title: Hybrid regulatory models: a statistically tractable approach to model regulatory network dynamics.
abstract: BACKGROUND
  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.
  
  
  METHODS
  http://homepages.inf.ed.ac.uk/gsanguin/software.html.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors: School of Informatics, University of Edinburgh, Edinburgh EH8 9AB, UK.
authors: Andrea
authors: A
authors: Ocone
authors: Andrew J
authors: AJ
authors: Millar
authors: Guido
authors: G
authors: Sanguinetti
mesh_terms: D000465:Algorithms; D000818:Animals; D000460:Chlorophyta / Q000235:genetics; D057906:Circadian Clocks / Q000235:genetics; D003198:Computer Simulation; D053263:Gene Regulatory Networks; D006801:Humans; D015233:Models, Statistical; D014176:Protein Biosynthesis; D012441:Saccharomyces cerevisiae / Q000235:genetics / Q000378:metabolism; D049490:Systems Biology / Q000379:methods; D014158:Transcription, Genetic