Schladt, T. ; Engelmann, N. ; Kubaczka, E. ; Hochberger, C. ; Koeppl, H. (2021)
Automated Design of Robust Genetic Circuits: Structural Variants and Parameter Uncertainty.
In: ACS Synthetic Biology, 2021, 10
doi: 10.1021/acssynbio.1c00193
Article, Bibliographie
This is the latest version of this item.
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.
| Item Type: | Article |
|---|---|
| Erschienen: | 2021 |
| Creators: | Schladt, T. ; Engelmann, N. ; Kubaczka, E. ; Hochberger, C. ; Koeppl, H. |
| Type of entry: | Bibliographie |
| Title: | Automated Design of Robust Genetic Circuits: Structural Variants and Parameter Uncertainty |
| Language: | English |
| Date: | 22 November 2021 |
| Year of primary publication: | 2021 |
| Place of primary publication: | Washington, DC |
| Publisher: | ACS Publications |
| Journal or Publication Title: | ACS Synthetic Biology |
| Volume of the journal: | 10 |
| DOI: | 10.1021/acssynbio.1c00193 |
| URL / URN: | https://pubs.acs.org/doi/abs/10.1021/acssynbio.1c00193 |
| Corresponding Links: | |
| 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. |
| Uncontrolled Keywords: | genetic design automation, synthetic biology, circuit synthesis, structural variants, cell-to-cell variability, robust genetic circuit |
| Divisions: | 18 Department of Electrical Engineering and Information Technology 18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications > Bioinspired Communication Systems 18 Department of Electrical Engineering and Information Technology > Institute of Computer Engineering 18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications 18 Department of Electrical Engineering and Information Technology > Institute of Computer Engineering > Computer Systems Group |
| Date Deposited: | 29 Nov 2021 09:53 |
| Last Modified: | 17 May 2024 10:19 |
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| Suche nach Titel in: | TUfind oder in Google |
Available Versions of this Item
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Automated Design of Robust Genetic Circuits: Structural Variants and Parameter Uncertainty. (deposited 13 May 2024 09:45)
- Automated Design of Robust Genetic Circuits: Structural Variants and Parameter Uncertainty. (deposited 29 Nov 2021 09:53) [Currently Displayed]
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