Udemy, Coursera, 2U/edX Face Lawsuits Over Meta Pixel Use

Class Central is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

University of Colorado Boulder

Engineering Genetic Circuits: Modeling and Analysis

University of Colorado Boulder via Coursera

Go to class Write review

Overview

Class Central Tips
This course gives an introduction to how to create genetic circuit models. These models leverage chemical reactions represented using the Systems Biology Markup Language (SBML). The second module introduces methods to simulate these models using ordinary differential equation (ODE) methods. The third module teach stochastic simulation methods. The fourth module introduces several variations of the stochastic simulation algorithm. Finally, the fifth module introduces genetic technology method that leverage computational analysis for selecting parts and verifying their performance.   This course can also be taken for academic credit as ECEA 5935, part of CU Boulder’s Master of Science in Electrical Engineering.

Syllabus

  • Genetic Circuit Models
    • 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.
  • Genetic Circuit Analysis (ODEs)
    • 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.
  • Stochastic Analysis
    • 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.
  • SSA Variations
    • 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.
  • Genetic Circuit Technology Mapping
    • This module presents various ways that modeling can be utilized in genetic circuit design to select parts for optimal performance.

Taught by

Chris Myers

Tags

Reviews

Start your review of Engineering Genetic Circuits: Modeling and Analysis

Go to class

Never Stop Learning.

Get personalized course recommendations, track subjects and courses with reminders, and more.

Sign up for free
Someone learning on their laptop while sitting on the floor.