ENBC 441
Computational Systems Biology
Introduces quantitative principles for studying biological systems using computational modeling and simulations. Topics include continuous modeling of systems using ordinary differential equations, discrete modeling using Boolean networks and Markov chains, probabilistic modeling through Bayesian networks, stochastic modeling via Monte Carlo and Brownian and molecular dynamics, model optimization, and parameter estimation. Simulation algorithms that implement these approaches will be introduced through MATLAB programming.
Topics
- continuous modeling of systems using ordinary differential equations
- discrete modeling using Boolean networks and Markov chains
- probabilistic modeling through Bayesian networks
- stochastic modeling via Monte Carlo and Brownian and molecular dynamics
- model optimization
- and parameter estimation
Topics explicitly listed in official catalogue description
Prerequisites & course information
Prerequisite: ENBC351 . Restriction: Permission of ENGR-Fischell Department of Bioengineering department; and must be in the Biocomputational Engineering major.
Sources
- Course catalogue ↗2026-2027