Kirk, Paul

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

Model selection in systems and synthetic biology

Kirk, P., Thorne, T., & Stumpf, M. P. (2013) · Current Opinion in Biotechnology

Gene Regulatory Network AnalysisMicrobial Metabolic Engineering and Bioproduction

Cited by 118

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

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