Skip to main content

Research Repository

Advanced Search

Virtual Parts Repository 2: Model-Driven Design of Genetic Regulatory Circuits

Yang, B; James, K; Wipat, A; Misirli, G

Virtual Parts Repository 2: Model-Driven Design of Genetic Regulatory Circuits Thumbnail


Authors

B Yang

K James

A Wipat



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 for which 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 to the modular composition of models of synthetic systems. Here, we present the second version of the Virtual Parts Repository, a framework to facilitate the model-driven design of genetic regulatory circuits, which provides reusable, modular, and composable models. The new framework is service-oriented, easier to use in computational workflows, and provides several new features and access methods. New features include supporting hierarchical designs via a graph-based repository or compatible remote repositories, enriching existing designs, and using designs provided in Synthetic Biology Open Language documents to derive system-scale and hierarchical Systems Biology Markup Language models. We also present a reaction-based modeling abstraction inspired by rule-based modeling techniques to facilitate scalable and modular modeling of complex and large designs. This modeling abstraction enhances the modeling capability of the framework, for example, to incorporate design patterns such as roadblocking, distributed deployment of genetic circuits using plasmids, and cellular resource dependency. The framework 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.

Acceptance Date Nov 11, 2021
Publication Date Nov 11, 2021
Publicly Available Date Mar 28, 2024
Journal ACS Synthetic Biology
Publisher American Chemical Society
Pages 3304-3315
DOI https://doi.org/10.1021/acssynbio.1c00157
Keywords genetic circuits; model-driven design; genetic design automation; modular models; computational simulation
Publisher URL https://pubs.acs.org/doi/10.1021/acssynbio.1c00157

Files




You might also like



Downloadable Citations