Multi-scale genetic dynamic modelling II : application to synthetic biology
Kirkilionis, Markus, 1962-, Janus, Ulrich and Sbano, Luca. (2011) Multi-scale genetic dynamic modelling II : application to synthetic biology. Theory in Biosciences, Vol.130 (No.3). pp. 183-201. ISSN 1431-7613Full text not available from this repository.
Official URL: http://dx.doi.org/10.1007/s12064-011-0126-z
We model in detail a simple synthetic genetic clock that was engineered in Atkinson et al. (Cell 113(5):597-607, 2003) using Escherichia coli as a host organism. Based on this engineered clock its theoretical description uses the modelling framework presented in Kirkilionis et al. (Theory Biosci. 10.1007/s12064-011-0125-0, this volume). The main goal of this accompanying article was to illustrate that parts of the modelling process can be algorithmically automatised once the model framework we called 'average dynamics' is accepted (Sbano and Kirkilionis, WMI Preprint 7/2007, 2008c; Kirkilionis and Sbano, Adv Complex Syst 13(3):293-326, 2010). The advantage of the 'average dynamics' framework is that system components (especially in genetics) can be easier represented in the model. In particular, if once discovered and characterised, specific molecular players together with their function can be incorporated. This means that, for example, the 'gene' concept becomes more clear, for example, in the way the genetic component would react under different regulatory conditions. Using the framework it has become a realistic aim to link mathematical modelling to novel tools of bioinformatics in the future, at least if the number of regulatory units can be estimated. This should hold in any case in synthetic environments due to the fact that the different synthetic genetic components are simply known (Elowitz and Leibler, Nature 403(6767):335-338, 2000; Gardner et al., Nature 403(6767):339-342, 2000; Hasty et al., Nature 420(6912):224-230, 2002). The paper illustrates therefore as a necessary first step how a detailed modelling of molecular interactions with known molecular components leads to a dynamic mathematical model that can be compared to experimental results on various levels or scales. The different genetic modules or components are represented in different detail by model variants. We explain how the framework can be used for investigating other more complex genetic systems in terms of regulation and feedback.
|Item Type:||Journal Article|
|Subjects:||Q Science > QA Mathematics
Q Science > QR Microbiology
T Technology > TA Engineering (General). Civil engineering (General)
|Divisions:||Faculty of Science > Mathematics|
|Library of Congress Subject Headings (LCSH):||Genetic algorithms, Genetic programming (Computer science), Multiscale modeling, Markov processes, Escherichia coli, Synthetic biology|
|Journal or Publication Title:||Theory in Biosciences|
|Official Date:||September 2011|
|Page Range:||pp. 183-201|
|Funder:||European Commission (EC)|
|Grant number:||12990 (EC)|
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