Affiliation: Imperial College
- Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systemsTina Toni
Centre for Bioinformatics, Division of Molecular Biosciences, Imperial College London, London SW7 2AZ, UK
J R Soc Interface 6:187-202. 2009..Moreover, we develop ABC SMC as a tool for model selection; given a range of different mathematical descriptions, ABC SMC is able to choose the best model using the standard Bayesian model selection apparatus...
- From qualitative data to quantitative models: analysis of the phage shock protein stress response in Escherichia coliTina Toni
Division of Molecular Biosciences, Imperial College London, South Kensington, London SW7 2AZ, UK
BMC Syst Biol 5:69. 2011..This system has not yet been theoretically modelled or analysed in silico...
- Simulation-based model selection for dynamical systems in systems and population biologyTina Toni
Division of Molecular Biosciences, Imperial College London, Wolfson Building, SW72AZ London, UK
Bioinformatics 26:104-10. 2010..The present approach enables us to employ the whole formal apparatus to any system that can be (efficiently) simulated, even when exact likelihoods are computationally intractable...
- Managing membrane stress: the phage shock protein (Psp) response, from molecular mechanisms to physiologyNicolas Joly
Division of Biology, Imperial College London, South Kensington, London, UK
FEMS Microbiol Rev 34:797-827. 2010....
- Model-based evolutionary analysis: the natural history of phage-shock stress responseMaxime Huvet
Division of Molecular Biosciences, Imperial College London, Centre for Bioinformatics, South Kensington, London SW7 2AZ, UK
Biochem Soc Trans 37:762-7. 2009..We use the specific example of the phage-shock stress response, which has been well characterized in Escherichia coli, to elucidate patterns of gene sharing and sequence conservation across bacterial species...
- Elucidating the in vivo phosphorylation dynamics of the ERK MAP kinase using quantitative proteomics data and Bayesian model selectionTina Toni
Centre for Integrative Systems Biology and Bioinformatics, Division of Molecular Biosciences, Department of Life Sciences, Imperial College London, London, UK
Mol Biosyst 8:1921-9. 2012..The models with the highest inferred posterior probability are the two models employing distributive dephosphorylation, whereas we are unable to choose decisively between the processive and distributive phosphorylation mechanisms...
- ABC-SysBio--approximate Bayesian computation in Python with GPU supportJuliane Liepe
Centre for Bioinformatics, Division of Molecular Biosciences, Imperial College London, London, UK
Bioinformatics 26:1797-9. 2010..A number of statistical approaches, both frequentist and Bayesian, have been proposed to answer these questions...
- The ABC of reverse engineering biological signalling systemsMaria Secrier
Centre for Bioinformatics, Imperial College London, UK
Mol Biosyst 5:1925-35. 2009..Our analysis also highlights the added benefit of using the distribution of parameters rather than point estimates of parameter values when considering the notion of sloppy models in systems biology...
- Parameter inference and model selection in signaling pathway modelsTina Toni
Division of Molecular Biosciences, Centre for Bioinformatics, Imperial College London, London, UK
Methods Mol Biol 673:283-95. 2010..Approximate Bayesian computation techniques are introduced and employed to explore different hypothesis about the JAK-STAT signaling pathway...
- Designing attractive models via automated identification of chaotic and oscillatory dynamical regimesDaniel Silk
Centre for Bioinformatics, Imperial College London, London SW7 2AZ, UK
Nat Commun 2:489. 2011..This change in perspective from quantitative to qualitative dynamics, provides fundamental and new insights into the properties of dynamical systems...