DNA as Features

Abstract

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. CCS CONCEPTS • Software and its engineering → Software product lines; • Applied computing → Systems biology.

Automatically Navigating Protein Interaction Networks with a Software Product Line Approach

“…While not tested, this approach has been suggested in [8], where mathematical models of molecular and gene networks are presented as a potential approach to uncover new synthetic entities. Last, the notions of organic software product lines are presented in [9], where a product line approach is applied to synthetic biology to uncover every possible DNA interaction.…”

Learning feature interactions with and without specifications

“…Other approaches require the creation of formal models or other manually developed artifacts that most projects do not have . Such problems are compounded in large configurable software systems where it is more likely that each developer is familiar with only part of the system, and that documentation and configuration guidance are out of date ; Cashman et al (2019). The limited adoption in practice of existing approaches motivates us to pursue automated learning of unwanted feature interactions from models extracted from source code.…”

“…We envision that the most beneficial usage of our method will be to pinpoint for developers, prior to testing, feature interactions of concern for investigation and joint testing. Such problems are compounded in large configurable software systems where it is even more likely that products are siloed, that each developer is familiar with only part of the system, and that documentation and configuration guidance are out of date ; Cashman et al (2017Cashman et al ( , 2019.…”