BioSIMP: Using Software Testing Techniques for Sampling and Inference in Biological Organisms

Authors

Mikaela Cashman, University of Nebraska-LincolnFollow
Jennie L. Catlett, University of Nebraska-LincolnFollow
Myra B. Cohen, University of Nebraska-LincolnFollow
Nicole R. Buan, University of Nebraska-LincolnFollow
Zahmeeth Sakkaff, University of Nebraska-LincolnFollow
Massimiliano Pierobon, University of Nebraska-LincolnFollow
Christine A. Kelley, University of Nebraska-LincolnFollow

Date of this Version

2017

Citation

Published in 2017 IEEE/ACM 12th International Workshop on Software Engineering for Science (SE4Science)

DOI 10.1109/SE4Science.2017.9

Comments

Copyright © 2017 IEEE. Used by permission.

Abstract

Years of research in software engineering have given us novel ways to reason about, test, and predict the behavior of complex software systems that contain hundreds of thousands of lines of code. Many of these techniques have been inspired by nature such as genetic algorithms, swarm intelligence, and ant colony optimization. In this paper we reverse the direction and present BioSIMP, a process that models and predicts the behavior of biological organisms to aid in the emerging field of systems biology. It utilizes techniques from testing and modeling of highly-configurable software systems. Using both experimental and simulation data we show that BioSIMP can find important environmental factors in two microbial organisms. However, we learn that in order to fully reason about the complexity of biological systems, we will need to extend existing or create new software engineering techniques.