Literature DB >> 32771905

Sequencing enabling design and learning in synthetic biology.

Pierre-Aurélien Gilliot1, Thomas E Gorochowski2.   

Abstract

The ability to read and quantify nucleic acids such as DNA and RNA using sequencing technologies has revolutionized our understanding of life. With the emergence of synthetic biology, these tools are now being put to work in new ways - enabling de novo biological design. Here, we show how sequencing is supporting the creation of a new wave of biological parts and systems, as well as providing the vast data sets needed for the machine learning of design rules for predictive bioengineering. However, we believe this is only the tip of the iceberg and end by providing an outlook on recent advances that will likely broaden the role of sequencing in synthetic biology and its deployment in real-world environments.
Copyright © 2020 Elsevier Ltd. All rights reserved.

Keywords:  Biological design; Machine learning; Omics; Sequencing; Synthetic biology; Systems biology

Year:  2020        PMID: 32771905     DOI: 10.1016/j.cbpa.2020.06.002

Source DB:  PubMed          Journal:  Curr Opin Chem Biol        ISSN: 1367-5931            Impact factor:   8.822


  5 in total

1.  Design and Analysis of Massively Parallel Reporter Assays Using FORECAST.

Authors:  Pierre-Aurelien Gilliot; Thomas E Gorochowski
Journal:  Methods Mol Biol       Date:  2023

Review 2.  Deep Learning Concepts and Applications for Synthetic Biology.

Authors:  William A V Beardall; Guy-Bart Stan; Mary J Dunlop
Journal:  GEN Biotechnol       Date:  2022-08-18

3.  A versatile active learning workflow for optimization of genetic and metabolic networks.

Authors:  Amir Pandi; Christoph Diehl; Ali Yazdizadeh Kharrazi; Scott A Scholz; Elizaveta Bobkova; Léon Faure; Maren Nattermann; David Adam; Nils Chapin; Yeganeh Foroughijabbari; Charles Moritz; Nicole Paczia; Niña Socorro Cortina; Jean-Loup Faulon; Tobias J Erb
Journal:  Nat Commun       Date:  2022-07-05       Impact factor: 17.694

Review 4.  Towards an engineering theory of evolution.

Authors:  Simeon D Castle; Claire S Grierson; Thomas E Gorochowski
Journal:  Nat Commun       Date:  2021-06-07       Impact factor: 14.919

5.  Massively parallel characterization of engineered transcript isoforms using direct RNA sequencing.

Authors:  Matthew J Tarnowski; Thomas E Gorochowski
Journal:  Nat Commun       Date:  2022-01-21       Impact factor: 14.919

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.