Configurability in the Biosciences
Abstract: End users of bioinformatics software tools range from bench scientists with little computational experience, to sophisticated developers. This diverse set of end-users utilize applications for automating tasks to make decisions and draw scientific conclusions based on their discoveries. As the number and types of tools are increasing, they are also increasing in flexibility. The customization of these tools indicates bioinformatics is entering a mature phase of software development — that of highly-configurable software — where the end-user is provided with many customization options. At the same time, biologists and chemists are engineering living organisms in a process that mimics software development. As they share their designs and promote re-use and plug-and-play, their programs are also emerging as highly-configurable.
In my research I address three areas of configurability in the biosciences: (1) configurability of bioinformatics software, (2) configurability of synthetic biology constructs, and (3) configurability in biological models. We highlight the challenges of configurability in these areas and provide approaches to help end users navigate the configuration spaces. Wedemonstrate there is variability in both the functional and performance outcomes of highly-configurable bioinformatic tools, discuss the implications of this variability, and provide suggestions for developers. We then define a mapping of software product line engineering to the domain of synthetic biology resulting in organic software product lines. In a case study we demonstrate the potential reuse and existence of both commonality and variability in an open source synthetic biology repository. Finally we view living organisms as highly configurable software systems and demonstrate the use of sampling, inference, modeling, and prediction to understanding these complex systems.
Mikaela Cashman is a PhD Candidate in Computer Science at Iowa State University who obtained her Master’s in Computer Science in 2016 at the University of Nebraska - Lincoln with her thesis on “Using Software Testing Techniques to Infer Biological Models”. She has published in top computer science conferences and journals; her papers have won awards such as the Best Student Paper Award (2019), ACM SIGSOFT Distinguished Paper Award (2018), and Best Paper Award (2017). Her research centers around view biological systems as software systems and applying methods from software engineering to better understand, manipulate, and predict them. For further information and contact information please see her website: https://sites.google.com/view/mikaelacashman.