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Optimal Observation Time Points in Stochastic Chemical Kinetics

Part of the Lecture Notes in Computer Science book series (LNBI,volume 7699)

Abstract

Wet-lab experiments, in which the dynamics within living cells are observed, are usually costly and time consuming. This is particularly true if single-cell measurements are obtained using experimental techniques such as flow-cytometry or fluorescence microscopy. It is therefore important to optimize experiments with respect to the information they provide about the system. In this paper we make a priori predictions of the amount of information that can be obtained from measurements. We focus on the case where the measurements are made to estimate parameters of a stochastic model of the underlying biochemical reactions. We propose a numerical scheme to approximate the Fisher information of future experiments at different observation time points and determine optimal observation time points. To illustrate the usefulness of our approach, we apply our method to two interesting case studies.

Keywords

  • Unknown Parameter
  • Fisher Information
  • Fisher Information Matrix
  • Observation Sequence
  • Chemical Master Equation

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Correspondence to Charalampos Kyriakopoulos .

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Kyriakopoulos, C., Wolf, V. (2015). Optimal Observation Time Points in Stochastic Chemical Kinetics. In: Maler, O., Halász, Á., Dang, T., Piazza, C. (eds) Hybrid Systems Biology. HSB 2014. Lecture Notes in Computer Science(), vol 7699. Springer, Cham. https://doi.org/10.1007/978-3-319-27656-4_5

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  • DOI: https://doi.org/10.1007/978-3-319-27656-4_5

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