Sign in
Synthetic BiologyACS Synthetic Biology
Peer-reviewed journal article

Virtual Parts Repository 2: Model-driven design of genetic regulatory circuits

Göksel MısırlıiD, Bill Huajian Yang, Katherine JamesiD, Anil WipatiD

Abstract

Engineering genetic regulatory circuits is key to the creation of biological applications that are responsive to environmental changes. Computational models can assist in understanding especially large and complex circuits where manual analysis is infeasible, permitting a model-driven design process. However, there are still few tools that offer the ability to simulate the system under design. One of the reasons for this is the lack of accessible model repositories or libraries that cater for the modular composition of models of synthetic systems that do not yet exist in nature. Here, we present the Virtual Parts Repository 2, a resource to facilitate the model-driven design of genetic regulatory circuits, which provides reusable, modular and composable models. The repository is service-oriented and can be utilized by design tools in computational workflows. Designs provided in Synthetic Biology Open Language documents are used to derive system-scale and hierarchical Systems Biology Markup Language models. We also present a rule-based modeling abstraction based on reaction networks to facilitate scalable and modular modeling of complex and large designs. This modeling abstraction incorporates design patterns such as roadblocking, distributed deployment of genetic circuits using plasmids and cellular resource dependency. The computational resources and the modeling abstraction presented in this paper allow computational design tools to take advantage of computational simulations and ultimately help facilitate more predictable applications. Graphical TOC Entry O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=81 SRC="FIGDIR/small/439316v1_ufig1.gif" ALT="Figure 1"> View larger version (23K): org.highwire.dtl.DTLVardef@be045dorg.highwire.dtl.DTLVardef@110856forg.highwire.dtl.DTLVardef@1a861eforg.highwire.dtl.DTLVardef@1b5cf48_HPS_FORMAT_FIGEXP M_FIG C_FIG

PDFDOI

02 /Specification & Classification

Click any tag to browse matching papers
Citations
8
FWCI
0.47
Field percentile
top 44%

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.

Similarity Map
SelectedSame fieldCross-field
THISCrowther '22Riva '22Dreyer '26Poole '22Starkey '22Vidal '22
55% semantic matchSynthetic Biology
Matthew Crowther, Anil Wipat, Ángel Goñi‐Moreno·ACS Synthetic Biology·Aug 31, 2022· 14 citations