Stumpf, Michael P. H.

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

Evolution of pathogenicity and sexual reproduction in eight Candida genomes

Butler, G., Rasmussen, M. D., Lin, M. F., Santos, M. A. S., Sakthikumar, S., Munro, C. A., Rheinbay, E., Grabherr, M., Forche, A., Reedy, J. L., et al. (2009) · Nature

Fungal and yeast genetics researchYeasts and Rust Fungi Studies

Cited by 1,065

Balancing Selection at the Prion Protein Gene Consistent with Prehistoric Kurulike Epidemics

Mead, S., Stumpf, M. P. H., Whitfield, J., Beck, J. A., Poulter, M., Campbell, T., Uphill, J. B., Goldstein, D., Alpers, M., Fisher, E. M. C., et al. (2003) · Science

Prion Diseases and Protein MisfoldingEvolution and Genetic Dynamics

Cited by 394

Network motifs: Structure does not determine function.

Ingram, P. J., Stumpf, M. P., & Stark, J. (2006) · BMC Genomics

Gene Regulatory Network AnalysisBioinformatics and Genomic Networks

Cited by 267

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

Phylogenetic diversity of stress signalling pathways in fungi

Nikolaou, E., Agrafioti, I., Stumpf, M., Quinn, J., Stansfield, I., & Brown, A. J. (2009) · BMC Evolutionary Biology

Fungal and yeast genetics researchBacterial Genetics and Biotechnology

Cited by 206

Maximizing the information content of experiments in systems biology

Liepe, J., Filippi, S., Komorowski, M., & Stumpf, M. P. H. (2013) · PLoS Computational Biology

Gene Regulatory Network AnalysisControl Systems and Identification

Cited by 170

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

A predominantly indigenous paternal heritage for the Austronesian-speaking peoples of insular Southeast Asia and Oceania

Capelli, C., Wilson, J. F., Richards, M., Stumpf, M. P. H., Gratrix, F., Oppenheimer, S., Underhill, P., Pascali, V. L., Ko, T.-M., & Goldstein, D. B. (2001) · The American Journal of Human Genetics

Forensic and Genetic ResearchPacific and Southeast Asian Studies

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

Practical limits for reverse engineering of dynamical systems: a statistical analysis of sensitivity and parameter inferability in systems biology models

Erguler, K., & Stumpf, M. P. H. (2011) · Molecular BioSystems

Gene Regulatory Network AnalysisMicrobial Metabolic Engineering and Bioproduction

Cited by 91

Bayesian design of synthetic biological systems

Barnes, C. P., Silk, D., Sheng, X., & Stumpf, M. P. H. (2011) · Proceedings of the National Academy of Sciences

Gene Regulatory Network AnalysisMicrobial Metabolic Engineering and Bioproduction

Cited by 90

Combinatorial stresses kill pathogenic Candida species

Kaloriti, D., Tillmann, A., Cook, E., Jacobsen, M., You, T., Lenardon, M., Ames, L., Barahona, M., Chandrasekaran, K., Coghill, G., et al. (2012) · Medical Mycology

Fungal and yeast genetics researchAntifungal resistance and susceptibility

Cited by 86

Considerate approaches to constructing summary statistics for ABC model selection

Barnes, C. P., Filippi, S., Stumpf, M. P. H., & Thorne, T. (2012) · Statistics and Computing

Markov Chains and Monte Carlo MethodsStatistical Methods and Bayesian Inference

Cited by 61

Calibrating spatio-temporal models of leukocyte dynamics against in vivo live-imaging data using approximate Bayesian computation

Liepe, J., Taylor, H., Barnes, C. P., Huvet, M., Bugeon, L., Thorne, T., Lamb, J. R., Dallman, M. J., & Stumpf, M. P. H. (2012) · Integrative Biology

Zebrafish Biomedical Research ApplicationsMarkov Chains and Monte Carlo Methods

Cited by 33

Graph spectral analysis of protein interaction network evolution

Thorne, T., & Stumpf, M. P. H. (2012) · Journal of The Royal Society Interface

Complex Network Analysis TechniquesBioinformatics and Genomic Networks

Cited by 30

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

Prediction of putative protein interactions through evolutionary analysis of osmotic stress response in the model yeast Saccharomyces cerevisae

Thorne, T. W., Ho, H.-L., Huvet, M., Haynes, K., & Stumpf, M. P. H. (2011) · Fungal Genetics and Biology

Fungal and yeast genetics researchBioinformatics and Genomic Networks

Cited by 8

The Topological Properties of the Protein Universe

Madsen, C. D., Barbensi, A., Zhang, S. Y., Ham, L., David, A., Pires, D. E. V., & Stumpf, M. P. H. (2023) · Nature Communications

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 7

The topological properties of the protein universe

Madsen, C. D., Barbensi, A., Zhang, S. Y., Ham, L., David, A., Pires, D. E. V., & Stumpf, M. P. H. (2025) · Nature Communications

Protein Structure and DynamicsMachine Learning in Bioinformatics

Cited by 7