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Moment-based methods for parameter inference and experiment design for biochemical reaction networks

Author(s):

J. Ruess, J. Lygeros
Conference/Journal:

ACM Transactions on Modeling and Computer Simulation
Abstract:

Continuous-time Markov chains are commonly used in practice for modeling biochemical reaction networks in which the inherent randomness of the molecular interactions cannot be ignored. This has motivated recent research effort into methods for parameter inference and experiment design for such models. The major difficulty is that such methods usually require one to iteratively solve the chemical master equation which governs the time evolution of the probability distribution of the system. This, however, is rarely possible and even approximation techniques remain limited to relatively small and simple systems. An alternative explored in this paper is to base methods on only some low-order moments instead of the entire probability distribution. We summarize the theory behind such moment-based methods for parameter inference and experiment design and provide new case studies where we investigate their performance.

Year:

2014
Type of Publication:

(01)Article
Supervisor:

J. Lygeros

No Files for download available.
% Autogenerated BibTeX entry
@Article { RueLyg:2014:IFA_4818,
    author={J. Ruess and J. Lygeros},
    title={{Moment-based methods for parameter inference and experiment
	  design for biochemical reaction networks}},
    journal={ACM Transactions on Modeling and Computer Simulation},
    year={2014},
    volume={},
    number={},
    pages={},
    month=dec,
    url={http://control.ee.ethz.ch/index.cgi?page=publications;action=details;id=4818}
}
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