Publications

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  • Lucy Ham, David Schnoerr, Rowan Brackston, M. STUMPF. Exactly solvable models of stochastic gene expression. 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, 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.

  • Leander Dony, Fei He, M. STUMPF. Parametric and non-parametric gradient matching for network inference: a comparison.. BMC bioinformatics, 20, 52, 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. Cold Spring Harbor Laboratory, 2019. doi: 10.1101/858308.

  • Elisabeth Roesch, M. STUMPF. Parameter inference in dynamical systems with co-dimension 1 bifurcations. Cold Spring Harbor Laboratory, 2019. doi: 10.1101/623413.

  • Lucy Ham, Rowan Brackston, M. STUMPF. Extrinsic noise and heavy-tailed laws in gene expression. 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, 243-257.e4, 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, 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, 2018. doi: 10.1038/s41467-018-07085-1.

  • Rowan Brackston, Eszter Lakatos, M. STUMPF. Transition State Characteristics During Cell Differentiation. 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. Cold Spring Harbor Laboratory, 2018. doi: 10.1101/264853.

  • M. STUMPF. Biology challenging statistics.. Statistical applications in genetics and molecular biology, 17, 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.

  • 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.

  • 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.

  • M. STUMPF. Neuroscience of birdsong.. Human genomics, 4, 143-144, 2009. doi: 10.1186/1479-7364-4-2-143.

  • 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.