Michael Stumpf

I hold the Chair of Theoretical Systems Biology, and in my research I am trying to understand how cells make decisions (e.g. whether to divide/proliferate, differentiate or commit to controlled cell death). Such decisions are the result of computations done by complex molecular networks. The structure and dynamics of these networks are generally not known and much of my work deals with the development of new methods that allow us to get better and deeper understanding and mechanistic insights into these networks.

We use mathematical models, coupled to state-of-the-art statistical methods – generally developed in the group – to analyse biological systems, that range from stem cells to bacterial organisms. Often these systems show rich dynamical behaviour, which is caused by the interplay of non-linearities and stochastic dynamics; the differentiation of stem cells and the development of tissues offer good examples for this. The analysis of such data, and the development of useful, that is quantitative, predictive and explanatory, models offers a wealth of statistical and intellectual challenges, in addition to the intrinsic biological and biomedical interest in such systems. We work in close collaboration with experimental and clinical groups wherever possible.

Because we combine a broad range of mathematical, statistical and computational tools, including bioinformatics, evolutionary analysis and text-mining, we have been able to make progress in the analysis of molecular machines (such as the proteasome), signal transduction and gene regulation networks, as well developmental processes. More recently we have started to consider different aspects of the population and evolutionary dynamics of stem cells and their progeny in both health and disease, especially cancer.

In silico models of biological systems have phenomenal scope in guiding future experiments, replacing and reducing the number of experiments carried out in animals. In addition to modelling and statistical approaches we also use comparative genomics to identify and develop suitable alternative experimental model systems.

My original background is in theoretical physics, which I studied in Tübingen, Göttingen and Oxford. But straight after my DPhil in Statistical Physics in Oxford I moved into biological research in the Department of Zoology in Oxford, a move that I have never regretted. Some of our work has, nevertheless, implications for physics and is published in the physics literature.

Research Interests

The research in the Theoretical Systems Biology Group uses mathematical, statistical and computational methods to explore functional, evolutionary and statistical problems in systems biology. The methods we use can be applied widely in systems biology, developmental and stem cell biology.

Our research covers biological problems related to:

  • Information processing and cell-fate decision machineries in eukaryotic and prokaryotic cells
  • Regulatory and signalling networks in (embryonic and haematopietic) stem cell differentiation
  • Innate immune dynamics analysed via in vivo imaging and molecular data
  • Stem cell population dynamics in haematopoiesis
  • Regulatory and genomic determinants of haemtatopietic cancers, including leukaemia and multiple myeloma
  • Making better mechanstic models for cellular machines such as the proteasome

We address these problems using sophisticated new modelling and analysis tools, which we develop in the group. Our methodological work is centred around Bayesian reverse engineering of biological systems using statistical, comparative and text-mining approaches. In particular we are interested in

  • Development of inferential procedures for model selection and parameter estimation in complex dynamical systems
  • Network inference using graphical models and information theoretical functionals.
  • Approximate Bayesian computation, especially for challenging scientific modelling problems involving multi-scale problems, agent-based approaches and non-linear stochastic systems
  • Stochastic processes and statistical inference in the functional and evolutionary analysis of biological networks

Brief CV

  • 2007 - Professor for Theoretical Systems Biology, Imperial College London
  • 2003-2006 Reader in Bioinformatics and Wellcome Trust Research Career Development Fellow, Imperial College London
  • 2002-2003 Wellcome Trust Research Career Development Fellow, University College London
  • 1999-2002 Wellcome Trust Mathematical Biology Research Training Fellowship, University of Oxford and EPA Cephalosporin Junion Research Fellow Linacre College Oxford
  • 1995-1999 DPhil Student, University of Oxford (Kekule Scholarship and Balliol College Jowett Exhibition)
  • 1990-1995 Physics studies at the Universities of Tübingen and Göttingen finishing with Diplom (1.0) in Göttingen

Honours and Awards

  • 2013 Mieyungah Distinguished Fellowship, University of Melbourne
  • 2012 Election to Fellowship of the Society of Biology
  • 2010 Royal Society Wolfson Merit Award
  • 2005 EMBO Young Investigator

Selected Publications

Stem Cell Population Biology: Insights from Haematopoiesis.

MacLean AL, Lo Celso C, Stumpf MP.

Stem Cells. 2016 Sep 27. doi: 10.1002/stem.2508. Review.


Accurate Reconstruction of Cell and Particle Tracks from 3D Live Imaging Data.

Liepe J, Sim A, Weavers H, Ward L, Martin P, Stumpf MP.

Cell Syst. 2016 Jul;3(1):102-7. doi: 10.1016/j.cels.2016.06.002.

Systems Analysis of the Dynamic Inflammatory Response to Tissue Damage Reveals Spatiotemporal Properties of the Wound Attractant Gradient.

Weavers H, Liepe J, Sim A, Wood W, Martin P, Stumpf MP.

Curr Biol. 2016 Aug 8;26(15):1975-89. doi: 10.1016/j.cub.2016.06.012.

Systematic tracking of altered haematopoiesis during sporozoite-mediated malaria development reveals multiple response points.

Vainieri ML, Blagborough AM, MacLean AL, Haltalli ML, Ruivo N, Fletcher HA, Stumpf MP, Sinden RE, Celso CL.

Open Biol. 2016 Jun;6(6). pii: 160038. doi: 10.1098/rsob.160038.

Robustness of MEK-ERK Dynamics and Origins of Cell-to-Cell Variability in MAPK Signaling.

Filippi S, Barnes CP, Kirk PD, Kudo T, Kunida K, McMahon SS, Tsuchiya T, Wada T, Kuroda S, Stumpf MP.

Cell Rep. 2016 Jun 14;15(11):2524-35. doi: 10.1016/j.celrep.2016.05.024.

MEANS: python package for Moment Expansion Approximation, iNference and Simulation.

Fan S, Geissmann Q, Lakatos E, Lukauskas S, Ale A, Babtie AC, Kirk PD, Stumpf MP.

Bioinformatics. 2016 Sep 15;32(18):2863-5. doi: 10.1093/bioinformatics/btw229.

Feedback mechanisms control coexistence in a stem cell model of acute myeloid leukaemia.

Crowell HL, MacLean AL, Stumpf MP.

J Theor Biol. 2016 Jul 21;401:43-53. doi: 10.1016/j.jtbi.2016.04.002.

Coalescent models for developmental biology and the spatio-temporal dynamics of growing tissues.

Smadbeck P, Stumpf MP.

J R Soc Interface. 2016 Apr;13(117). pii: 20160112. doi: 10.1098/rsif.2016.0112.

A graph theoretical approach to data fusion.

Žurauskienė J, Kirk PD, Stumpf MP.

Stat Appl Genet Mol Biol. 2016 Apr;15(2):107-22. doi: 10.1515/sagmb-2016-0016.


Mathematical and Statistical Techniques for Systems Medicine: The Wnt Signaling Pathway as a Case Study.

MacLean AL, Harrington HA, Stumpf MP, Byrne HM.

Methods Mol Biol. 2016;1386:405-39. doi: 10.1007/978-1-4939-3283-2_18. Review.


SYSTEMS BIOLOGY. Systems biology (un)certainties.

Kirk PD, Babtie AC, Stumpf MP.

Science. 2015 Oct 23;350(6259):386-8. doi: 10.1126/science.aac9505. No abstract available.


Cellular population dynamics control the robustness of the stem cell niche.

MacLean AL, Kirk PD, Stumpf MP.

Biol Open. 2015 Oct 9;4(11):1420-6. doi: 10.1242/bio.013714.

Inference of random walk models to describe leukocyte migration.

Jones PJ, Sim A, Taylor HB, Bugeon L, Dallman MJ, Pereira B, Stumpf MP, Liepe J.

Phys Biol. 2015 Sep 25;12(6):066001. doi: 10.1088/1478-3975/12/6/066001.


Quantitative time-resolved analysis reveals intricate, differential regulation of standard- and immuno-proteasomes.

Liepe J, Holzhütter HG, Bellavista E, Kloetzel PM, Stumpf MP, Mishto M.

Elife. 2015 Sep 22;4:e07545. doi: 10.7554/eLife.07545.


Free PMC Article

Multivariate moment closure techniques for stochastic kinetic models.

Lakatos E, Ale A, Kirk PD, Stumpf MP.

J Chem Phys. 2015 Sep 7;143(9):094107. doi: 10.1063/1.4929837.


Information processing by simple molecular motifs and susceptibility to noise.

Mc Mahon SS, Lenive O, Filippi S, Stumpf MP.

J R Soc Interface. 2015 Sep 6;12(110):0597. doi: 10.1098/rsif.2015.0597.

Great cities look small.

Sim A, Yaliraki SN, Barahona M, Stumpf MP.

J R Soc Interface. 2015 Aug 6;12(109):20150315. doi: 10.1098/rsif.2015.0315.

Goldstein-Kac telegraph processes with random speeds: Path probabilities, likelihoods, and reported Lévy flights.

Sim A, Liepe J, Stumpf MP.

Phys Rev E Stat Nonlin Soft Matter Phys. 2015 Apr;91(4):042115.


Phosphorelay of non-orthodox two component systems functions through a bi-molecular mechanism in vivo: the case of ArcB.

Jovanovic G, Sheng X, Ale A, Feliu E, Harrington HA, Kirk P, Wiuf C, Buck M, Stumpf MP.

Mol Biosyst. 2015 May;11(5):1348-59. doi: 10.1039/c4mb00720d.


Topological sensitivity analysis for systems biology.

Babtie AC, Kirk P, Stumpf MP.

Proc Natl Acad Sci U S A. 2014 Dec 30;111(52):18507-12. doi: 10.1073/pnas.1414026112.

SYSBIONS: nested sampling for systems biology.

Johnson R, Kirk P, Stumpf MP.

Bioinformatics. 2015 Feb 15;31(4):604-5. doi: 10.1093/bioinformatics/btu675.

Proteasome isoforms exhibit only quantitative differences in cleavage and epitope generation.

Mishto M, Liepe J, Textoris-Taube K, Keller C, Henklein P, Weberruß M, Dahlmann B, Enenkel C, Voigt A, Kuckelkorn U, Stumpf MP, Kloetzel PM.

Eur J Immunol. 2014 Dec;44(12):3508-21. doi: 10.1002/eji.201444902.

Overlapping genes: a window on gene evolvability.

Huvet M, Stumpf MP.

BMC Genomics. 2014 Aug 27;15:721. doi: 10.1186/1471-2164-15-721.

Approximate Bayesian inference for complex ecosystems.

Stumpf MP.

F1000Prime Rep. 2014 Jul 17;6:60. doi: 10.12703/P6-60. Review.

Modelling proteasome and proteasome regulator activities.

Liepe J, Holzhütter HG, Kloetzel PM, Stumpf MP, Mishto M.

Biomolecules. 2014 Jun 20;4(2):585-99. doi: 10.3390/biom4020585. Review.

Information theory and signal transduction systems: from molecular information processing to network inference.

Mc Mahon SS, Sim A, Filippi S, Johnson R, Liepe J, Smith D, Stumpf MP.

Semin Cell Dev Biol. 2014 Nov;35:98-108. doi: 10.1016/j.semcdb.2014.06.011. Review.


Nuclear to cytoplasmic shuttling of ERK promotes differentiation of muscle stem/progenitor cells.

Michailovici I, Harrington HA, Azogui HH, Yahalom-Ronen Y, Plotnikov A, Ching S, Stumpf MP, Klein OD, Seger R, Tzahor E.

Development. 2014 Jul;141(13):2611-20. doi: 10.1242/dev.107078.

Model selection in systems biology depends on experimental design.

Silk D, Kirk PD, Barnes CP, Toni T, Stumpf MP.

PLoS Comput Biol. 2014 Jun 12;10(6):e1003650. doi: 10.1371/journal.pcbi.1003650.

The ecology in the hematopoietic stem cell niche determines the clinical outcome in chronic myeloid leukemia.

MacLean AL, Filippi S, Stumpf MP.

Proc Natl Acad Sci U S A. 2014 Mar 11;111(10):3883-8. doi: 10.1073/pnas.1317072111.

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 CP, Stumpf MP.

Nat Protoc. 2014 Feb;9(2):439-56. doi: 10.1038/nprot.2014.025.


Nitrogen and carbon status are integrated at the transcriptional level by the nitrogen regulator NtrC in vivo.

Schumacher J, Behrends V, Pan Z, Brown DR, Heydenreich F, Lewis MR, Bennett MH, Razzaghi B, Komorowski M, Barahona M, Stumpf MP, Wigneshweraraj S, Bundy JG, Buck M.

MBio. 2013 Nov 19;4(6):e00881-13. doi: 10.1128/mBio.00881-13.

StochDecomp–Matlab package for noise decomposition in stochastic biochemical systems.

Jetka T, Charzyńska A, Gambin A, Stumpf MP, Komorowski M.

Bioinformatics. 2014 Jan 1;30(1):137-8. doi: 10.1093/bioinformatics/btt631.

Optimizing threshold-schedules for sequential approximate Bayesian computation: applications to molecular systems.

Silk D, Filippi S, Stumpf MP.

Stat Appl Genet Mol Biol. 2013 Oct 1;12(5):603-18. doi: 10.1515/sagmb-2012-0043.


Balancing the robustness and predictive performance of biomarkers.

Kirk P, Witkover A, Bangham CR, Richardson S, Lewin AM, Stumpf MP.

J Comput Biol. 2013 Dec;20(12):979-89. doi: 10.1089/cmb.2013.0018.


A general moment expansion method for stochastic kinetic models.

Ale A, Kirk P, Stumpf MP.

J Chem Phys. 2013 May 7;138(17):174101. doi: 10.1063/1.4802475.


Cellular compartments cause multistability and allow cells to process more information.

Harrington HA, Feliu E, Wiuf C, Stumpf MP.

Biophys J. 2013 Apr 16;104(8):1824-31. doi: 10.1016/j.bpj.2013.02.028.

Decomposing noise in biochemical signaling systems highlights the role of protein degradation.

Komorowski M, Miękisz J, Stumpf MP.

Biophys J. 2013 Apr 16;104(8):1783-93. doi: 10.1016/j.bpj.2013.02.027.

Graphical modelling of molecular networks underlying sporadic inclusion body myositis.

Thorne T, Fratta P, Hanna MG, Cortese A, Plagnol V, Fisher EM, Stumpf MP.

Mol Biosyst. 2013 Jul;9(7):1736-42. doi: 10.1039/c3mb25497f.


Model selection in systems and synthetic biology.

Kirk P, Thorne T, Stumpf MP.

Curr Opin Biotechnol. 2013 Aug;24(4):767-74. doi: 10.1016/j.copbio.2013.03.012. Review.


On optimality of kernels for approximate Bayesian computation using sequential Monte Carlo.

Filippi S, Barnes CP, Cornebise J, Stumpf MP.

Stat Appl Genet Mol Biol. 2013 Mar 26;12(1):87-107. doi: 10.1515/sagmb-2012-0069.


Maximizing the information content of experiments in systems biology.

Liepe J, Filippi S, Komorowski M, Stumpf MP.

PLoS Comput Biol. 2013;9(1):e1002888. doi: 10.1371/journal.pcbi.1002888.

Population dynamics of normal and leukaemia stem cells in the haematopoietic stem cell niche show distinct regimes where leukaemia will be controlled.

MacLean AL, Lo Celso C, Stumpf MP.

J R Soc Interface. 2013 Apr 6;10(81):20120968. doi: 10.1098/rsif.2012.0968.

Bayesian design strategies for synthetic biology.

Barnes CP, Silk D, Stumpf MP.

Interface Focus. 2011 Dec 6;1(6):895-908. doi: 10.1098/rsfs.2011.0056.

P38 and JNK have opposing effects on persistence of in vivo leukocyte migration in zebrafish.

Taylor HB, Liepe J, Barthen C, Bugeon L, Huvet M, Kirk PD, Brown SB, Lamb JR, Stumpf MP, Dallman MJ.

Immunol Cell Biol. 2013 Jan;91(1):60-9. doi: 10.1038/icb.2012.57.

Inference of temporally varying Bayesian networks.

Thorne T, Stumpf MP.

Bioinformatics. 2012 Dec 15;28(24):3298-305. doi: 10.1093/bioinformatics/bts614.

Perturbation of fetal liver hematopoietic stem and progenitor cell development by trisomy 21.

Roy A, Cowan G, Mead AJ, Filippi S, Bohn G, Chaidos A, Tunstall O, Chan JK, Choolani M, Bennett P, Kumar S, Atkinson D, Wyatt-Ashmead J, Hu M, Stumpf MP, Goudevenou K, O’Connor D, Chou ST, Weiss MJ, Karadimitris A, Jacobsen SE, Vyas P, Roberts I.

Proc Natl Acad Sci U S A. 2012 Oct 23;109(43):17579-84. doi: 10.1073/pnas.1211405109.

Parameter-free model discrimination criterion based on steady-state coplanarity.

Harrington HA, Ho KL, Thorne T, Stumpf MP.

Proc Natl Acad Sci U S A. 2012 Sep 25;109(39):15746-51.

Evolutionary characteristics of bacterial two-component systems.

Sheng X, Huvet M, Pinney JW, Stumpf MP.

Adv Exp Med Biol. 2012;751:121-37. doi: 10.1007/978-1-4614-3567-9_6.


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

Toni T, Ozaki Y, Kirk P, Kuroda S, Stumpf MP.

Mol Biosyst. 2012 Jul 6;8(7):1921-9. doi: 10.1039/c2mb05493k.


Graph spectral analysis of protein interaction network evolution.

Thorne T, Stumpf MP.

J R Soc Interface. 2012 Oct 7;9(75):2653-66. doi: 10.1098/rsif.2012.0220.

Mathematical modeling reveals the functional implications of the different nuclear shuttling rates of Erk1 and Erk2.

Harrington HA, Komorowski M, Beguerisse-Díaz M, Ratto GM, Stumpf MP.

Phys Biol. 2012 Jun;9(3):036001. doi: 10.1088/1478-3975/9/3/036001.


StochSens–Matlab package for sensitivity analysis of stochastic chemical systems.

Komorowski M, Zurauskiene J, Stumpf MP.

Bioinformatics. 2012 Mar 1;28(5):731-3. doi: 10.1093/bioinformatics/btr714.

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

Liepe J, Taylor H, Barnes CP, Huvet M, Bugeon L, Thorne T, Lamb JR, Dallman MJ, Stumpf MP.

Integr Biol (Camb). 2012 Mar;4(3):335-45. doi: 10.1039/c2ib00175f.

Mathematics. Critical truths about power laws.

Stumpf MP, Porter MA.

Science. 2012 Feb 10;335(6069):665-6. doi: 10.1126/science.1216142. No abstract available.


The degree distribution of networks: statistical model selection.

Kelly WP, Ingram PJ, Stumpf MP.

Methods Mol Biol. 2012;804:245-62. doi: 10.1007/978-1-61779-361-5_13.


Which species is it? Species-driven gene name disambiguation using random walks over a mixture of adjacency matrices.

Harmston N, Filsell W, Stumpf MP.

Bioinformatics. 2012 Jan 15;28(2):254-60. doi: 10.1093/bioinformatics/btr640.

The roles of contact residue disorder and domain composition in characterizing protein-ligand binding specificity and promiscuity.

Tang Y, Sheng X, Stumpf MP.

Mol Biosyst. 2011 Dec;7(12):3280-6. doi: 10.1039/c1mb05325f.


Plasma proteome analysis in HTLV-1-associated myelopathy/tropical spastic paraparesis.

Kirk PD, Witkover A, Courtney A, Lewin AM, Wait R, Stumpf MP, Richardson S, Taylor GP, Bangham CR.

Retrovirology. 2011 Oct 12;8:81. doi: 10.1186/1742-4690-8-81.

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

Silk D, Kirk PD, Barnes CP, Toni T, Rose A, Moon S, Dallman MJ, Stumpf MP.

Nat Commun. 2011 Oct 4;2:489. doi: 10.1038/ncomms1496.

Bayesian design of synthetic biological systems.

Barnes CP, Silk D, Sheng X, Stumpf MP.

Proc Natl Acad Sci U S A. 2011 Sep 13;108(37):15190-5. doi: 10.1073/pnas.1017972108.

Assessing coverage of protein interaction data using capture-recapture models.

Kelly WP, Stumpf MP.

Bull Math Biol. 2012 Feb;74(2):356-74. doi: 10.1007/s11538-011-9680-2.


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 MP.

BMC Syst Biol. 2011 May 12;5:69. doi: 10.1186/1752-0509-5-69.

Sensitivity, robustness, and identifiability in stochastic chemical kinetics models.

Komorowski M, Costa MJ, Rand DA, Stumpf MP.

Proc Natl Acad Sci U S A. 2011 May 24;108(21):8645-50. doi: 10.1073/pnas.1015814108.

GPU accelerated biochemical network simulation.

Zhou Y, Liepe J, Sheng X, Stumpf MP, Barnes C.

Bioinformatics. 2011 Mar 15;27(6):874-6. doi: 10.1093/bioinformatics/btr015.

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

Thorne TW, Ho HL, Huvet M, Haynes K, Stumpf MP.

Fungal Genet Biol. 2011 May;48(5):504-11. doi: 10.1016/j.fgb.2010.12.005.


What the papers say: text mining for genomics and systems biology.

Harmston N, Filsell W, Stumpf MP.

Hum Genomics. 2010 Oct;5(1):17-29. Review.

The evolution of the phage shock protein response system: interplay between protein function, genomic organization, and system function.

Huvet M, Toni T, Sheng X, Thorne T, Jovanovic G, Engl C, Buck M, Pinney JW, Stumpf MP.

Mol Biol Evol. 2011 Mar;28(3):1141-55. doi: 10.1093/molbev/msq301.

Statistical inference of the time-varying structure of gene-regulation networks.

Lèbre S, Becq J, Devaux F, Stumpf MP, Lelandais G.

BMC Syst Biol. 2010 Sep 22;4:130. doi: 10.1186/1752-0509-4-130.

Trees on networks: resolving statistical patterns of phylogenetic similarities among interacting proteins.

Kelly WP, Stumpf MP.

BMC Bioinformatics. 2010 Sep 20;11:470. doi: 10.1186/1471-2105-11-470.

Parameter inference and model selection in signaling pathway models.

Toni T, Stumpf MP.

Methods Mol Biol. 2010;673:283-95. doi: 10.1007/978-1-60761-842-3_18. Review.


Managing membrane stress: the phage shock protein (Psp) response, from molecular mechanisms to physiology.

Joly N, Engl C, Jovanovic G, Huvet M, Toni T, Sheng X, Stumpf MP, Buck M.

FEMS Microbiol Rev. 2010 Sep;34(5):797-827. doi: 10.1111/j.1574-6976.2010.00240.x. Review.

ABC-SysBio–approximate Bayesian computation in Python with GPU support.

Liepe J, Barnes C, Cule E, Erguler K, Kirk P, Toni T, Stumpf MP.

Bioinformatics. 2010 Jul 15;26(14):1797-9. doi: 10.1093/bioinformatics/btq278.

Incomplete and noisy network data as a percolation process.

Stumpf MP, Wiuf C.

J R Soc Interface. 2010 Oct 6;7(51):1411-9. doi: 10.1098/rsif.2010.0044.

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

Toni T, Stumpf MP.

Bioinformatics. 2010 Jan 1;26(1):104-10. doi: 10.1093/bioinformatics/btp619.

The ABC of reverse engineering biological signalling systems.

Secrier M, Toni T, Stumpf MP.

Mol Biosyst. 2009 Dec;5(12):1925-35. doi: 10.1039/b908951a.


Model-based evolutionary analysis: the natural history of phage-shock stress response.

Huvet M, Toni T, Tan H, Jovanovic G, Engl C, Buck M, Stumpf MP.

Biochem Soc Trans. 2009 Aug;37(Pt 4):762-7. doi: 10.1042/BST0370762.


Evolution of pathogenicity and sexual reproduction in eight Candida genomes.

Butler G, Rasmussen MD, Lin MF, Santos MA, Sakthikumar S, Munro CA, Rheinbay E, Grabherr M, Forche A, Reedy JL, Agrafioti I, Arnaud MB, Bates S, Brown AJ, Brunke S, Costanzo MC, Fitzpatrick DA, de Groot PW, Harris D, Hoyer LL, Hube B, Klis FM, Kodira C, Lennard N, Logue ME, Martin R, Neiman AM, Nikolaou E, Quail MA, Quinn J, Santos MC, Schmitzberger FF, Sherlock G, Shah P, Silverstein KA, Skrzypek MS, Soll D, Staggs R, Stansfield I, Stumpf MP, Sudbery PE, Srikantha T, Zeng Q, Berman J, Berriman M, Heitman J, Gow NA, Lorenz MC, Birren BW, Kellis M, Cuomo CA.

Nature. 2009 Jun 4;459(7247):657-62. doi: 10.1038/nature08064.

Evolving proteins at Darwin’s bicentenary.

Pinney JW, Stumpf MP.

Genome Biol. 2009;10(4):307. doi: 10.1186/gb-2009-10-4-307.

Gaussian process regression bootstrapping: exploring the effects of uncertainty in time course data.

Kirk PD, Stumpf MP.

Bioinformatics. 2009 May 15;25(10):1300-6. doi: 10.1093/bioinformatics/btp139.

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

Toni T, Welch D, Strelkowa N, Ipsen A, Stumpf MP.

J R Soc Interface. 2009 Feb 6;6(31):187-202.

Nonidentifiability of the source of intrinsic noise in gene expression from single-burst data.

Ingram PJ, Stumpf MP, Stark J.

PLoS Comput Biol. 2008 Oct;4(10):e1000192. doi: 10.1371/journal.pcbi.1000192.

Statistical interpretation of the interplay between noise and chaos in the stochastic logistic map.

Erguler K, Stumpf MP.

Math Biosci. 2008 Nov;216(1):90-9. doi: 10.1016/j.mbs.2008.08.012.


Estimating the size of the human interactome.

Stumpf MP, Thorne T, de Silva E, Stewart R, An HJ, Lappe M, Wiuf C.

Proc Natl Acad Sci U S A. 2008 May 13;105(19):6959-64. doi: 10.1073/pnas.0708078105.

Parameter inference for biochemical systems that undergo a Hopf bifurcation.

Kirk PD, Toni T, Stumpf MP.

Biophys J. 2008 Jul;95(2):540-9. doi: 10.1529/biophysj.107.126086.

Generating confidence intervals on biological networks.

Thorne T, Stumpf MP.

BMC Bioinformatics. 2007 Nov 30;8:467.

Evolution at the system level: the natural history of protein interaction networks.

Stumpf MP, Kelly WP, Thorne T, Wiuf C.

Trends Ecol Evol. 2007 Jul;22(7):366-73. Review.


SNPSTR: a database of compound microsatellite-SNP markers.

Agrafioti I, Stumpf MP.

Nucleic Acids Res. 2007 Jan;35(Database issue):D71-5.

Systems biology and its impact on anti-infective drug development.

Stumpf MP, Robertson BD, Duncan K, Young DB.

Prog Drug Res. 2007;64:1, 3-20. Review.


The effects of incomplete protein interaction data on structural and evolutionary inferences.

de Silva E, Thorne T, Ingram P, Agrafioti I, Swire J, Wiuf C, Stumpf MP.

BMC Biol. 2006 Nov 3;4:39.

Evidence for an apartheid-like social structure in early Anglo-Saxon England.

Thomas MG, Stumpf MP, Härke H.

Proc Biol Sci. 2006 Oct 22;273(1601):2651-7.

Complex networks and simple models in biology.

de Silva E, Stumpf MP.

J R Soc Interface. 2005 Dec 22;2(5):419-30. Review.

Induction and function of the phage shock protein extracytoplasmic stress response in Escherichia coli.

Jovanovic G, Lloyd LJ, Stumpf MP, Mayhew AJ, Buck M.

J Biol Chem. 2006 Jul 28;281(30):21147-61.

Allelic histories: positive selection on a HIV-resistance allele.

Stumpf MP, Wilkinson-Herbots HM.

Trends Ecol Evol. 2004 Apr;19(4):166-8.


A likelihood approach to analysis of network data.

Wiuf C, Brameier M, Hagberg O, Stumpf MP.

Proc Natl Acad Sci U S A. 2006 May 16;103(20):7566-70.

Network motifs: structure does not determine function.

Ingram PJ, Stumpf MP, Stark J.

BMC Genomics. 2006 May 5;7:108.

Sampling properties of random graphs: the degree distribution.

Stumpf MP, Wiuf C.

Phys Rev E Stat Nonlin Soft Matter Phys. 2005 Sep;72(3 Pt 2):036118.


Ecology. Making sense of evolution in an uncertain world.

Jansen VA, Stumpf MP.

Science. 2005 Sep 23;309(5743):2005-7. No abstract available.


Recombination hotspots as a point process.

De Iorio M, de Silva E, Stumpf MP.

Philos Trans R Soc Lond B Biol Sci. 2005 Aug 29;360(1460):1597-603.

Introduction: genetic variation and human health.

Stumpf MP, Goldstein DB, Wood NW.

Philos Trans R Soc Lond B Biol Sci. 2005 Aug 29;360(1460):1539-41. No abstract available.

Comparative analysis of the Saccharomyces cerevisiae and Caenorhabditis elegans protein interaction networks.

Agrafioti I, Swire J, Abbott J, Huntley D, Butcher S, Stumpf MP.

BMC Evol Biol. 2005 Mar 18;5:23.

Subnets of scale-free networks are not scale-free: sampling properties of networks.

Stumpf MP, Wiuf C, May RM.

Proc Natl Acad Sci U S A. 2005 Mar 22;102(12):4221-4.

The extent and importance of intragenic recombination.

de Silva E, Kelley LA, Stumpf MP.

Hum Genomics. 2004 Nov;1(6):410-20.

HIV and the CCR5-Delta32 resistance allele.

de Silva E, Stumpf MP.

FEMS Microbiol Lett. 2004 Dec 1;241(1):1-12. Review.

Estimating recombination rates from population-genetic data.

Stumpf MP, McVean GA.

Nat Rev Genet. 2003 Dec;4(12):959-68. Review.


Some notes on the combinatorial properties of haplotype tagging.

Wiuf C, Laidlaw Z, Stumpf MP.

Math Biosci. 2003 Oct;185(2):205-16.


A Y chromosome census of the British Isles.

Capelli C, Redhead N, Abernethy JK, Gratrix F, Wilson JF, Moen T, Hervig T, Richards M, Stumpf MP, Underhill PA, Bradshaw P, Shaha A, Thomas MG, Bradman N, Goldstein DB.

Curr Biol. 2003 May 27;13(11):979-84.

Balancing selection at the prion protein gene consistent with prehistoric kurulike epidemics.

Mead S, Stumpf MP, Whitfield J, Beck JA, Poulter M, Campbell T, Uphill JB, Goldstein D, Alpers M, Fisher EM, Collinge J.

Science. 2003 Apr 25;300(5619):640-3.

Herpes viruses hedge their bets.

Stumpf MP, Laidlaw Z, Jansen VA.

Proc Natl Acad Sci U S A. 2002 Nov 12;99(23):15234-7.

Genetic diversity and models of viral evolution for the hepatitis C virus.

Stumpf MP, Pybus OG.

FEMS Microbiol Lett. 2002 Sep 10;214(2):143-52. Review.

Haplotype diversity and the block structure of linkage disequilibrium.

Stumpf MP.

Trends Genet. 2002 May;18(5):226-8.


Population genomics: ageing by association.

Pletcher SD, Stumpf MP.

Curr Biol. 2002 Apr 30;12(9):R328-30.

Theoretical models of sheep BSE reveal possibilities.

Krebs JR, May RM, Stumpf MP.

Nature. 2002 Jan 10;415(6868):115. No abstract available.


Genealogical and evolutionary inference with the human Y chromosome.

Stumpf MP, Goldstein DB.

Science. 2001 Mar 2;291(5509):1738-42. Review.


Ecology. Species-area relations in tropical forests.

May RM, Stumpf MP.

Science. 2000 Dec 15;290(5499):2084-6.


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

Capelli C, Wilson JF, Richards M, Stumpf MP, Gratrix F, Oppenheimer S, Underhill P, Pascali VL, Ko TM, Goldstein DB.

Am J Hum Genet. 2001 Feb;68(2):432-43.

Mapping the parameters of prion-induced neuropathology.

Stumpf MP, Krakauer DC.

Proc Natl Acad Sci U S A. 2000 Sep 12;97(19):10573-7.