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).
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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.
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.
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.
