T Stadler, OG Pybus, M. STUMPF. Phylodynamics for cell biologists.. Science (New York, N.Y.), 371, eaah6266, 2021. doi: 10.1126/science.aah6266.
Anissa Guillemin, Elisabeth Roesch, M. STUMPF. Uncertainty in cell fate decision making: Lessons from potential landscapes of bifurcation systems. 2021.01.03.425143, Cold Spring Harbor Laboratory, 2021. doi: 10.1101/2021.01.03.425143.
M. STUMPF. Statistical and computational challenges for whole cell modelling. Current Opinion in Systems Biology, 26, 58-63, 2021. doi: 10.1016/j.coisb.2021.04.005.
Lucy Ham, David Schnoerr, Rowan Brackston, M. STUMPF. Exactly solvable models of stochastic gene expression. 2020.01.05.895359, Cold Spring Harbor Laboratory, 2020. doi: 10.1101/2020.01.05.895359.
Lucy Ham, David Schnoerr, Rowan D Brackston, M. STUMPF. Exactly solvable models of stochastic gene expression.. The Journal of chemical physics, 152, 144106 (18pp), 2020. doi: 10.1063/1.5143540.
Lucy Ham, Rowan D Brackston, M. STUMPF. Extrinsic Noise and Heavy-Tailed Laws in Gene Expression.. Physical review letters, 124, 108101 (6pp), 2020. doi: 10.1103/PhysRevLett.124.108101.
Evgeny Tankhilevich, Jonathan Ish-Horowicz, Tara Hameed, Elisabeth Roesch, Istvan Kleijn, M. STUMPF, Fei He. GpABC: a Julia package for approximate Bayesian computation with Gaussian process emulation.. Bioinformatics (Oxford, England), 3286-3287, 2020. doi: 10.1093/bioinformatics/btaa078.
Léo PM Diaz, M. STUMPF. Gaining confidence in inferred networks. 2020.09.19.304980, Cold Spring Harbor Laboratory, 2020. doi: 10.1101/2020.09.19.304980.
Ivan Croydon Veleslavov, M. STUMPF. Repeated Decision Stumping Distils Simple Rules from Single Cell Data. 2020.09.08.288662, Cold Spring Harbor Laboratory, 2020. doi: 10.1101/2020.09.08.288662.
M. STUMPF. Multi-model and network inference based on ensemble estimates: avoiding the madness of crowds.. Journal of the Royal Society, Interface, 17, 20200419 (10pp), 2020. doi: 10.1098/rsif.2020.0419.
Anissa Guillemin, M. STUMPF. Noise and the molecular processes underlying cell fate decision-making.. Physical biology, 011002 (10pp), 2020. doi: 10.1088/1478-3975/abc9d1.
M. STUMPF. Multi-model and network inference based on ensemble estimates: Avoiding the madness of crowds: Multi-model and network inference based on ensemble estimates: Avoiding the madness of crowds. Journal of the Royal Society Interface, 17, 2020. doi: 10.1098/rsif.2020.0419rsif20200419.
Megan Coomer, Lucy Ham, M. STUMPF. Shaping the Epigenetic Landscape: Complexities and Consequences. 2020.12.21.423724, Cold Spring Harbor Laboratory, 2020. doi: 10.1101/2020.12.21.423724.
Lucy Ham, Marcel Jackson, M. STUMPF. Pathway dynamics can delineate the sources of transcriptional noise in gene expression. 2020.09.30.319814, Cold Spring Harbor Laboratory, 2020. doi: 10.1101/2020.09.30.319814.
Leander Dony, Fei He, M. STUMPF. Parametric and non-parametric gradient matching for network inference: a comparison.. BMC bioinformatics, 20, 52 (12pp), 2019. doi: 10.1186/s12859-018-2590-7.
Elisabeth Roesch, M. STUMPF. Parameter inference in dynamical systems with co-dimension 1 bifurcations. Royal Society Open Science, 6, 190747 (13pp), 2019. doi: 10.1098/rsos.190747.
M. STUMPF. Multi-Model and Network Inference Based on Ensemble Estimates: Avoiding the Madness of Crowds. 858308, Cold Spring Harbor Laboratory, 2019. doi: 10.1101/858308.
Elisabeth Roesch, M. STUMPF. Parameter inference in dynamical systems with co-dimension 1 bifurcations. 623413, Cold Spring Harbor Laboratory, 2019. doi: 10.1101/623413.
Lucy Ham, Rowan Brackston, M. STUMPF. Extrinsic noise and heavy-tailed laws in gene expression. 623371, Cold Spring Harbor Laboratory, 2019. doi: 10.1101/623371.
Natalie S Scholes, David Schnoerr, Mark Isalan, M. STUMPF. A Comprehensive Network Atlas Reveals That Turing Patterns Are Common but Not Robust.. Cell systems, 9, 515-517, 2019. doi: 10.1016/j.cels.2019.09.010.
Natalie S Scholes, David Schnoerr, Mark Isalan, M. STUMPF. A Comprehensive Network Atlas Reveals That Turing Patterns Are Common but Not Robust.. Cell systems, 9, 243-+, 2019. doi: 10.1016/j.cels.2019.07.007.
Fei He, M. STUMPF. Quantifying Dynamic Regulation in Metabolic Pathways with Nonparametric Flux Inference.. Biophysical journal, 116, 2035-2046, 2019. doi: 10.1016/j.bpj.2019.04.009.
Ulrike Kuckelkorn, Sabine Stübler, Kathrin Textoris-Taube, Christiane Kilian, Agathe Niewienda, Petra Henklein, Katharina Janek, M. STUMPF, Michele Mishto, Juliane Liepe. Proteolytic dynamics of human 20S thymoproteasome.. The Journal of biological chemistry, 294, 7740-7754, 2019. doi: 10.1074/jbc.RA118.007347.
Thalia E Chan, M. STUMPF, Ann C Babtie. Gene Regulatory Networks from Single Cell Data for Exploring Cell Fate Decisions.. Methods in molecular biology (Clifton, N.J.), 1975, 211-238, 2019. doi: 10.1007/978-1-4939-9224-9_10.
Rowan D Brackston, Eszter Lakatos, M. STUMPF. Transition state characteristics during cell differentiation.. PLoS computational biology, 14, e1006405 (24pp), 2018. doi: 10.1371/journal.pcbi.1006405.
T Jetka, K Nienałtowski, S Filippi, M. STUMPF, M Komorowski. An information-theoretic framework for deciphering pleiotropic and noisy biochemical signaling.. Nature communications, 9, 4591 (9pp), 2018. doi: 10.1038/s41467-018-07085-1.
Rowan Brackston, Eszter Lakatos, M. STUMPF. Transition State Characteristics During Cell Differentiation. 264143, Cold Spring Harbor Laboratory, 2018. doi: 10.1101/264143.
Thalia Chan, Ananth Pallaseni, Ann Babtie, Kirsten McEwen, M. STUMPF. Empirical Bayes Meets Information Theoretical Network Reconstruction from Single Cell Data. 264853, Cold Spring Harbor Laboratory, 2018. doi: 10.1101/264853.
M. STUMPF. Biology challenging statistics.. Statistical applications in genetics and molecular biology, 17, 20180048 (2pp), 2018. doi: 10.1515/sagmb-2018-0048.
RD Brackston, A Wynn, M. STUMPF. Construction of quasipotentials for stochastic dynamical systems: An optimization approach.. Physical review. E, 98, 022136, 2018. doi: 10.1103/PhysRevE.98.022136.
Ann C Babtie, M. STUMPF. How to deal with parameters for whole-cell modelling.. Journal of the Royal Society Interface, 14, 20170237, 2017. doi: 10.1098/rsif.2017.0237.
Eszter Lakatos, M. STUMPF. Control mechanisms for stochastic biochemical systems via computation of reachable sets.. Royal Society open science, 4, 160790, 2017. doi: 10.1098/rsos.160790.
NM Rashidi, N Scherf, A Krinner, I Roeder, C Lo Celso, M. STUMPF, Adam L MacLean, Maia A Smith, Juliane Liepe, Aaron Sim, Reema Khorshed. Single Cell Phenotyping Reveals Heterogeneity Among Hematopoietic Stem Cells Following Infection.. Stem cells (Dayton, Ohio), 35, 2292-2304, 2017. doi: 10.1002/stem.2692.
Thalia E Chan, M. STUMPF, Ann C Babtie. Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures.. Cell systems, 5, 251-267.e3, 2017. doi: 10.1016/j.cels.2017.08.014.
Amanda G Fisher, M. STUMPF, Matthias Merkenschlager. Reconciling Epigenetic Memory and Transcriptional Responsiveness.. Cell systems, 4, 373-374, 2017. doi: 10.1016/j.cels.2017.04.005.
Angelique Ale, Valerie F Crepin, James W Collins, Nicholas Constantinou, Maryam Habibzay, Ann C Babtie, Gad Frankel, M. STUMPF. Model of Host-Pathogen Interaction Dynamics Links In Vivo Optical Imaging and Immune Responses.. Infection and immunity, 85, 2017. doi: 10.1128/IAI.00606-16.
AC Babtie, TE Chan, M. STUMPF. Learning regulatory models for cell development from single cell transcriptomic data. Current Opinion in Systems Biology, 5, 72-81, 2017. doi: 10.1016/j.coisb.2017.07.013.
M. STUMPF, Rosanna CG Smith, Michael Lenz, Andreas Schuppert, Franz-Josef Müller, Ann Babtie, Thalia E Chan, Colin P Please, Sam D Howison, Fumio Arai, Ben D MacArthur. Stem Cell Differentiation as a Non-Markov Stochastic Process.. Cell systems, 5, 268-282.e7, 2017. doi: 10.1016/j.cels.2017.08.009.
Eszter Lakatos, Ali Salehi-Reyhani, Michael Barclay, M. STUMPF, David R Klug. Protein degradation rate is the dominant mechanism accounting for the differences in protein abundance of basal p53 in a human breast and colorectal cancer cell line.. PloS one, 12, e0177336, 2017. doi: 10.1371/journal.pone.0177336.
Thalia Chan, M. STUMPF, Ann Babtie. Gene regulatory network inference from single-cell data using multivariate information measures. Cold Spring Harbor Laboratory, 2016. doi: 10.1101/082099.
Eszter Lakatos, M. STUMPF. Control mechanisms for stochastic biochemical systems via computation of reachable sets. Cold Spring Harbor Laboratory, 2016. doi: 10.1101/079723.
Patrick Smadbeck, M. STUMPF. Coalescent models for developmental biology and the spatio-temporal dynamics of growing tissues.. Journal of the Royal Society, Interface, 13, 20160112, 2016. doi: 10.1098/rsif.2016.0112.
Oleg Lenive, Paul D W Kirk, Michael P H Stumpf, M. STUMPF, Paul D W. Kirk, Michael P H. Stumpf. Inferring extrinsic noise from single-cell gene expression data using approximate Bayesian computation.. BMC systems biology, 10, 81, 2016. doi: 10.1186/s12918-016-0324-x.
P Kirk, D Silk, M. STUMPF. Reverse Engineering Under Uncertainty. 17, 15-32, Springer International Publishing, 2016. doi: 10.1007/978-3-319-21296-8_2.
M. STUMPF. Quantitative time-resolved analysis reveals intricate, differential regulation of standard- and immuno-proteasomes. eLife, 4, 2015. doi: 10.7554/eLife.07545.001.
Juliane Liepe, Hermann-Georg Holzhütter, Elena Bellavista, Peter M Kloetzel, M. STUMPF, Michele Mishto. Quantitative time-resolved analysis reveals intricate, differential regulation of standard- and immuno-proteasomes.. eLife, 4, e07545, 2015. doi: 10.7554/eLife.07545.
Siobhan McMahon, Oleg Lenive, Sarah Filippi, M. STUMPF. Information Processing by Simple Molecular Motifs and Susceptibility to Noise. Cold Spring Harbor Laboratory, 2015. doi: 10.1101/023697.
Patrick Smadbeck, M. STUMPF. Coalescent models for developmental biology and the spatio-temporal dynamics of growing tissues.. Cold Spring Harbor Laboratory, 2015. doi: 10.1101/022251.
Siobhan S Mc Mahon, Oleg Lenive, Sarah Filippi, M. STUMPF. Information processing by simple molecular motifs and susceptibility to noise.. Journal of the Royal Society, Interface, 12, 0597-20150597, 2015. doi: 10.1098/rsif.2015.0597.
P Kirk, DMY Rolando, AL Maclean, M. STUMPF. Conditional random matrix ensembles and the stability of dynamical systems. New Journal of Physics, 17, 083025, 2015. doi: 10.1088/1367-2630/17/8/083025.
WE Matzke, CP Barnes, E Jentzsch, T Mascher, M. STUMPF. On industrial strength Bio-design Automation. Communications in Computer and Information Science, 469, 277-299, 2014. doi: 10.1007/978-3-319-13206-8_14.
J Žurauskiene, P Kirk, T Thorne, M. STUMPF. Bayesian non-parametric approaches to reconstructing oscillatory systems and the Nyquist limit. Physica A: Statistical Mechanics and its Applications, 407, 33-42, 2014. doi: 10.1016/j.physa.2014.03.069.
CP Barnes, S Filippi, M. STUMPF, T Thorne. Considerate approaches to constructing summary statistics for ABC model selection. Statistics and Computing, 22, 1181-1197, 2012. doi: 10.1007/s11222-012-9335-7.
Anindita Roy, Gillian Cowan, Adam J Mead, Sarah Filippi, Georg Bohn, Aristeidis Chaidos, Oliver Tunstall, Jerry KY Chan, Mahesh Choolani, Phillip Bennett, S. Kumar, Deborah Atkinson, Josephine Wyatt-Ashmead, Ming Hu, M. STUMPF, Katerina Goudevenou, Stella T Chou, Mitchell J Weiss, Anastasios Karadimitris, Sten Eirik Jacobsen, Paresh Vyas, Irene Roberts. Perturbation of fetal liver hematopoietic stem and progenitor cell development by trisomy 21.. Proceedings of the National Academy of Sciences of the United States of America, 109, 17579-17584, 2012. doi: 10.1073/pnas.1211405109.
T Toni, J Liepe, M. STUMPF. Inference of Signalling Pathway Models. 417-439, John Wiley & Sons, Ltd, 2011. doi: 10.1002/9781119970606.ch21.
M. STUMPF. Neuroscience of birdsong.. Human genomics, 4, 143-144, 2009. doi: 10.1186/1479-7364-4-2-143.
M. STUMPF, C Wiuf. Statistical and evolutionary analysis of biological networks. 1-170, IMPERIAL COLLEGE PRESS, 2009. doi: 10.1142/P659.
WP Kelly, T Thorne, M. STUMPF. Statistical null models for biological network analysis. 145-170, IMPERIAL COLLEGE PRESS, 2009. doi: 10.1142/9781848164345_0008.
Mark G Thomas, M. STUMPF, Heinrich Härke. Integration versus apartheid in post-Roman Britain: a response to Pattison.. Proceedings. Biological sciences, 275, 2419-2421, 2008. doi: 10.1098/rspb.2008.0677.
Wp Kelly, M. STUMPF. Protein–protein interactions: from global to local analyses. Current opinion in biotechnology, 19, 396-403, 2008. doi: 10.1016/j.copbio.2008.06.010.
DB Thomas, W Luk, M. STUMPF. Reconfigurable hardware acceleration of canonical graph labelling. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4419 LNCS, 302-313, 2007. doi: 10.1007/978-3-540-71431-6_28.
R Chakraborty, M Reinis, T Rostron, S Philpott, T Dong, R Musoke, E Silva, M. STUMPF, B Weiser, H Burger, SL Rowland-Jones, E de Silva. nef gene sequence variation among HIV?1?infected African children*. HIV medicine, 7, 75-84, 2006. doi: 10.1111/j.1468-1293.2006.00341.x.
C Wiuf, M. STUMPF. Binomial subsampling. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 462, 1181-1195, 2006. doi: 10.1098/rspa.2005.1622.
M. STUMPF, PJ Ingram, I Nouvel, C Wiuf. Statistical model selection methods applied to biological networks. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3737 LNBI, 65-77, 2005. doi: 10.1007/11599128_5.
Z Yang, D Stephens, KJ Dawson, A Drummond, G Nicholls, RC Griffiths, HM Wilkinson-Herbots, MA Beaumont, SJE Baird, M Lascoux, R Leblois, A Estoup, R Nielsen, J Hey, M. STUMPF. Discussion on the paper by Wilson, Weale and Balding. Journal of the Royal Statistical Society: Series A (Statistics in Society), 166, 188-201, 2003. doi: 10.1111/1467-985X.00265.
C Capelli, JF Wilson, M Richards, M. STUMPF, F Gratrix, S Oppenheimer, P Underhill, VL Pascali, TM Ko, DB Goldstein. A predominantly indigenous paternal heritage for the Austronesian-speaking peoples of insular Southeast Asia and Oceania.. The American Journal of Human Genetics, 68, 432-443, 2001. doi: 10.1086/318205.
M. STUMPF. Language's place in nature. Trends in Ecology and Evolution, 16, 475-476, 2001. doi: 10.1016/S0169-5347(01)02275-3.