Literature DB >> 21487823

Multi-scale genetic dynamic modelling I : an algorithm to compute generators.

Markus Kirkilionis1, Ulrich Janus, Luca Sbano.   

Abstract

We present a new approach or framework to model dynamic regulatory genetic activity. The framework is using a multi-scale analysis based upon generic assumptions on the relative time scales attached to the different transitions of molecular states defining the genetic system. At micro-level such systems are regulated by the interaction of two kinds of molecular players: macro-molecules like DNA or polymerases, and smaller molecules acting as transcription factors. The proposed genetic model then represents the larger less abundant molecules with a finite discrete state space, for example describing different conformations of these molecules. This is in contrast to the representations of the transcription factors which are-like in classical reaction kinetics-represented by their particle number only. We illustrate the method by considering the genetic activity associated to certain configurations of interacting genes that are fundamental to modelling (synthetic) genetic clocks. A largely unknown question is how different molecular details incorporated via this more realistic modelling approach lead to different macroscopic regulatory genetic models which dynamical behaviour might-in general-be different for different model choices. The theory will be applied to a real synthetic clock in a second accompanying article (Kirkilioniset al., Theory Biosci, 2011).

Mesh:

Year:  2011        PMID: 21487823     DOI: 10.1007/s12064-011-0125-0

Source DB:  PubMed          Journal:  Theory Biosci        ISSN: 1431-7613            Impact factor:   1.919


  37 in total

1.  Using graphical models and genomic expression data to statistically validate models of genetic regulatory networks.

Authors:  A J Hartemink; D K Gifford; T S Jaakkola; R A Young
Journal:  Pac Symp Biocomput       Date:  2001

2.  Stochasticity in transcriptional regulation: origins, consequences, and mathematical representations.

Authors:  T B Kepler; T C Elston
Journal:  Biophys J       Date:  2001-12       Impact factor: 4.033

3.  DNA looping and physical constraints on transcription regulation.

Authors:  José M G Vilar; Stanislas Leibler
Journal:  J Mol Biol       Date:  2003-08-29       Impact factor: 5.469

4.  Building networks with microarray data.

Authors:  Bradley M Broom; Waree Rinsurongkawong; Lajos Pusztai; Kim-Anh Do
Journal:  Methods Mol Biol       Date:  2010

Review 5.  Introduction to microarray technology.

Authors:  Martin Dufva
Journal:  Methods Mol Biol       Date:  2009

Review 6.  Exploration of cellular reaction systems.

Authors:  Markus Kirkilionis
Journal:  Brief Bioinform       Date:  2010-01       Impact factor: 11.622

Review 7.  Genome simulation approaches for synthesizing in silico datasets for human genomics.

Authors:  Marylyn D Ritchie; William S Bush
Journal:  Adv Genet       Date:  2010       Impact factor: 1.944

Review 8.  Engineered gene circuits.

Authors:  Jeff Hasty; David McMillen; J J Collins
Journal:  Nature       Date:  2002-11-14       Impact factor: 49.962

9.  Detecting novel genes with sparse arrays.

Authors:  Mikko Arvas; Niina Haiminen; Bart Smit; Jari Rautio; Marika Vitikainen; Marilyn Wiebe; Diego Martinez; Christine Chee; Joe Kunkel; Charles Sanchez; Mary Anne Nelson; Tiina Pakula; Markku Saloheimo; Merja Penttilä; Teemu Kivioja
Journal:  Gene       Date:  2010-08-05       Impact factor: 3.688

10.  A Bayesian network approach to operon prediction.

Authors:  Joseph Bockhorst; Mark Craven; David Page; Jude Shavlik; Jeremy Glasner
Journal:  Bioinformatics       Date:  2003-07-01       Impact factor: 6.937

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