Schladt T, Engelmann N, Kubaczka E, Hochberger C, Koeppl H. Automated Design of Robust Genetic Circuits: Structural Variants and Parameter Uncertainty.
ACS Synth Biol 2021;
10:3316-3329. [PMID:
34807573 PMCID:
PMC8689692 DOI:
10.1021/acssynbio.1c00193]
[Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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 (Nielsen, A., et al., Science, 2016, 352 (6281), DOI: 10.1126/science.aac7341). Across this set of functions, 32 of them can be improved by simply
considering structural variants yielding performance gains of up to
7.9-fold, whereas 22 of them can be improved with gains up to 26-fold
when selecting circuits according to the novel robustness score. We
furthermore report on the synergistic combination of the two proposed
improvements.
Collapse