Hybrid regulatory models: a statistically tractable approach to model regulatory network dynamics

Motivation: Computational modelling of the dynamics of gene regulatory networks is a central task of systems biology. For networks of small/medium scale, the dominant paradigm is represented by systems of coupled non-linear ordinary differential equations (ODEs). ODEs afford great mechanistic detail...

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Bibliographic Details
Published in:Bioinformatics Vol. 29; no. 7; pp. 910 - 916
Main Authors: Ocone, Andrea, Millar, Andrew J., Sanguinetti, Guido
Format: Journal Article
Language:English
Published: England 01.04.2013
Subjects:
Algorithms
Animals
Bioinformatics
Biological
Chlorophyta - genetics
Circadian Clocks - genetics
Computer Simulation
Dynamical systems
Gene Regulatory Networks
Humans
Inference
Mathematical analysis
Mathematical models
Models, Statistical
Networks
Nonlinear dynamics
Protein Biosynthesis
Saccharomyces cerevisiae - genetics
Saccharomyces cerevisiae - metabolism
Systems Biology - methods
Transcription, Genetic
ISSN:1367-4803, 1367-4811, 1367-4811, 1460-2059
Online Access:Get full text
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Summary:Motivation: Computational modelling of the dynamics of gene regulatory networks is a central task of systems biology. For networks of small/medium scale, the dominant paradigm is represented by systems of coupled non-linear ordinary differential equations (ODEs). ODEs afford great mechanistic detail and flexibility, but calibrating these models to data is often an extremely difficult statistical problem. Results: Here, we develop a general statistical inference framework for stochastic transcription–translation networks. We use a coarse-grained approach, which represents the system as a network of stochastic (binary) promoter and (continuous) protein variables. We derive an exact inference algorithm and an efficient variational approximation that allows scalable inference and learning of the model parameters. We demonstrate the power of the approach on two biological case studies, showing that the method allows a high degree of flexibility and is capable of testable novel biological predictions. Availability and implementation:  http://homepages.inf.ed.ac.uk/gsanguin/software.html. Supplementary information:  Supplementary data are available at Bioinformatics online. Contact:  G.Sanguinetti@ed.ac.uk
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ISSN:1367-4803
1367-4811
1367-4811
1460-2059
DOI:10.1093/bioinformatics/btt069