Misirli, G, Nguyen, T, McLaughlin, JA, Vaidyanathan, P, Jones, T, Densmore, D, Myers, CJ and Wipat, A (2018) A computational workflow for the automated generation of models of genetic designs. ACS Synthetic Biology, 8 (7). pp. 1548-1559. ISSN 2161-5063

[thumbnail of acs-modelgenerationworkflow.pdf]
acs-modelgenerationworkflow.pdf - Accepted Version

Download (4MB) | Preview


Computational models are essential to engineer predictable biological systems and to scale up this process for complex systems. Computational modelling often requires expert knowledge and data to build models. Clearly, manual creation of models is not scalable for large designs. Despite several automated model construction approaches, computational methodologies to bridge knowledge in design repositories and the process of creating computational models has still not been established. This paper describes a workflow for automatic generation of computational models of genetic circuits from data stored in design repositories using existing standards. This workflow leverages the software tool SBOLDesigner to build structural models that are then enriched by the Virtual Parts Repository API using Systems Biology Open Language (SBOL) data fetched from the SynBioHub design repository. The iBioSim software tool is then utilized to convert this SBOL description into a computational model encoding using the Systems Biology Markup Language (SBML). Finally, this SBML model can be simulated using a variety of methods. This workflow provides synthetic biologists with easy to use tools to create predictable biological systems, hiding away the complexity of building computational models. This approach can further be incorporated into other computational workflows for design automation.

Item Type: Article
Additional Information: This is the accepted author manuscript (AAM). The final published version (version of record) is available online via ACS Publications at http://dx.doi.org/10.1021/acssynbio.7b00459 - please refer to any applicable terms of use of the publisher.
Uncontrolled Keywords: data standards, genetic design automation, modeling, SBML, SBOL, VPR API,
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Divisions: Faculty of Natural Sciences > School of Computing and Mathematics
Related URLs:
Depositing User: Symplectic
Date Deposited: 06 Jun 2018 13:37
Last Modified: 08 Aug 2019 15:26
URI: https://eprints.keele.ac.uk/id/eprint/4990

Actions (login required)

View Item
View Item