TY - JOUR
T1 - Hybrid regulatory models
T2 - a statistically tractable approach to model regulatory network dynamics
AU - Ocone, Andrea
AU - Millar, Andrew J
AU - Sanguinetti, Guido
PY - 2013
Y1 - 2013
N2 - 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 coarsegrained 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 which 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.
AB - 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 coarsegrained 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 which 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.
U2 - 10.1093/bioinformatics/btt069
DO - 10.1093/bioinformatics/btt069
M3 - Article
C2 - 23407360
VL - 29
SP - 910
EP - 916
JO - Bioinformatics
JF - Bioinformatics
SN - 1367-4803
IS - 7
ER -