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There are 5 modules in this course
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.
This module will introduce methods to simplify chemical reaction models using automated model abstraction techniques.
What's included
13 videos6 readings2 assignments1 peer review
Show info about module content
13 videos•Total 136 minutes
Overview•7 minutes
Irrelevant Node Elimination•7 minutes
Production-Passage-Time Approximation•3 minutes
Michaelis-Menten Equation•12 minutes
Enzymatic Approximation Transformation•14 minutes
Models for Repression and Activation•19 minutes
Operator Site Reduction Derivation•7 minutes
Operator Site Reduction Transformation•15 minutes
Statistical Thermodynamical Model Overview•2 minutes
Statistical Thermodynamical Model Example•23 minutes
Dimerization Reduction•8 minutes
Application to the Phage Lambda Model•9 minutes
Stoichiometry Amplification•10 minutes
6 readings•Total 51 minutes
Course Updates and Accessibility Support•1 minute
Non-Credit Students: Welcome and Where to Find Help•10 minutes
Optional Reading Materials •10 minutes
Engineering Genetic Circuits Chapter 5 (Section 5.1 and 5.2)•10 minutes
Engineering Genetic Circuits Chapter 5 (Section 5.3 and 5.4)•10 minutes
Engineering Genetic Circuits Chapter 5 (Section 5.5 to 5.8)•10 minutes
2 assignments•Total 50 minutes
Enzymatic Approximation•25 minutes
Operator Site Reduction•25 minutes
1 peer review•Total 60 minutes
Reaction-based Abstraction of a Genetic Toggle Switch •60 minutes
Piecewise Models
Module 2•2 hours to complete
Module details
This module will introduce methods for abstracting models using piecewise linear representations.
What's included
12 videos3 readings3 assignments
Show info about module content
12 videos•Total 56 minutes
Logical Encoding Overview•7 minutes
Hill Functions•3 minutes
Critical Thresholds, Intervals, and Regulatory Domains•6 minutes
Piecewise Linear Differential Equations Overview•4 minutes
Piecewise Linear Differential Equations Solutions•4 minutes
Black, White, and Transparent Boundaries•7 minutes
Labeled Hybrid Petri Nets•4 minutes
Stochastic Finite State Machines Overview•2 minutes
Introduction to the Numerical Solution of Markov Chains Chapter 1-3 (Optional)•30 minutes
Introduction to the Numerical Solution of Markov Chains Chapter 8 (Optional)•10 minutes
3 assignments•Total 60 minutes
DTMC Quiz•20 minutes
Steady-State Distribution Analysis•20 minutes
Transient Analysis Quiz•20 minutes
State-based Abstraction
Module 4•3 hours to complete
Module details
This module will introduce a state-based abstraction workflow and analysis methods for these abstracted models.
What's included
8 videos3 readings3 assignments
Show info about module content
8 videos•Total 59 minutes
Overview of Abstraction Workflow•3 minutes
Genetic Toggle Switch Example•3 minutes
State-Based Abstraction•6 minutes
Transition Rate Calculations•5 minutes
Properties•9 minutes
Markov Chain Analysis•3 minutes
Examples•28 minutes
Qualitative Logical Models Overview•1 minute
3 readings•Total 70 minutes
Automated Abstraction Methodology for Genetic Regulatory Networks•30 minutes
Stochastic Model Checking of Genetic Circuits•30 minutes
Engineering Genetic Circuits Chapter 6 (Sections 6.5 and 6.6)•10 minutes
3 assignments•Total 40 minutes
Abstraction Method•10 minutes
Stochastic Model Checking•15 minutes
Quiz Qualitative Logic Models•15 minutes
Infinite-state Stochastic Model Checking Case Study
Module 5•5 hours to complete
Module details
This module introduces genetic circuit hazards and how to determine the likelihood that they cause circuit failure.
What's included
10 videos3 readings1 assignment2 peer reviews
Show info about module content
10 videos•Total 75 minutes
Genetic Circuit Failures•4 minutes
Automatic Model Generator•5 minutes
Function Hazards•8 minutes
Addressing Genetic Circuits Hazards•9 minutes
Continuous Stochastic Logic•4 minutes
Computational Prediction of Glitch Likelihood•5 minutes
Model Predictability•11 minutes
Laboratory Evaluation•9 minutes
Infinite-State Stochastic Model Checking •10 minutes
STAMINA Results•10 minutes
3 readings•Total 90 minutes
Genetic Circuit Dynamics: Hazard and Glitch Analysis•30 minutes
Stochastic Hazard Analysis of Genetic Circuits in iBioSim and STAMINA•30 minutes
STAMINA 2.0: Improving Scalability of Infinite-State Stochastic Model Checking•30 minutes
1 assignment•Total 10 minutes
Genetic Circuits Hazards•10 minutes
2 peer reviews•Total 120 minutes
Hazard Simulation Using iBioSim•60 minutes
Hazard Verification Using STAMINA•60 minutes
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Build toward a degree
This course is part of the following degree program(s) offered by University of Colorado Boulder. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹
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Build toward a degree
This course is part of the following degree program(s) offered by University of Colorado Boulder. If you are admitted and enroll, your completed coursework may count toward your degree learning and your progress can transfer with you.¹
¹Successful application and enrollment are required. Eligibility requirements apply. Each institution determines the number of credits recognized by completing this content that may count towards degree requirements, considering any existing credits you may have. Click on a specific course for more information.
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