Toni, Tina

Approximate Bayesian computation scheme for parameter inference and model selection in dynamical systems

Toni, T., Welch, D., Strelkowa, N., Ipsen, A., & Stumpf, M. P. H. (2009) · Journal of The Royal Society Interface

Markov Chains and Monte Carlo MethodsMathematical and Theoretical Epidemiology and Ecology Models

Cited by 1,498

Simulation-based model selection for dynamical systems in systems and population biology

Toni, T., & Stumpf, M. P. H. (2010) · Bioinformatics

Gene Regulatory Network AnalysisMarkov Chains and Monte Carlo Methods

Cited by 260

A framework for parameter estimation and model selection from experimental data in systems biology using approximate Bayesian computation

Liepe, J., Kirk, P., Filippi, S., Toni, T., Barnes, C. P., & Stumpf, M. P. H. (2014) · Nature Protocols

Markov Chains and Monte Carlo MethodsGene Regulatory Network Analysis

Cited by 217

ABC-SysBio approximate Bayesian computation in Python with GPU support

Liepe, J., Barnes, C., Cule, E., Erguler, K., Kirk, P., Toni, T., & Stumpf, M. P. H. (2010) · Bioinformatics

Markov Chains and Monte Carlo MethodsGene Regulatory Network Analysis

Cited by 136

Designing attractive models via automated identification of chaotic and oscillatory dynamical regimes

Silk, D., Kirk, P. D. W., Barnes, C. P., Toni, T., Rose, A., Moon, S., Dallman, M. J., & Stumpf, M. P. H. (2011) · Nature Communications

Chaos control and synchronizationMathematical Dynamics and Fractals

Cited by 76

The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up

Marinescu, R. V., Oxtoby, N. P., Young, A. L., Bron, E. E., Toga, A. W., Weiner, M. W., Barkhof, F., Fox, N. C., Eshaghi, A., Toni, T., et al. (2021) · Machine Learning for Biomedical Imaging

Dementia and Cognitive Impairment ResearchAlzheimer's disease research and treatments

Cited by 68

Combined Model of Intrinsic and Extrinsic Variability for Computational Network Design with Application to Synthetic Biology

Toni, T., & Tidor, B. (2013) · PLoS Computational Biology

Gene Regulatory Network Analysisstochastic dynamics and bifurcation

Cited by 62

Elucidating the in vivo phosphorylation dynamics of the ERK MAP kinase using quantitative proteomics data and Bayesian model selection

Toni, T., Ozaki, Y.-I., Kirk, P., Kuroda, S., & Stumpf, M. P. H. (2012) · Molecular BioSystems

Gene Regulatory Network AnalysisProtein Kinase Regulation and GTPase Signaling

Cited by 28

From qualitative data to quantitative models: analysis of the phage shock protein stress response in Escherichia coli

Toni, T., Jovanovic, G., Huvet, M., Buck, M., & Stumpf, M. P. (2011) · BMC Systems Biology

Gene Regulatory Network AnalysisBacterial Genetics and Biotechnology

Cited by 23