dc.contributor.advisor | Sanguinetti, Guido | en |
dc.contributor.advisor | Cohen, Shay | en |
dc.contributor.author | Scher, Emily Alice | en |
dc.date.accessioned | 2021-03-15T15:51:17Z | |
dc.date.available | 2021-03-15T15:51:17Z | |
dc.date.issued | 2020-11-30 | |
dc.identifier.uri | https://hdl.handle.net/1842/37528 | |
dc.identifier.uri | http://dx.doi.org/10.7488/era/812 | |
dc.description.abstract | Since the turn of the century, the scope and scale of Synthetic Biology projects have grown
dramatically. Instead of limiting themselves to simple genetic circuits, researchers aim
for genome-scale organism redesigns, revolutionary gene therapies, and high throughput,
industrial scale natural product syntheses. However, the engineering principles adopted
by the founders of the field have been applied to Biology in a way that does not fit
many modern experiments. This has limited the usefulness of common sequence design
paradigms. As experiments have become more complex, the sequence design process
has taken up more and more intellectual bandwidth, partially because software tools for
DNA design have remained largely unchanged.
This thesis will explore software engineering, social science, and machine learning
projects aiming to improve the ways in which researchers design novel DNA sequences
for Synthetic Biology experiments.
Popular DNA design tools will be reviewed, alongside an analysis of the key conceptual
metaphors that underlie their workflows. Flaws in the ubiquitous parts-based design
model will be demonstrated, and several alternatives will be explored.
A tool called Part Crafter (partcrafter.com) will be presented, which aggregates
sequence and annotation data from a variety of data sources to allow for rational search
over genomic features, as well as the automated production of biological parts for Synthetic
Biology experiments. However, Part Crafter’s mode of part creation is more flexible than
traditional implementations of parts-based design in the field. Parts are abstracted away
from specific manufacturing standards, and as much contextual information as possible
is presented alongside parts of interest.
Additionally, various types of machine learning models will be presented which predict
histone modification occupancy in novel sequences. Current Synthetic Biology design
paradigms largely ignore the epigenetic context of designed sequences. A gradient of
increasingly complex models will be analysed in order to characterise the complexity
of the combinatorial patterns of sequences of these epigenetic proteins. This work was
exploratory, serving as a proof of concept for using a variety of increasingly complex models to represent genomic elements, and demonstrating that the parts-based design
model is not the only option available to us.
The aims of the field of Synthetic Biology become more ambitious every year. In
order for the goals of the field to be accomplished, we must be able to better understand
the sequences we are designing. The projects presented in this thesis were all completed
with the aim of assisting Synthetic Biologists in designing sequences deliberately. By
taking into account as much contextual information as possible, including epigenetic
factors, researchers will be able to design sequences more quickly and reliably, increasing
their chances of achieving the moon shot goals of the field. | en |
dc.language.iso | en | |
dc.publisher | The University of Edinburgh | en |
dc.relation.hasversion | Emily Scher, Yisha Luo, Aaron Berliner, Jacqueline Quinn, Carlos Olguin, and Yizhi Cai. GenomeCarver: harvesting genetic parts from genomes to support biological design automation. In 6th International Workshop on Bio-Design Automation, Seattle, WA, 2014. | en |
dc.relation.hasversion | Emily Scher, Shay B Cohen, and Guido Sanguinetti. PartCrafter: find, generate and analyze BioParts. Synthetic Biology, 4(1):ysz014, 2019. | en |
dc.relation.hasversion | Erika Szymanski and Emily Scher. Models for DNA Design Tools: The Trouble with Metaphors Is That They Don’t Go Away. ACS Synthetic Biology, 8(12):2635–2641, 2019. | en |
dc.subject | Synthetic Biology | en |
dc.subject | machine learning | en |
dc.subject | novel DNA sequence design | en |
dc.subject | DNA design software review | en |
dc.subject | Part Crafter | en |
dc.subject | epigenetic protein prediction | en |
dc.title | Human genome interaction: models for designing DNA sequences | en |
dc.type | Thesis or Dissertation | en |
dc.type.qualificationlevel | Doctoral | en |
dc.type.qualificationname | PhD Doctor of Philosophy | en |