Team:Tsinghua-A/Modeling/GILLESPIE/part1
From 2012.igem.org
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<p>To make the model more convincing, we use Gillespie algorithm, which is a kind of stochastic simulation algorithm to generate a statistically correct trajectory (possible solution) of a stochastic equation.</p> | <p>To make the model more convincing, we use Gillespie algorithm, which is a kind of stochastic simulation algorithm to generate a statistically correct trajectory (possible solution) of a stochastic equation.</p> | ||
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- | <h2 style="font-size:38px;"> | + | <h2 style="font-size:38px;">Summary of the steps to run the algorithm</h2> |
<p>“Step 0 (Initialization). Input the desired values for the M reaction constants c_1,. . ., c_Mand the N initial molecular population numbers X_1,. . ., X_M. Set the time variable t and the reaction counter n both to zero. Initialize the unit-interval uniform random number generator (URN).</br> | <p>“Step 0 (Initialization). Input the desired values for the M reaction constants c_1,. . ., c_Mand the N initial molecular population numbers X_1,. . ., X_M. Set the time variable t and the reaction counter n both to zero. Initialize the unit-interval uniform random number generator (URN).</br> | ||
Step 1. Calculate and store the M quantities a_1 = h_1 c_1,, . , , a_M = h_M c_M for the current molecular population numbers, where h_v is that function of X_1,. . ., X_M defined in (1). Also calculate and store as a_0 the sum of the M a_v values.</br> | Step 1. Calculate and store the M quantities a_1 = h_1 c_1,, . , , a_M = h_M c_M for the current molecular population numbers, where h_v is that function of X_1,. . ., X_M defined in (1). Also calculate and store as a_0 the sum of the M a_v values.</br> | ||
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Step 3. Using theτandμvalues obtained in step 2, increase t byτ, and adjust the molecular population levels to reflect the occurrence of one R_μ, reaction; e.g., if R_μ is the reaction in (4), then increase X_1 by 1 and decrease X_2 by 1. Then increase the reaction counter n by 1 and return to step 1. “<b>(Danlel T. Gillespie, 1977, p. 2345)[7]<b/></br> | Step 3. Using theτandμvalues obtained in step 2, increase t byτ, and adjust the molecular population levels to reflect the occurrence of one R_μ, reaction; e.g., if R_μ is the reaction in (4), then increase X_1 by 1 and decrease X_2 by 1. Then increase the reaction counter n by 1 and return to step 1. “<b>(Danlel T. Gillespie, 1977, p. 2345)[7]<b/></br> | ||
</p> | </p> | ||
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</div> | </div> | ||
Revision as of 17:12, 25 September 2012
Tsinghua-A::Modeling
A more accurate model using Gillespie algorithm
To make the model more convincing, we use Gillespie algorithm, which is a kind of stochastic simulation algorithm to generate a statistically correct trajectory (possible solution) of a stochastic equation.
Summary of the steps to run the algorithm
“Step 0 (Initialization). Input the desired values for the M reaction constants c_1,. . ., c_Mand the N initial molecular population numbers X_1,. . ., X_M. Set the time variable t and the reaction counter n both to zero. Initialize the unit-interval uniform random number generator (URN). Step 1. Calculate and store the M quantities a_1 = h_1 c_1,, . , , a_M = h_M c_M for the current molecular population numbers, where h_v is that function of X_1,. . ., X_M defined in (1). Also calculate and store as a_0 the sum of the M a_v values. Step 2. Generate two random numbers r1 and r2 using the unit-interval uniform random number generator, and calculateτandμaccording to (2) and (3). Step 3. Using theτandμvalues obtained in step 2, increase t byτ, and adjust the molecular population levels to reflect the occurrence of one R_μ, reaction; e.g., if R_μ is the reaction in (4), then increase X_1 by 1 and decrease X_2 by 1. Then increase the reaction counter n by 1 and return to step 1. “(Danlel T. Gillespie, 1977, p. 2345)[7]