Engineering Genetic Circuits: Abstraction Methods

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.

• 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.