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  Designing Distributed Cell Classifier Circuits Using a Genetic Algorithm

Nowicka, M., & Siebert, H. (2019). Designing Distributed Cell Classifier Circuits Using a Genetic Algorithm. In L. Bortolussi, & G. Sanguinetti (Eds.), CMSB 2019, part of Lecture Notes in Computer Science 11773 (pp. 96-119). Cham, Switzerland: Springer Nature Switzerland AG. doi:10.1007/978-3-030-31304-3_6.

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Datensatz-Permalink: https://hdl.handle.net/21.11116/0000-000E-5A9B-F Versions-Permalink: https://hdl.handle.net/21.11116/0000-000E-5B35-1
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CMSB 2019_Nowicka_2019.pdf (Verlagsversion), 804KB
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 Urheber:
Nowicka, Melania1, Autor                 
Siebert, Heike, Autor
Affiliations:
1IMPRS for Biology and Computation (Anne-Dominique Gindrat), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1479666              

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Schlagwörter: Synthetic biology · Boolean modeling · Genetic algorithms · miRNA profiling · Cell classifiers · Cancer
 Zusammenfassung: Cell classifiers are decision-making synthetic circuits that allow in vivo cell-type classification. Their design is based on finding a relationship between differential expression of miRNAs and the cell condition. Such biological devices have shown potential to become a valuable tool in cancer treatment as a new type-specific cell targeting approach. So far, only single-circuit classifiers were designed in this context. However, reliable designs come with high complexity, making them difficult to assemble in the lab. Here, we apply so-called Distributed Classifiers (DC) consisting of simple single circuits, that decide collectively according to a threshold function. Such architecture potentially simplifies the assembly process and provides design flexibility. We present a genetic algorithm that allows the design and optimization of DCs. Breast cancer case studies show that DCs perform with high accuracy on real-world data. Optimized classifiers capture biologically relevant miRNAs that are cancer-type specific. The comparison to a single-circuit classifier design approach shows that DCs perform with significantly higher accuracy than individual circuits. The algorithm is implemented as an open source tool.

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Sprache(n): eng - English
 Datum: 2019-09-17
 Publikationsstatus: Online veröffentlicht
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 Identifikatoren: DOI: 10.1007/978-3-030-31304-3_6
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Veranstaltung

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Titel: 17th International Conference, CMSB 2019: Computational Methods in Systems Biology
Veranstaltungsort: Trieste, Italy
Start-/Enddatum: 2019-09-18 - 2019-09-20

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Titel: CMSB 2019, part of Lecture Notes in Computer Science 11773
Genre der Quelle: Konferenzband
 Urheber:
Bortolussi, Luca, Herausgeber
Sanguinetti, Guido, Herausgeber
Affiliations:
-
Ort, Verlag, Ausgabe: Cham, Switzerland : Springer Nature Switzerland AG
Seiten: - Band / Heft: LNBI 11773 Artikelnummer: - Start- / Endseite: 96 - 119 Identifikator: ISBN: 978-3-030-31303-6 (print) 978-3-030-31304-3 (online)