TY - GEN
T1 - BioSIMP
T2 - 12th IEEE/ACM International Workshop on Software Engineering for Science, SE4Science 2017
AU - Cashman, Mikaela
AU - Catlett, Jennie L.
AU - Cohen, Myra B.
AU - Buan, Nicole R.
AU - Sakkaff, Zahmeeth
AU - Pierobon, Massimiliano
AU - Kelley, Christine A.
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/6/29
Y1 - 2017/6/29
N2 - 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.
AB - 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.
KW - Highly-Configurable Software
KW - Systems Biology
UR - http://www.scopus.com/inward/record.url?scp=85026728125&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85026728125&partnerID=8YFLogxK
U2 - 10.1109/SE4Science.2017.9
DO - 10.1109/SE4Science.2017.9
M3 - Conference contribution
AN - SCOPUS:85026728125
T3 - Proceedings - 2017 IEEE/ACM 12th International Workshop on Software Engineering for Science, SE4Science 2017
SP - 2
EP - 8
BT - Proceedings - 2017 IEEE/ACM 12th International Workshop on Software Engineering for Science, SE4Science 2017
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 22 May 2017
ER -