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A Systems Theoretic Approach to Systems and Synthetic Biology II: Analysis and Design of Cellular SystemsFrom Specification to Parameters: A Linearization Approach

A Systems Theoretic Approach to Systems and Synthetic Biology II: Analysis and Design of Cellular... [With the improvement of protocols for the assembly of transcriptional parts, synthetic biological devices can now be reliably assembled based on a design. The standardization of the parts open up the way for in silico design tools that improve the construct and optimize devices with respect to given formal specifications. The simplest such optimization is the selection of kinetic parameters and protein abundances such that the specified constraints are robustly satisfied. In this chapter we address the problem of determining parameter values that fulfill specifications expressed in terms of a functional on the trajectories of a dynamical model. We solve this inverse problem by linearizing the forward operator that maps parameter sets to specifications, and then inverting it locally. This approach has two advantages over brute-force random sampling. First, the linearization approach allows us to map back intervals instead of points and second, every obtained value in the parameter region is satisfying the specifications by construction.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

A Systems Theoretic Approach to Systems and Synthetic Biology II: Analysis and Design of Cellular SystemsFrom Specification to Parameters: A Linearization Approach

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References (21)

Publisher
Springer Netherlands
Copyright
© Springer Science+Business Media Dordrecht 2014
ISBN
978-94-017-9046-8
Pages
245 –256
DOI
10.1007/978-94-017-9047-5_10
Publisher site
See Chapter on Publisher Site

Abstract

[With the improvement of protocols for the assembly of transcriptional parts, synthetic biological devices can now be reliably assembled based on a design. The standardization of the parts open up the way for in silico design tools that improve the construct and optimize devices with respect to given formal specifications. The simplest such optimization is the selection of kinetic parameters and protein abundances such that the specified constraints are robustly satisfied. In this chapter we address the problem of determining parameter values that fulfill specifications expressed in terms of a functional on the trajectories of a dynamical model. We solve this inverse problem by linearizing the forward operator that maps parameter sets to specifications, and then inverting it locally. This approach has two advantages over brute-force random sampling. First, the linearization approach allows us to map back intervals instead of points and second, every obtained value in the parameter region is satisfying the specifications by construction.]

Published: Jul 3, 2014

Keywords: Linearization; Parameters; Temporal logic; Linear temporal logic (LTL); Specification functionals; Stoichiometric matrix; State space; Equilibrium point; Bifurcation point; Inverse bifurcation analysis; Inverse bifurcation synthesis

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