Hochberger, C. | bioRxiv Science

bioRxiv2022

Context-Aware Technology Mapping in Genetic Design Automation

Genetic design automation (GDA) tools hold promise to speed-up circuit design in synthetic biology. Their wide-spread adoption is hampered by their limited predictive power, resulting in frequent deviations between in-silico and in-vivo performance of a genetic circuit. Context-effects, i.e., the change in overall circuit functioning, due to the intracellular environment of the host and due to cross-talk among circuits components are believed to be a major source for the aforementioned deviations. Incorporating these effects in computational models of GDA tools is challenging but is expected to boost their predictive power, and hence, their deployment. Using fine-grained thermodynamic models of promoter activity we show in this work, how to account for two major components of cellular context effects: (i) Crosstalk due to limited specificity of used regulators and (ii) titration of circuit regulators to off-target binding sites on the host genome. We show, how we can compensate the incurred increase in computational complexity through dedicated branch-and-bound techniques during the technology mapping process. Using the synthesis of several combinational logic circuits based on Cellos device library as a case study, we analyze the effect of different intensities and distributions of crosstalk on circuit performance and on the usability of a given device library.

synthetic biology↗

bioRxiv2021

Automated Design of Robust Genetic Circuits: Structural Variants and Parameter Uncertainty

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. Graphical TOC Entry O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=82 SRC="FIGDIR/small/456094v1_ufig1.gif" ALT="Figure 2"> View larger version (20K): org.highwire.dtl.DTLVardef@3ef8aorg.highwire.dtl.DTLVardef@635957org.highwire.dtl.DTLVardef@7e747eorg.highwire.dtl.DTLVardef@122db0_HPS_FORMAT_FIGEXP M_FIG C_FIG

synthetic biology↗