Parameter identification and model ranking of thomas networks

Authors:
Hannes Klarner;Adam Streck;David Šafránek;Juraj Kolčák;Heike Siebert
Affiliations:
Freie Universität Berlin, Berlin, Germany;Masaryk University, Brno, Czech Republic;Masaryk University, Brno, Czech Republic;Masaryk University, Brno, Czech Republic;Freie Universität Berlin, Berlin, Germany
Venue:
CMSB'12 Proceedings of the 10th international conference on Computational Methods in Systems Biology
Year:
2012

Citing 8
Cited 0

Dynamical analysis of a generic Boolean model for the control of the mammalian cell cycle

Bioinformatics
Principles of Model Checking (Representation and Mind Series)

Principles of Model Checking (Representation and Mind Series)
Efficient parameter search for qualitative models of regulatory networks using symbolic model checking

Bioinformatics
Dynamically consistent reduction of logical regulatory graphs

Theoretical Computer Science
Incorporating time delays into the logical analysis of gene regulatory networks

CMSB'06 Proceedings of the 2006 international conference on Computational Methods in Systems Biology
On Parameter Synthesis by Parallel Model Checking

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Automatic inference of regulatory and dynamical properties from incomplete gene interaction and expression data

IPCAT'12 Proceedings of the 9th international conference on Information Processing in Cells and Tissues
Time Series Dependent Analysis of Unparametrized Thomas Networks

IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)

Quantified Score

Hi-index	0.00

Visualization

Abstract

We propose a new methodology for identification and analysis of discrete gene networks as defined by René Thomas, supported by a tool chain: (i) given a Thomas network with partially known kinetic parameters, we reduce the number of acceptable parametrizations to those that fit time-series measurements and reflect other known constraints by an improved technique of coloured LTL model checking performing efficiently on Thomas networks in distributed environment; (ii) we introduce classification of acceptable parametrizations to identify most optimal ones; (iii) we propose two ways of visualising parametrizations dynamics wrt time-series data. Finally, computational efficiency is evaluated and the methodology is validated on bacteriophage λ case study.