2020
DOI: 10.1016/j.cbpa.2020.06.002
|View full text |Cite
|
Sign up to set email alerts
|

Sequencing enabling design and learning in synthetic biology

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 waysenabling 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… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
13
0

Year Published

2021
2021
2023
2023

Publication Types

Select...
6
1
1

Relationship

2
6

Authors

Journals

citations
Cited by 19 publications
(13 citation statements)
references
References 62 publications
0
13
0
Order By: Relevance
“…By developing a new nanopore-based dRNA-seq characterization approach (Figure 1), we were able to simultaneously measure the termination efficiency of an entire mixed pool of 1183 unique transcriptional valves as well as provide nucleotide resolution insight into precisely where termination occurred for each (Figure 3). Such detail is lost with more typical fluorescence-based assays 6,11 , but is essential for developing the low-level biophysical or machine learning based models of genetic parts that are essential for predictive biodesign workflows [44][45][46][47] . While rich, highcontent characterization data can normally only be produced for a small set of samples 8,13,36,48 , the approach presented here removes this limitation, allowing us to more systematically explore the genetic design space of a large pooled library and glean several design principles.…”
Section: Discussionmentioning
confidence: 99%
“…By developing a new nanopore-based dRNA-seq characterization approach (Figure 1), we were able to simultaneously measure the termination efficiency of an entire mixed pool of 1183 unique transcriptional valves as well as provide nucleotide resolution insight into precisely where termination occurred for each (Figure 3). Such detail is lost with more typical fluorescence-based assays 6,11 , but is essential for developing the low-level biophysical or machine learning based models of genetic parts that are essential for predictive biodesign workflows [44][45][46][47] . While rich, highcontent characterization data can normally only be produced for a small set of samples 8,13,36,48 , the approach presented here removes this limitation, allowing us to more systematically explore the genetic design space of a large pooled library and glean several design principles.…”
Section: Discussionmentioning
confidence: 99%
“…However, to be practical, supporting tools must exist that can provide key information regarding the genetic variation, genotype-function map and selective pressures within a biosystem. Advances in sequencing offer a means to quantitatively measure millions of genotypes in parallel 83 and when combined with high-throughput techniques, such as fluorescence-activated cell sorting, make it possible to infer simplified genotype-function maps 84,85 . The local function landscapes of the green fluorescent protein 86 and transcription factor-binding sites 48 have already been characterised experimentally with such methods.…”
Section: Toward Evotype Engineeringmentioning
confidence: 99%
“…Even so, the vastness of evotype landscapes and the need for functions calculated from many outputs of a system mean that new methods with greater throughputs are also necessary 85,90 . There is a particular need for methods able to measure many characteristics of each cell simultaneously (e.g., via automated high-content microscopy 91 or high-throughput Raman spectroscopy 92 ).…”
Section: Toward Evotype Engineeringmentioning
confidence: 99%
“…Without the rapid sharing of pathogen genetic resources and digital sequence information (DSI) (called genetic sequence data (GSD)) by the World Health Organization (WHO), it would not have been possible to create effective vaccines within a truly short timeframe of under a year. While rapid progress by the scientific community relies on openness and public availability of genetic sequences, fears have been expressed regarding the increasing ease with which genetic material can be transformed into digital information, transmitted, reproduced, and manipulated through advances in sequencing technologies, genome editing and synthetic biology [170,171]. Progress in synthetic biology might soon enable de novo biological design [170], thus, confirming fears of the dematerialization of genetic resources, i.e., making physical access superfluous and in that way threatening the principles of ABS as established under the International Treaty and the Nagoya Protocol of the CBD [171].…”
Section: The Political Dimension Of Digital Sequence Information (Dsi) Sharingmentioning
confidence: 99%