Team:Grenoble/Modeling/Amplification/Stochastic

From 2012.igem.org

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Rather than using fixed variables such as in other mathematical modeling, a stochastic model incorporates random variations to predict future conditions and to see what they might be like.
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To introduce that randomness we use a new function : propensities.
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Revision as of 14:16, 27 August 2012

iGEM Grenoble 2012

iGEM Grenoble iGEM
iGEM 2012
main page
Project

Goal


Statistic modeling is a technique of presenting data or predicting outcomes that takes into account a certain degree of randomness or unpredictability. The stochastic process is often used to represent the evolution of some random value, or system, over time.

It is the probabilistic counterpart to a deterministic process.

Why


Gene expression is a stochastic process due to the inherent unpredictability of molecular collisions resulting from Brownian motion : the binding or unbinding of RNA polymerase to a promotor is partially random. In biology systems, introducing stochastic noise has been found to help improve the signal strength of the internal feedback loops for balance and other vestibular communication.

How


Rather than using fixed variables such as in other mathematical modeling, a stochastic model incorporates random variations to predict future conditions and to see what they might be like.
To introduce that randomness we use a new function : propensities.

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