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Team:KULeuven/Modelling
From 2009.igem.org
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this presentations is mainly based on the presentation of last year and the wiki of ETH Zürich 2007. | this presentations is mainly based on the presentation of last year and the wiki of ETH Zürich 2007. | ||
| - | == | + | == modelling overview == |
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* Foward modelling: define the physical laws that, given the values of the parameters of the system, determine the value of the observable parameters. | * Foward modelling: define the physical laws that, given the values of the parameters of the system, determine the value of the observable parameters. | ||
* Inverse modelling: given observed parameters, infer the actual parameters that produced the observed data. | * Inverse modelling: given observed parameters, infer the actual parameters that produced the observed data. | ||
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| + | While the first two steps are mainly deductive, the third step is inductive. | ||
| + | Most of the time this is an iterative approach, the last step will contain an indication of how good the model | ||
| + | fits the observed data. Sometimes it will be necessary to adjust the proposed model, if it doesn't fit the observed data. | ||
== References == | == References == | ||
Revision as of 12:04, 28 July 2009
Modelling
introduction
As an introduction to modelling we made a short presentation. This presentation handles about the following:
- some definitions and the role of modelling
- black and white box modelling
- the role of ordinary differential equations
- modelling applied to iGEM
this presentations is mainly based on the presentation of last year and the wiki of ETH Zürich 2007.
modelling overview
The scientific procedure for the study of a physical system can be divided in the following 3 steps
- Parameterization of the system: discover the minimal set of parameters that completely define the system.
- Foward modelling: define the physical laws that, given the values of the parameters of the system, determine the value of the observable parameters.
- Inverse modelling: given observed parameters, infer the actual parameters that produced the observed data.
While the first two steps are mainly deductive, the third step is inductive. Most of the time this is an iterative approach, the last step will contain an indication of how good the model fits the observed data. Sometimes it will be necessary to adjust the proposed model, if it doesn't fit the observed data.
References
["Inverse problem theory and methods for model parameter estimation", Albert Tarantola]
["Modern simulation and modeling", Reuven Y. Rubinstein, Benjamin Melamed]
