -> D_A*theta_A : (DA’=DA+1) & (D_A’=D_A-1) & (A’=A+1);
\nendmodule<\/p>\n

<\/div>\n

Download : genosc.plasma<\/a><\/strong><\/div>\n
<\/div>\n
\n

Here is our model in Biological language<\/strong>.<\/p>\n

Important!<\/strong><\/p><\/p>\n

constant alphaA=50,alpha_A=500,alphaR=0.01,alpha_R=50,betaA=50,betaR=5\u00a0\u00a0 \u00a0\/\/ enzymatic
\nconstant gammaA=1,gammaR=1,gammaC=2\u00a0\u00a0 \u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0 \u00a0\u00a0\u00a0 \u00a0\/\/ bimolecular
\nconstant deltaA=1,deltaR=0.2,deltaMA=10,deltaMR=0.5,thetaA=50,thetaR=100\/\/ decay<\/p>\n

species DA=1,D_A,DR=1,D_R,MA,MR,A,R,C<\/p>\n

A + DA gammaA-> D_A
\nD_A thetaA-> A + DA
\nA + DR gammaR-> D_R
\nD_R thetaR-> A + DR
\nD_A alpha_A-> MA + D_A
\nDA alphaA-> MA + DA
\nD_R alpha_R-> MR + D_R
\nDR alphaR-> MR + DR
\nMA betaA-> MA + A
\nMR betaR-> MR + R
\nA + R gammaC-> C
\nC \/\/deltaA
\n0.5-> R
\nA deltaA-> *
\nR deltaR-> *
\nMA deltaMA-> *
\nMR deltaMR-> *
\n\/\/plot A,R,C
\n<\/div>\n

Download : genosc.plasma<\/a><\/strong><\/p>\n<\/div>\n

\n
\n

<\/h3>\n

Simulation<\/h3>\n<\/div>\n
After having opened this project in Plasma Lab you can simulate one instance of this model by choosing the Simulate<\/strong> method. Set the number of step to 250<\/strong> with no step time specified.<\/div>\n
<\/div>\n
Plasma Lab will then simulate the selected model for 250 time units (the models are CTMC) and record a data point every 1 time unit.<\/div>\n
<\/div>\n

<\/p>","protected":false},"excerpt":{"rendered":"

Introduction To measure the performance of PLASMA’s simulation engine applied to systems biology, we constructed plausible biological models of increasing complexity using the genetic oscillator of (Vilar, Kueh, Barkai, Leibler 2002) as a building block. Algorithm Here is the genosc1 model. This model is in the Reactive Module Languages. Download\u2026<\/p>\n

Continue reading<\/span><\/i><\/a> <\/p>\n","protected":false},"author":234,"featured_media":0,"parent":236,"menu_order":0,"comment_status":"closed","ping_status":"open","template":"","meta":{"footnotes":""},"class_list":["post-552","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/project.inria.fr\/plasma-lab\/wp-json\/wp\/v2\/pages\/552","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/project.inria.fr\/plasma-lab\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/project.inria.fr\/plasma-lab\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/project.inria.fr\/plasma-lab\/wp-json\/wp\/v2\/users\/234"}],"replies":[{"embeddable":true,"href":"https:\/\/project.inria.fr\/plasma-lab\/wp-json\/wp\/v2\/comments?post=552"}],"version-history":[{"count":22,"href":"https:\/\/project.inria.fr\/plasma-lab\/wp-json\/wp\/v2\/pages\/552\/revisions"}],"predecessor-version":[{"id":1944,"href":"https:\/\/project.inria.fr\/plasma-lab\/wp-json\/wp\/v2\/pages\/552\/revisions\/1944"}],"up":[{"embeddable":true,"href":"https:\/\/project.inria.fr\/plasma-lab\/wp-json\/wp\/v2\/pages\/236"}],"wp:attachment":[{"href":"https:\/\/project.inria.fr\/plasma-lab\/wp-json\/wp\/v2\/media?parent=552"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}