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From sequence to function and back – High-throughput sequence-function mapping in synthetic biology
Höllerer, Simon, Desczyk, Charlotte, Muro, Ricardo Farrera und Jeschek, Markus
(2023)
From sequence to function and back – High-throughput sequence-function mapping in synthetic biology.
Current Opinion in Systems Biology 37, S. 100499.
Veröffentlichungsdatum dieses Volltextes: 02 Feb 2024 13:50
Artikel
DOI zum Zitieren dieses Dokuments: 10.5283/epub.55514
Zusammenfassung
How does genetic sequence give rise to biological function? Answering this question is key to our understanding of life and the construction of synthetic biosystems that fight disease, resource scarcity and climate change. Unfortunately, the virtually infinite number of possible sequences and limitations in their functional characterization limit our current understanding of sequence-function ...
How does genetic sequence give rise to biological function? Answering this question is key to our understanding of life and the construction of synthetic biosystems that fight disease, resource scarcity and climate change. Unfortunately, the virtually infinite number of possible sequences and limitations in their functional characterization limit our current understanding of sequence-function relationships. To overcome this dilemma, several high-throughput methods to experimentally link sequences to corresponding functional properties have been developed recently. While all of these share the goal to collect sequence-function data at large scale, they differ significantly in their technical approach, functional readout and application scope. Herein, we highlight recent developments in the aspiring field of high-throughput sequence-function mapping providing a critical assessment of their potential in synthetic biology.
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| Dokumentenart | Artikel | ||||
| Titel eines Journals oder einer Zeitschrift | Current Opinion in Systems Biology | ||||
| Verlag: | Elsevier | ||||
|---|---|---|---|---|---|
| Band: | 37 | ||||
| Seitenbereich: | S. 100499 | ||||
| Datum | 13 Dezember 2023 | ||||
| Institutionen | Biologie und Vorklinische Medizin > Institut für Biochemie, Genetik und Mikrobiologie > Lehrstuhl für Mikrobiologie (Archaeenzentrum) | ||||
| Identifikationsnummer |
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| Stichwörter / Keywords | Sequence-function mapping, Sequence-activity landscapes, Nextgeneration sequencing, DNA recording, DNA recorder, RNA-Seq, Flow- Seq, Sort-Seq, Recombinases, Integrases, Machine learning in biology, Data-driven modelling, Synthetic biology.. | ||||
| Dewey-Dezimal-Klassifikation | 500 Naturwissenschaften und Mathematik > 570 Biowissenschaften, Biologie | ||||
| Status | Veröffentlicht | ||||
| Begutachtet | Ja, diese Version wurde begutachtet | ||||
| An der Universität Regensburg entstanden | Ja | ||||
| URN der UB Regensburg | urn:nbn:de:bvb:355-epub-555143 | ||||
| Dokumenten-ID | 55514 |
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