David Schnoerr
I joined the Theoretical Systems Biology Group at the Imperial College London as a postdoc with Michael Stumpf in autumn 2017. I previously worked as a postdoc at the University of Edinburgh where I also obtained my Ph.D. in 2016 under the supervision of Ramon Grima and Guido Sanguinetti. I received my diploma in theoretical physics from the University of Heidelberg in 2013, where I wrote my thesis on Functional Renormalization Group methods in the group of Christof Wetterich.
My research interests include:
Computational systems biology and biophysics
Stochastic processes in biochemical reaction networks
Stochastic reaction-diffusion processes
Self-organisation
Metabolic whole-cell models
Statistical inference
Publications
Neural Field Models for Latent State Inference: Application to Large-Scale Neuronal Recordings. ➟
*M.R. Rule, D. Schnoerr, M.H. Hennig, G. Sanguinetti. bioRxiv:543769.
Turing Patterns Are Common But Not Robust ➟
*N. S. Scholes, *D. Schnoerr, M. Isalan, M. P. H. Stumpf. bioRxiv:352302.
* N.S. Scholes and D. Schnoerr contributed equally
Probabilistic Model Checking for Continuous Time Markov Chains Via Sequential Bayesian Inference ➟
D. Milios, G. Sanguinetti, and D. Schnoerr. arXiv:1711.01863.
Efficient Low-Order Approximation of First-Passage Time Distributions ➟
D. Schnoerr, B. Cseke, G. Sanguinetti, and R. Grima (2017). Physical Review Letters, 119, 210601. arXiv:1706.00348.
An Alternative Route to the System-Size Expansion ➟
D. Schnoerr, A. Piehler, R. Grima (2017). Journal of Physics A: Mathematical and Theoretical, 50.15, 395003.
Approximation and Inference Methods for Stochastic Biochemical Kinetics – A Tutorial Review ➟
D. Schnoerr, G. Sanguinetti, and R. Grima (2017). Journal of Physics A: Mathematical and Theoretical, 50.9, 093001. arxiv:1608.06582.
* Selected for the “Highlights 2017 Collection” of Journal of Physics A
Expectation Propagation for Diffusion Processes by Moment Closure Approximations ➟
*B. Cseke, *D. Schnoerr, M. Opper, G. Sanguinetti (2016). Journal of Physics A: Mathematical and Theoretical 49.49, 494002. arxiv:1512.06098.
* B. Cseke and D. Schnoerr contributed equally
Cox Process Representation and Inference for Stochastic Reaction-Diffusion Processes ➟
D. Schnoerr, R. Grima, G. Sanguinetti (2016). Nature Communications 7, 11729. arxiv:1601.01972
Comparison of Different Moment-Closure Approximations for Stochastic Chemical Kinetics ➟
D. Schnoerr, G. Sanguinetti, R. Grima (2015). The Journal of Chemical Physics 143, 185101. arxiv:1508.01737
Validity Conditions for Moment Closure Approximations in Stochastic Chemical Kinetics ➟
D. Schnoerr, G. Sanguinetti, R. Grima (2014). The Journal of Chemical Physics 141, 084103. arxiv:1407.8316
The Complex Chemical Langevin Equation ➟
D. Schnoerr, G. Sanguinetti, R. Grima (2014). The Journal of Chemical Physics 141, 024103. arXiv:1406.2502
Error Estimates and Specification Parameters for Functional Renormalization ➟
D. Schnoerr, I. Boettcher, J. M. Pawlowski, C. Wetterich (2013). Annals of Physics 334, 83-99. arXiv:1301.4169
Reviewer
The Journal of Chemical Physics
SIAM Journal on Applied Mathematics
Bulletin of Mathematical Biology
Journal of Statistical Mechanics: Theory and Experiment
Journal of Physics A: Mathematical and Theoretical
Journal of The Royal Society Interface
Journal of Theoretical Biology
PLOS One
Frontiers in Genetics
Entropy
AMMCS
Conference and Workshop Talks
“Cox Process Representation and Inference for Stochastic Reaction-Diffusion Processes”
Workshop on Stochastic dynamics on large networks: prediction and inference, October 2018, Max Planck Institute for the Physics of Complex Systems, Dresden, Germany.
“Efficient Approximations of (Spatio-Temporal) Stochastic Processes Using Machine Learning” (Invited)
Nanoscale mathematical modeling of synaptic transmission and calcium dynamics, October 2018, Centro di Ricerca Matematica Ennio De Giorgi, Pisa.
“Efficient Approximations of (Spatio-Temporal) Stochastic Processes Using Machine Learning” (Invited)
Workshop on Multiscale modeling and simulations to bridge molecular and cellular scales, October 2018, Centro di Ricerca Matematica Ennio De Giorgi, Pisa.
“Cox Process Representation and Inference for Stochastic Reaction-Diffusion Processes”
Bioms Symposium, October 2019, BioQuant, Heidelberg University, Germany.
“Cox Process Representation and Inference for Stochastic Reaction-Diffusion Processes”
10th European Conference on Mathematical & Theoretical Biology and SMB Annual Meeting, July 2016, Nottingham, U.K.
“Breakdown of the Chemical Langevin Equation and Moment Closure Approximations for Stochastic Chemical Kinetics”
Mathematical Trends in Reaction Network Theory, July 2015, University of Copenhagen, Denmark.
Seminar Talks (Invited)
“Efficient Approximations of (Spatio-Temporal) Stochastic Processes Using Machine Learning”
Departmental Seminar, December 2018, Helmholtz Center Munich, Institute for Computational Biology, Munich, Germany.
“Efficient Approximations of (Spatio-Temporal) Stochastic Processes Using Machine Learning”
Departmental Seminar, October 2018, Max Planck Institute for the Physics of Complex Systems, Dresden, Germany.
“Modelling the RNA Life Cycle in Yeast Under Stress From RNA-Protein Binding Data”
Departmental Seminar, September 2018, Max Planck Institute for Biophysical Chemistry, Göttingen, Germany.
“Efficient Approximations of (Spatio-Temporal) Stochastic Processes Using Machine Learning”
Biophysics Seminar, September 2018, Department of Physics, University of Göttingen, Germany.
“Efficient Approximations of (Spatio-Temporal) Stochastic Processes Using Machine Learning”
CeNoS Colloquium, May 2018, Center for Nonlinear Science, University of Münster, Germany.
“Modelling the RNA Life Cycle in Yeast Under Stress From RNA-Protein Binding Data”
BIOMS seminar, April 2018, BioQuant, Heidelberg University, Germany.
“Using Ideas Form Statistics for Analysing (Spatio-Temporal) Stochastic Processes”
Biophysics and Soft Matter Seminar, June 12, 2017, Simon Fraser University, Canada.
“Using Ideas Form Statistics for Analysing (Spatio-Temporal) Stochastic Processes”
Industrial and Applied Mathematics Seminar, April 27, 2017, University of Oxford, U.K.
“Cox Process Representation and Inference for Stochastic Reaction-Diffusion Processes”
Biomathematical Seminar, November 2016, Imperial College London, U.K.
“Cox Process Representation and Inference for Stochastic Reaction-Diffusion Processes”
Stochastic Dynamical Systems in Biology: Numerical Methods and Applications, June 2016, Newton Institute, University of Cambridge, U.K.
Teaching
Tutorial on Mathematics and Physics for Biologists (2015)
Tutorial I on Mathematics and Physics for Biologists (2014)
Tutorial II on Mathematics and Physics for Biologists (2014)
Tutorial I on Mathematics for Natural Scientists (2012)
Tutorial II on Mathematics for Natural Scientists (2012)
Tutorial on Theoretical Physics II (2010/11)
Tutorial on Theoretical Physics I (2008/09)