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Dr. Andreas Ruttor

Lupe [1]
Lupe [2]

Scientific Assistant
Office: MAR 4.019
Phone: +49 30 314-23938
E-Mail: andreas.ruttor <AT> tu-berlin.de


Consulting hour according to agreement.

Curriculum vitae

Andreas Ruttor
Born 1977

Academic Career
Period
Occupation
since 2007
Post-Doc Researcher at TU Berlin, Artificial Intelligence Group

2007
Ph. D. in Physics, Universität Würzburg
2003-2007
Research Assistant at Universität Würzburg, Statistical Physics Group

2003
Master in Physics, Universität Würzburg

Research Fields

  • Stochastic dynamical systems (exact and approximate inference, model selection)
  • Statistical learning theory (Gaussian processes, neural networks)
  • Statistical physics of complex systems
  • Applications: Systems biology, data analysis, cryptography

Publications

Switching Regulatory Models of Cellular Stress Response
Citation key Sanguinetti:2009:SRM
Author Guido Sanguinetti and Andreas Ruttor and Manfred Opper and Cedric Archambeau
Pages 1280-1286
Year 2009
DOI 10.1093/bioinformatics/btp138
Journal Bioinformatics
Volume 25
Number 10
Abstract Stress response in cells is often mediated by quick activation of transcription factors (TFs). Given the difficulty in experimentally assaying TF activities, several statistical approaches have been proposed to infer them from microarray time courses. However, these approaches often rely on prior assumptions which rule out the rapid responses observed during stress response. We present a novel statistical model to infer how TFs mediate stress response in cells. The model is based on the assumption that sensory TFs quickly transit between active and inactive states. We therefore model mRNA production using a bistable dynamical systems whose behaviour is described by a system of differential equations driven by a latent stochastic process. We assume the stochastic process to be a two-state continuous time jump process, and devise both an exact solution for the inference problem as well as an efficient approximate algorithm. We evaluate the method on both simulated data and real data describing Escherichia coli's response to sudden oxygen starvation. This highlights both the accuracy of the proposed method and its potential for generating novel hypotheses and testable predictions.
Link to publication [3] Link to original publication [4] Download Bibtex entry [5]

Postal Address

TU Berlin
Fakultät IV
Elektrotechnik und Informatik
sec. MAR 4-2
Marchstrasse 23
D-10587 Berlin
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