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 PhD 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.
Short CV: [PDF]
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Research interests
- 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]
- 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
Physical Review Letters, 119, 210601 (2017).
[arXiv:1706.00348]
- An alternative route to the system-size expansion.
C. Cianci, D. Schnoerr, A. Piehler, R. Grima
Journal of Physics A: Mathematical and Theoretical, 50.15, 395003 (2017). - Approximation and inference methods for stochastic biochemical kinetics – a tutorial review.
† D. Schnoerr, G. Sanguinetti, and R. Grima
Journal of Physics A: Mathematical and Theoretical, 50.9, 093001 (2017).
[arxiv:1608.06582]
- Expectation propagation for diffusion processes by moment closure approximations.
‡ B. Cseke>, ‡ D. Schnoerr, M. Opper, G. Sanguinetti
Journal of Physics A: Mathematical and Theoretical 49.49, 494002 (2016).
[arxiv:1512.06098] - Cox process representation and inference for stochastic reaction-diffusion processes
D. Schnoerr, R. Grima, G. Sanguinetti
Nature Communications 7, 11729 (2016).
[arxiv:1601.01972] - Comparison of different moment-closure approximations for stochastic chemical kinetics
D. Schnoerr, G. Sanguinetti, R. Grima
The Journal of Chemical Physics 143, 185101 (2015).
[arxiv:1508.01737] - Validity conditions for moment closure approximations in stochastic chemical kinetics
D. Schnoerr, G. Sanguinetti, R. Grima
The Journal of Chemical Physics 141, 084103 (2014).
[arxiv:1407.8316] - The complex chemical Langevin equation
D. Schnoerr, G. Sanguinetti, R. Grima
The Journal of Chemical Physics 141, 024103 (2014).
[arXiv:1406.2502] - Error estimates and specification parameters for functional renormalization
D. Schnoerr, I. Boettcher, J. M. Pawlowski, C. Wetterich
Annals of Physics 334, 83-99 (2013).
[arXiv:1301.4169]
* N. S. Scholes and D. Schnoerr contributed equally
† Selected for the “Highlights 2017 Collection” of Journal of Physics A
‡ B. Cseke and D. Schnoerr contributed equally
Reviewer for
- 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. - (invited) “Efficient approximations of (spatio-temporal) stochastic processes using machine learning”.
Nanoscale mathematical modeling of synaptic transmis- sion and calcium dynamics, October 2018,
Centro di Ricerca Matematica Ennio De Giorgi, Pisa. - (invited) “Efficient approximations of (spatio-temporal) stochastic processes using machine learning”.
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, UK.
- “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, UK. - “Cox process representation and inference for stochastic reaction-diffusion processes”,
Biomathematical Seminar, November 2016, Imperial College London, UK. - “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, UK.
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)