Difference between revisions of "Deterministic vs. Stochastic Models"
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[[Image:Stochastic_model3.png]] | [[Image:Stochastic_model3.png]] | ||
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| − | Here | + | Here are two visual representation of stochastic models. In the first, nodes represent states of molecular complexes while arrows represent binding of transcriptional, translational and degredation proteins. The second also details transcription and translation but goes into detail about competition for the leader region of mRNA between degradosomes and protiens that induce translation. |
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<center>[[Noise as a Variable |Next Section in Modeling Stochasticity]] | [[Modeling Stochasticity | Back to Modeling Stochasticity]]</center> | <center>[[Noise as a Variable |Next Section in Modeling Stochasticity]] | [[Modeling Stochasticity | Back to Modeling Stochasticity]]</center> | ||
Revision as of 02:06, 13 November 2007
The Two Equations Used to Model Gene Expression
Determinisic
A deterministic equation uses a rate equation to describe the transcription and translation of genes. Deterministic equations are characterized as behaving predictably, more specifically a single input will consitently produce the same output. Returning to one of the Collins graphs, the blue line represents the deterministic model and the red line represents the stochastic model.
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Stochastic
Stochastic models take into account the "randomness" of transcription and translation by utilizing variables for the formation and decay of single molecules and multi-component complexes.
Here are two visual representation of stochastic models. In the first, nodes represent states of molecular complexes while arrows represent binding of transcriptional, translational and degredation proteins. The second also details transcription and translation but goes into detail about competition for the leader region of mRNA between degradosomes and protiens that induce translation.
