This course introduces how to perform abstraction of genetic circuit models. The first module teaches reaction-based abstraction methods that apply steady-state approximations to reduce the complexity and improve the analysis time of these models. The second module describes piecewise approximations to simplify non-linear reaction-based models of genetic circuits. The third module presents Markov chain models and methods for analyzing them. The fourth module provides methods to abstract models even further using state-based abstraction methods. Finally, the fifth module demonstrates methods, such as infinite-state stochastic model checking, to determine the likelihood that a genetic circuit hazard will cause circuit failure.
This course can also be taken for academic credit as ECEA 5935, part of CU Boulder’s Master of Science in Electrical Engineering.
Overview
Syllabus
- Reaction-based Abstraction
- This module will introduce methods to simplify chemical reaction models using automated model abstraction techniques.
- Piecewise Models
- This module will introduce methods for abstracting models using piecewise linear representations.
- Markov Chain Analysis
- This module will introduce Markov chains and analysis methods for them.
- State-based Abstraction
- This module will introduce a state-based abstraction workflow and analysis methods for these abstracted models.
- Infinite-state Stochastic Model Checking Case Study
- This module introduces genetic circuit hazards and how to determine the likelihood that they cause circuit failure.
Taught by
Chris Myers and Lukas Buecherl
