Abstract
The advent of biological data of increasingly higher resolution in space and time has triggered the use of dynamic models to explain and predict the evolution of biological systems over space and time. Computer-aided system modeling and analysis in biology has led to many new discoveries and explanations that would otherwise be intractable to articulate without the available data and computing power. Nevertheless, the complexity in biology still challenges many labs in capturing studied phenomena in models that are tractable and simple enough to analyze. Moreover, the popular use of ordinary differential equation models have their limitations in that they solely capture continuous dynamics, while we observe many discrete dynamic phenomena in biology such as gene switching or mutations. Hybrid systems modeling provides a framework in which both continuous and discrete dynamics can be simulated and analyzed. Moreover, it provides techniques to develop approximations and abstractions of complex dynamics that are tractable to analyze.
Footnotes
This research was supported by the NIH NCI under the ICBP and PS-OC programs (5U54CA112970-08). R. Dobbe and C. J. Tomlin are with Department of Electrical Engineering and Computer Sciences, University of California, Berkeley 94720, CA, USA (e-mail: dobbe{at}berkeley.edu)
