T
Tina Toni
Researcher at Imperial College London
Publications - 26
Citations - 3013
Tina Toni is an academic researcher from Imperial College London. The author has contributed to research in topics: Model selection & Approximate Bayesian computation. The author has an hindex of 16, co-authored 26 publications receiving 2661 citations. Previous affiliations of Tina Toni include Massachusetts Institute of Technology.
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Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems
TL;DR: This paper discusses and applies an ABC method based on sequential Monte Carlo (SMC) to estimate parameters of dynamical models and develops ABC SMC as a tool for model selection; given a range of different mathematical descriptions, it is able to choose the best model using the standard Bayesian model selection apparatus.
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Simulation-based model selection for dynamical systems in systems and population biology
Tina Toni,Michael P. H. Stumpf +1 more
TL;DR: Toni et al. as discussed by the authors developed a model selection framework based on approximate Bayesian computation and employing sequential Monte Carlo sampling, which can be applied across a wide range of biological scenarios, and illustrate its use on real data describing influenza dynamics and the JAK-STAT signalling pathway.
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A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation
TL;DR: An approximate Bayesian computation framework and software environment, ABC-SysBio, which is a Python package that runs on Linux and Mac OS X systems and that enables parameter estimation and model selection in the Bayesian formalism by using sequential Monte Carlo (SMC) approaches is presented.
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Managing membrane stress: the phage shock protein (Psp) response, from molecular mechanisms to physiology.
Nicolas Joly,Christoph Engl,Goran Jovanovic,Maxime Huvet,Tina Toni,Xia Sheng,Michael P. H. Stumpf,Martin Buck +7 more
TL;DR: Progress in understanding the mechanism of signal transduction by the membrane-bound Psp proteins, regulation of the psp gene-specific transcription activator and the cell biology of the Psp system is presented and discussed.
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ABC-SysBio—approximate Bayesian computation in Python with GPU support
Juliane Liepe,Chris P. Barnes,Erika Cule,Kamil Erguler,Paul D. W. Kirk,Tina Toni,Michael P. H. Stumpf +6 more
TL;DR: A Python package, ABC-SysBio, that implements parameter inference and model selection for dynamical systems in an approximate Bayesian computation (ABC) framework and is designed to work with models written in Systems Biology Markup Language (SBML).