Team:Grenoble/Modeling/Amplification/Stochastic
From 2012.igem.org
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We randomly chose the next reaction regarding the propensities. | We randomly chose the next reaction regarding the propensities. | ||
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When we run the script once, we get a graph. This graph represent the rando evolution of an element. Because of this randomness, if we run the script an other time we will get a different graph. That is why to be able to interpret the results we have to run the cripts hundreds or thousands of times. | When we run the script once, we get a graph. This graph represent the rando evolution of an element. Because of this randomness, if we run the script an other time we will get a different graph. That is why to be able to interpret the results we have to run the cripts hundreds or thousands of times. | ||
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Traditional continuous and deterministic biochemical rate equations do not accurately predict cellular reactions since they rely on bulk reactions that require the interactions of millions of molecules. | Traditional continuous and deterministic biochemical rate equations do not accurately predict cellular reactions since they rely on bulk reactions that require the interactions of millions of molecules. | ||
- | In contrast, the Gillespie algorithm allows a discrete and stochastic simulation of a system with few reactants because every | + | In contrast, the Gillespie algorithm allows a discrete and stochastic simulation of a system with few reactants because every molecule is explicitly simulated. When simulated, a Gillespie realization represents a random walk of the entire system. |
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<center><img src="https://static.igem.org/mediawiki/2012/5/50/State_change_vector.png" alt="" /></center> | <center><img src="https://static.igem.org/mediawiki/2012/5/50/State_change_vector.png" alt="" /></center> | ||
+ | <center><img src="https://static.igem.org/mediawiki/2012/9/9a/Change.png" alt="" /></center> | ||
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Revision as of 08:13, 29 August 2012