Publication (download)
DNA as Features: Organic Software Product Lines
Mikaela Cashman, Justin Firestone, Myra B. Cohen, Thammasak Thianniwet, and Wei Niu. 2019. DNA as features: organic software product lines. In Proceedings of the 23rd International Systems and Software Product Line Conference (SPLC '19), ACM, New York, NY, USA, Article 20, 11 pages. DOI: https://doi.org/10.1145/3336294.3336298
Best Student Paper Award.
Software product line engineering is a best practice for managing reuse in families of software systems. In this work, we explore the use of product line engineering in the emerging programming domain of synthetic biology. In synthetic biology, living organisms are programmed to perform new functions or improve existing functions. These programs are designed and constructed using small building blocks made out of DNA. We conjecture that there are families of products that consist of common and variable DNA parts, and we can leverage product line engineering to help synthetic biologists build, evolve, and reuse these programs. As a first step towards this goal, we perform a domain engineering case study that leverages an open-source repository of more than 45,000 reusable DNA parts. We are able to identify features and their related artifacts, all of which can be composed to make different programs. We demonstrate that we can successfully build feature models representing families for two commonly engineered functions. We then analyze an existing synthetic biology case study and demonstrate how product line engineering can be beneficial in this domain.
This site includes all artifacts for our SPLC19 publication titled DNA as Features: Organic Software Product Lines. A zip file of all artifacts can be obtained here, each directory contains a readme explaining how to use each artifact. The artifacts in all consist of:
Feature Models
Our FeatureIDE Eclipse workspace containing all feature models
Image files of all feature models
The BioBrick SQL dump of all BioBrick parts
All SQL queries
List of 100 random composite parts and the results of manual evaluation of assets
All feature models can be found in the first artifact
FAMA input files to calculate the number of products in our largest feature model
List of all 2017 Gold Metals teams for building the Kill Switch
Input and output files for the covering arrays
Spreadsheet of the parts used by the 2017 ASU iGEM team
The reverse engineering tool SPLRevO and its input and output files
To look at a specific artifact please visit the corresponding page on this website.
Please send any correspondence to: mcashman.isu@gmail.com; mcohen@iastate.edu; jfiresto@cse.unl.edu
Acknowledgments
We would like to thank the Haynes Lab at Emory University (formally Arizona State University) for sharing additional artifacts with us. This work is supported in part by NSF Grant CCF-1901543, National Institute of Justice Grant 2016-R2-CX-0023, and by NSF CBET 1805528.