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	<title>bioRxiv Channel: International Workshop on Bio-Design Automation (IWBDA)</title>
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	This feed contains articles for bioRxiv Channel "International Workshop on Bio-Design Automation (IWBDA)"
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	<item rdf:about="https://biorxiv.org/cgi/content/short/2021.09.14.460206v1?rss=1">
<title>
<![CDATA[
Network visualisation of synthetic biology designs 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.09.14.460206v1?rss=1"
</link>
<description><![CDATA[
As genetic circuits become more sophisticated, the size and complexity of data about their designs increases. This data captured goes beyond monolithic genetic sequences and towards circuit modularity and functional details, which are beneficial for analyzing circuit performance and establishing design automation techniques. However, the accessibility, visualisation and usability of design data (and metadata) have received relatively little attention to date. Here, we present a method to turn circuit designs into networks and showcase its potential to enhance the utility of design data. Since networks are dynamic structures, initial graphs can be interactively shaped into sub-networks of relevant information based on requirements such as abstraction, hierarchy and protein interactions. Additionally, several visual changes can be applied, such as colouring or clustering nodes based on types (e.g., genes or promoters), resulting in easier comprehension from a user perspective. This approach allows circuit designs to be coupled to other networks, such as metabolic pathways or implementation protocols captured in graph-like formats. Therefore, we advocate using networks to structure, access and improve synthetic biology information.
]]></description>
<dc:creator>Crowther, M.</dc:creator>
<dc:creator>Wipat, A.</dc:creator>
<dc:creator>Goni-Moreno, A.</dc:creator>
<dc:date>2021-09-14</dc:date>
<dc:identifier>doi:10.1101/2021.09.14.460206</dc:identifier>
<dc:title><![CDATA[Network visualisation of synthetic biology designs]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-09-14</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.09.17.460644v1?rss=1">
<title>
<![CDATA[
Data Representation in the DARPA SD2 Program 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.09.17.460644v1?rss=1"
</link>
<description><![CDATA[
1Modern scientific enterprises are often highly complex and multidisciplinary, particularly in areas like synthetic biology where the subject at hand is itself inherently complex and multidisciplinary. Collaboration across many organizations is necessary to efficiently tackle such problems [6, 15], but remains difficult. The challenge is further amplified by automation that increases the pace at which new information can be produced, and particularly so for matters of fundamental research, where concepts and definitions are inherently fluid and may rapidly change as an investigation evolves [7].

The DARPA program Synergistic Discovery and Design (SD2) aimed to address these challenges by organizing the development of data-driven methods to accelerate discovery and improve design robustness, with one of the key domains under study being synthetic biology. The program was specifically organized such that teams provided complementary types of expertise and resources, and without any team being in a dominant organizational position, such that subject-matter investigations would necessarily require peer-level collaboration across multiple team boundaries. With more than 100 researchers across more than 20 organizations, several of which ran experimental facilities with high-throughput automation, participants were forced to confront challenges around effective data sharing.

The default architecture for scientific collaboration is essentially one of anarchy, with ad-hoc bilateral relations between pairs of collaborators or experimental phases (Figure 1(a)). This was by necessity the case during early phases of the SD2 program as well, in which incorporating new tools into pipelines was ad-hoc and time-consuming, and data was generally disconnected from genetic designs and experimental plans. The other typical approach for collaboration is one of "command and control", in which a dominant organization determines the data sharing content and format for all participants (Figure 1(b)). This can be efficient, but tends to be limited in flexibility and extensibility, rendering it unsuitable for research collaboration, as indeed was found when we attempted this approach during the first year of the SD2 program. We addressed these problems with the application of distributed standards to create a "flexible rendezvous" model of collaboration (Figure 1(c)), enabling information flow to track evolving collaborative relationships, improving the sharing and utility of information across the community and supporting accelerated rates of experimentation.

O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=71 SRC="FIGDIR/small/460644v1_fig1.gif" ALT="Figure 1">
View larger version (15K):
org.highwire.dtl.DTLVardef@fc7371org.highwire.dtl.DTLVardef@1ff2733org.highwire.dtl.DTLVardef@66af53org.highwire.dtl.DTLVardef@1809640_HPS_FORMAT_FIGEXP  M_FIG O_FLOATNOFigure 1:C_FLOATNO Architectures for data sharing: bilateral relations (a), command and control (b), and flexible rendezvous (c).

C_FIG
]]></description>
<dc:creator>Roehner, N.</dc:creator>
<dc:creator>Beal, J.</dc:creator>
<dc:creator>Bartley, B.</dc:creator>
<dc:creator>Markeloff, R.</dc:creator>
<dc:creator>Mitchell, T.</dc:creator>
<dc:creator>Nguyen, T.</dc:creator>
<dc:creator>Sumorok, D.</dc:creator>
<dc:creator>Walczak, N.</dc:creator>
<dc:creator>Myers, C.</dc:creator>
<dc:creator>Zundel, Z.</dc:creator>
<dc:creator>Scholz, J.</dc:creator>
<dc:creator>Hatch, B.</dc:creator>
<dc:creator>Weston, M.</dc:creator>
<dc:creator>Colonna-Romano, J.</dc:creator>
<dc:date>2021-09-18</dc:date>
<dc:identifier>doi:10.1101/2021.09.17.460644</dc:identifier>
<dc:title><![CDATA[Data Representation in the DARPA SD2 Program]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-09-18</prism:publicationDate>
<prism:section></prism:section>
</item>
<item rdf:about="https://biorxiv.org/cgi/content/short/2021.09.21.460548v1?rss=1">
<title>
<![CDATA[
LOICA: Logical Operators for Integrated Cell Algorithms 
]]>
</title>
<link>
https://biorxiv.org/cgi/content/short/2021.09.21.460548v1?rss=1"
</link>
<description><![CDATA[
1Mathematical and computational modeling is essential to genetic design automation and for the synthetic biology design-build-test-learn cycle. The construction and analysis of models is enabled by abstraction based on a hierarchy of components, devices, and systems that can be used to compose genetic circuits. These abstract elements must be parameterized from data derived from relevant experiments, and these experiments related to the part composition of the abstract components of the circuits measured. Here we present LOICA (Logical Operators for Integrated Cell Algorithms), a Python package for modeling and characterizing genetic circuits based on a simple object-oriented design abstraction. LOICA uses classes to represent different biological and experimental components, which generate models through their interactions. High-level designs are linked to their part composition via SynBioHub. Furthermore, LOICA communicates with Flapjack, a data management and analysis tool, to link to experimental data, enabling abstracted elements to characterize themselves.
]]></description>
<dc:creator>Vidal, G.</dc:creator>
<dc:creator>Vidal-Cespedes, C.</dc:creator>
<dc:creator>Rudge, T. J.</dc:creator>
<dc:date>2021-09-23</dc:date>
<dc:identifier>doi:10.1101/2021.09.21.460548</dc:identifier>
<dc:title><![CDATA[LOICA: Logical Operators for Integrated Cell Algorithms]]></dc:title>
<dc:publisher>Cold Spring Harbor Laboratory Press</dc:publisher>
<prism:publicationDate>2021-09-23</prism:publicationDate>
<prism:section></prism:section>
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