Sources for "Automated Exploration of DNA-based Structure Self-Assembly Networks"
| Main Authors: | Leo Cazenille, Alexandre Baccouche, Nathanael Aubert-Kato |
|---|---|
| Format: | info software eJournal |
| Terbitan: |
, 2021
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| Subjects: | |
| Online Access: |
https://zenodo.org/record/5461801 |
Daftar Isi:
- This is the source code of the article "Automated Exploration of DNA-based Structure Self-Assembly Networks" published in the Royal Society Open Science Journal. Current link: https://bitbucket.org/leo-cazenille/kakenhievolvedna/src/master/ Abstract of the article: Finding DNA sequences capable of folding into specific nanostructures is a hard problem, as it involves very large search spaces and complex non-linear dynamics. Typical methods to solve it aim to reduce the search space by minimizing unwanted interactions through restrictions on the design (e.g. staples in DNA origami or voxel-based designs in DNA Bricks). Here, we present a novel methodology that aims to reduce this search space by identifying the relevant properties of a given assembly system to the emergence of various families of structures (e.g. simple structures, polymers, branched structures). For a given set of DNA strands, our approach automatically finds Chemical Reaction Networks (\CRNs) that generate sets of structures exhibiting ranges of specific user-specified properties, such as length and type of structures or their frequency of occurrence. For each set, we enumerate the possible DNA structures that can be generated through domain-level interactions, identify the most prevalent structures, find the best-performing sequence sets to the emergence of target structures, and assess \CRNs robustness to the removal of reaction pathways. Our results suggest a connection between the characteristics of DNA strands and the distribution of generated structure families. This work was supported by JSPS KAKENHI Grant Number JP17K00399 and by Grant-in-Aid for JSPS Fellows JP19F19722.