Quantitative characterization of recombinase-based digitizer circuits enables predictable amplification of biological signals
<jats:title>Abstract</jats:title><jats:p>Many synthetic gene circuits are restricted to single-use applications or require iterative refinement for incorporation into complex systems. One example is the recombinase-based digitizer circuit, which has been used to improve weak or lea...
| Main Authors: | , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer Science and Business Media LLC
2021
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| Online Access: | https://hdl.handle.net/1721.1/133607 |
| _version_ | 1826198109618700288 |
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| author | Kiwimagi, Katherine A Letendre, Justin H Weinberg, Benjamin H Wang, Junmin Chen, Mingzhe Watanabe, Leandro Myers, Chris J Beal, Jacob Wong, Wilson W Weiss, Ron |
| author_facet | Kiwimagi, Katherine A Letendre, Justin H Weinberg, Benjamin H Wang, Junmin Chen, Mingzhe Watanabe, Leandro Myers, Chris J Beal, Jacob Wong, Wilson W Weiss, Ron |
| author_sort | Kiwimagi, Katherine A |
| collection | MIT |
| description | <jats:title>Abstract</jats:title><jats:p>Many synthetic gene circuits are restricted to single-use applications or require iterative refinement for incorporation into complex systems. One example is the recombinase-based digitizer circuit, which has been used to improve weak or leaky biological signals. Here we present a workflow to quantitatively define digitizer performance and predict responses to different input signals. Using a combination of signal-to-noise ratio (SNR), area under a receiver operating characteristic curve (AUC), and fold change (FC), we evaluate three small-molecule inducible digitizer designs demonstrating FC up to 508x and SNR up to 3.77 dB. To study their behavior further and improve modularity, we develop a mixed phenotypic/mechanistic model capable of predicting digitizer configurations that amplify a synNotch cell-to-cell communication signal (Δ SNR up to 2.8 dB). We hope the metrics and modeling approaches here will facilitate incorporation of these digitizers into other systems while providing an improved workflow for gene circuit characterization.</jats:p> |
| first_indexed | 2024-09-23T10:58:59Z |
| format | Article |
| id | mit-1721.1/133607 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T10:58:59Z |
| publishDate | 2021 |
| publisher | Springer Science and Business Media LLC |
| record_format | dspace |
| spelling | mit-1721.1/1336072021-10-28T04:23:34Z Quantitative characterization of recombinase-based digitizer circuits enables predictable amplification of biological signals Kiwimagi, Katherine A Letendre, Justin H Weinberg, Benjamin H Wang, Junmin Chen, Mingzhe Watanabe, Leandro Myers, Chris J Beal, Jacob Wong, Wilson W Weiss, Ron <jats:title>Abstract</jats:title><jats:p>Many synthetic gene circuits are restricted to single-use applications or require iterative refinement for incorporation into complex systems. One example is the recombinase-based digitizer circuit, which has been used to improve weak or leaky biological signals. Here we present a workflow to quantitatively define digitizer performance and predict responses to different input signals. Using a combination of signal-to-noise ratio (SNR), area under a receiver operating characteristic curve (AUC), and fold change (FC), we evaluate three small-molecule inducible digitizer designs demonstrating FC up to 508x and SNR up to 3.77 dB. To study their behavior further and improve modularity, we develop a mixed phenotypic/mechanistic model capable of predicting digitizer configurations that amplify a synNotch cell-to-cell communication signal (Δ SNR up to 2.8 dB). We hope the metrics and modeling approaches here will facilitate incorporation of these digitizers into other systems while providing an improved workflow for gene circuit characterization.</jats:p> 2021-10-27T19:53:47Z 2021-10-27T19:53:47Z 2021-12 2021-09-10T18:27:48Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/133607 en 10.1038/s42003-021-02325-5 Communications Biology Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf Springer Science and Business Media LLC Nature |
| spellingShingle | Kiwimagi, Katherine A Letendre, Justin H Weinberg, Benjamin H Wang, Junmin Chen, Mingzhe Watanabe, Leandro Myers, Chris J Beal, Jacob Wong, Wilson W Weiss, Ron Quantitative characterization of recombinase-based digitizer circuits enables predictable amplification of biological signals |
| title | Quantitative characterization of recombinase-based digitizer circuits enables predictable amplification of biological signals |
| title_full | Quantitative characterization of recombinase-based digitizer circuits enables predictable amplification of biological signals |
| title_fullStr | Quantitative characterization of recombinase-based digitizer circuits enables predictable amplification of biological signals |
| title_full_unstemmed | Quantitative characterization of recombinase-based digitizer circuits enables predictable amplification of biological signals |
| title_short | Quantitative characterization of recombinase-based digitizer circuits enables predictable amplification of biological signals |
| title_sort | quantitative characterization of recombinase based digitizer circuits enables predictable amplification of biological signals |
| url | https://hdl.handle.net/1721.1/133607 |
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