Schladt, T. ; Engelmann, N. ; Kubaczka, E. ; Hochberger, C. ; Koeppl, H. (2021)
Automated Design of Robust Genetic Circuits: Structural Variants and Parameter Uncertainty.
In: biorxiv, 2021 (Preprint)
doi: 10.1101/2021.08.13.456094
Artikel, Bibliographie

Kurzbeschreibung (Abstract)

Genetic design automation methods for combinational circuits often rely on standard algorithms from electronic design automation in their circuit synthesis and technology mapping. However, those algorithms are domain-specific and are hence often not directly suitable for the biological context. In this work we identify aspects of those algorithms that require domain-adaptation. We first demonstrate that enumerating structural variants for a given Boolean specification allows us to find better performing circuits and that stochastic gate assignment methods need to be properly adjusted in order to find the best assignment. Second, we present a general circuit scoring scheme that accounts for the limited accuracy of biological device models including the variability across cells and show that circuits selected according to this score exhibit higher robustness with respect to parametric variations. If gate characteristics in a library are just given in terms of intervals, we provide means to efficiently propagate signals through such a circuit and compute corresponding scores. We demonstrate the novel design approach using the Cello gate library and 33 logic functions that were synthesized and implemented in vivo recently (1). We show that an average 1.3-fold and a peak 6.5-fold performance increase can be achieved by simply considering structural variants and that an average 1.8-fold and a peak 30-fold gain in the novel robustness score can be obtained when selecting circuits according to it.

Frage zum Eintrag

Redaktionelle Details anzeigen