ArticleACS synthetic biology2021
Automated Design of Robust Genetic Circuits: Structural Variants and Parameter Uncertainty.
Article in ACS synthetic biology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- The Coli Toolkit (CTK): An Extension of the Modular Yeast Toolkit for Use inACS synthetic biology · 2026Article
- A tool for modeling gene regulatory networks (GRN_modeler) and its applications to synthetic biology.Molecular systems biology · 2025Article
- Tuning Ultrasensitivity in Genetic Logic Gates Using Antisense RNA Feedback.ACS synthetic biology · 2025Article
- Energy Aware Technology Mapping of Genetic Logic Circuits.ACS synthetic biology · 2024Article
- Developmental assembly of multi-component polymer systems through interconnected synthetic gene networks in vitro.Nature communications · 2024Article
- Energy Aware Technology Mapping of Genetic Logic Circuits.bioRxiv : the preprint server for biology · 2024Article
- Algorithmic Programming of Sequential Logic and Genetic Circuits for Recording Biochemical Concentration in a Probiotic Bacterium.ACS synthetic biology · 2023Article
- Context-Aware Technology Mapping in Genetic Design Automation.ACS synthetic biology · 2023Article
- Robustness and reproducibility of simple and complex synthetic logic circuit designs using a DBTL loop.Synthetic biology (Oxford, England) · 2023Article
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Authors and funding
5 authors.
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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.,
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