BMEG-E 501 Biomedical Engineering
3 credits
- Prerequisite(s): None
- Delivery: On-Campus
Description
This course explores the intersection of engineering, biology, and medicine. Topics include bioinstrumentation, biomanufacturing, drug delivery, cellular and molecular bioengineering, biomaterials, human physiology and biomechanics, AI in healthcare, genomics, and tissue engineering.
Topics
Introduction to biomedical engineering
- Overview of biomedical engineering disciplines
- Applications in medicine and healthcare
Python for biomedical modeling
- Basics of Python programming
- Simulation and data analysis in Python
Bioinstrumentation
- Biomedical sensors
- Signal processing techniques
Biomanufacturing
- 3D printing technologies
- Scaffold design for tissue engineering
Drug delivery systems
- Kinetics of drug delivery
- Michaelis-Menten model
Cellular and molecular bioengineering
- Neuronal action potential modeling
- Hodgkin-Huxley equations
Biomaterials
- Material properties of biomaterials
- Stress analysis in biological tissues
Tissue engineering
- Scaffold fabrication techniques
- Finite element modeling for tissue mechanics
Human physiology
- Fluid dynamics in biological systems
- Application of Reynolds number in blood flow
Biomechanics
- Laplace’s law in biomechanics
- Poiseuille’s law in cardiovascular mechanics
AI in healthcare
- Machine learning for diagnostics
- Personalized medicine using AI
Genomics and bioinformatics
- Hidden Markov models for genomic data
- Data clustering techniques
Medical imaging
- Fourier transforms for image processing
- MRI signal analysis
Neural networks in medicine
- Convolutional neural networks for image classification
- Recurrent neural networks for medical applications
Drug delivery kinetics
- Monte Carlo simulations of drug release
- Bolus models in pharmacokinetics
Advanced machine learning techniques
- Bayesian networks in healthcare
- Surgical robotics
Learning Outcomes
- Analyze the principles of diffusion and apply Fick's law to model biological diffusion using Python.
- Evaluate the effectiveness of bioinstrumentation in signal processing and apply signal filtering techniques to improve signal quality.
- Analyze scaffold designs and evaluate 3D printing parameters for biomanufacturing applications.
- Analyze neuronal action potentials using the Hodgkin-Huxley model and modify key parameters to observe changes in cell behavior.
- Analyze fluid dynamics in biological systems using Reynolds number and apply Poiseuille's law to assess blood flow.
- Create a finite element model to simulate stress in biological tissues and evaluate material properties' impact on tissue behavior.
- Create and implement a convolutional neural network (CNN) to classify medical images using Python and evaluate its diagnostic accuracy.
- Create a computational model to simulate drug delivery kinetics using Monte Carlo simulations.
- Evaluate healthcare data using machine learning techniques, including Bayesian networks and neural networks, and apply these models to real-world healthcare problems.
- Create a comprehensive biomedical solution integrating multiple computational techniques and evaluate its real-world applications in healthcare.
Policies and Procedures
Please be aware of the following linked policies and procedures. Note that in individual courses instructors will have stipulations specific to their course.