University of Colorado Boulder
Engineering Genetic Circuits: Modeling and Analysis
University of Colorado Boulder

Engineering Genetic Circuits: Modeling and Analysis

Taught in English


Gain insight into a topic and learn the fundamentals

Chris Myers

Instructor: Chris Myers

Intermediate level

Recommended experience

24 hours to complete
3 weeks at 8 hours a week
Flexible schedule
Learn at your own pace
Progress towards a degree

What you'll learn

  • Design and analyze models of genetic circuits.

  • Simulate genetic circuit models using ODE simulation methods.

  • Simulate genetic circuit models using stochastic simulation methods.

  • Utilize genetic technology mappers to select parts for genetic designs.

Details to know

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January 2024


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There are 5 modules in this course

This week will describe the basics of modeling biological systems using chemical reactions, how these models can be represented using the Systems Biology Markup Language (SBML) standard, and how these models can be constructed using software tools such as iBioSim.

What's included

23 videos6 readings2 assignments1 peer review

This module will introduce the theory and methods for the analysis of genetic circuit models using ordinary differential equations (ODEs). In particular, it will describe the classical chemical kinetic model, numerical methods for ODE simulation of these models, and techniques to analyze these ODE models qualitatively.

What's included

13 videos3 readings3 assignments1 peer review

This module will introduce stochastic analysis methods for genetic circuits. In particular, it will introduce the stochastic chemical kinetics model, Gillespie's Stochastic Simulation Algorithm (SSA) to analyze these models, and various alternative stochastic analysis methods. Finally, the module will conclude with some additional topics: the Chemical Langevin Equation, stochastic Petri nets, the phage lambda model, and spatial Gillespie methods.

What's included

20 videos4 readings4 assignments1 peer review

This module presents several variations on the SSA algorithm to solve particular analysis problems. In particular, the hierarchical SSA (hSSA) methods enable the analysis of large models, the weighted SSA (wSSA) methods allow for the analysis of rare events, and the incremental SSA (iSSA) methods enable the determination of typical behaviors.

What's included

18 videos4 readings3 assignments

This module presents various ways that modeling can be utilized in genetic circuit design to select parts for optimal performance.

What's included

21 videos4 readings4 assignments


Chris Myers
University of Colorado Boulder
2 Courses1,342 learners

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