Document Detail


Multi-scale genetic dynamic modelling II: application to synthetic biology : An algorithmic Markov chain based approach.
MedLine Citation:
PMID:  21509695     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
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. doi: 10.1007/s12064-011-0125-0 , 2011, 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.
Authors:
Markus Kirkilionis; Ulrich Janus; Luca Sbano
Related Documents :
21354205 - New and emerging imaging techniques for mapping brain circuitry.
21534965 - Intimacy in young adults' narratives of romance and friendship predicts eriksonian gene...
21173865 - Organ pose distribution model and an map framework for automated abdominal multi-organ ...
21097465 - Towards biodbcore: a community-defined information specification for biological databases.
10556875 - Improving biosensor analysis.
21061485 - Support vector machine-based feature extractor for l/h transitions in jet.
Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2011-4-21
Journal Detail:
Title:  Theory in biosciences = Theorie in den Biowissenschaften     Volume:  -     ISSN:  1611-7530     ISO Abbreviation:  -     Publication Date:  2011 Apr 
Date Detail:
Created Date:  2011-4-21     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9708216     Medline TA:  Theory Biosci     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Affiliation:
Mathematics Institute, University of Warwick, Coventry, CV4 7AL, UK, mak@maths.warwick.ac.uk.
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms
Descriptor/Qualifier:

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


Previous Document:  Oviposition by a moth suppresses constitutive and herbivore-induced plant volatiles in maize.
Next Document:  Lack of prognostic role of pre- and postoperative peritoneal cytology and cytokeratin PCR-expression...