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ISCS 2013: Interdisciplinary Symposium on Complex Systems pp 265–270Cite as

Inference of Optimized Control Strategies for Genetic Networks

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Part of the book series: Emergence, Complexity and Computation ((ECC,volume 8))

Abstract

In this chapter we present the application of control theoretical concepts to stochastic dynamical systems which are based on the current knowledge of genetic networks. We showcase the application of reinforcement learning algorithm inferring an optimized control strategy for a genetic switch reversal. The approach does not require precise knowledge of gene network mathematical equations and is therefore also applicable to experimentally obtained time traces.

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Author information

Authors and Affiliations

  1. Boehringer Ingelheim Pharma GmbH and Co. KG, Rhineland-Palatinate, Germany

    Natalja Strelkowa

Authors
  1. Natalja Strelkowa
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Correspondence to Natalja Strelkowa .

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Editors and Affiliations

  1. University of Tübingen, Tübingen, Germany

    Ali Sanayei

  2. Department of Computer Science, VŠB-Technical University of Ostrava, Ostrava, Czech Republic

    Ivan Zelinka

  3. Institute of Physical and Theoretical Chemistry, University of Tübingen, Tübingen, Germany

    Otto E. Rössler

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Strelkowa, N. (2014). Inference of Optimized Control Strategies for Genetic Networks. In: Sanayei, A., Zelinka, I., Rössler, O. (eds) ISCS 2013: Interdisciplinary Symposium on Complex Systems. Emergence, Complexity and Computation, vol 8. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45438-7_26

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  • DOI: https://doi.org/10.1007/978-3-642-45438-7_26

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-45437-0

  • Online ISBN: 978-3-642-45438-7

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