BioE 201 Foundations of Bioengineering

This course contains elements of programing, statistics, electronics, materials and synthetic biology. It describes the fundamental principles and methods of different engineering fields to provide the necessary background for future specialization in the tracks of this program. The course aims to apply engineering principles to understand the physical, chemical, and mathematical basis of biological systems. The students will learn the origin of electrical biosignals, fundamental operation principles of modern electronics (sensing and control instrumentation) used at the interface with biological systems including EEG, ECG, biochemical sensors. They will learn about the basics of fabrication of devices involving microfluidics and microarray device design principles. The students will be then introduced to the different types of reactor configurations commonly used as bioreactors, operational parameters related to these reactors, and optimization of the reactors to maximize cell yield. The course will then introduce the principles of material science interfacing with biology, in order to design artificial implants and matrices for biomedical applications. This will broaden the knowledge of the chemical, physical and biological properties of the materials, focusing on the materials recently used in the biomedical field. In particular, students will develop critical analysis of biomaterial development and methods of characterization. Furthermore, it will also introduce cutting-edge techniques associated with 3D bioprinting. Finally, the students will be introduced to data-analytics and modeling with particular focus on R and MATLAB through hands-on exercises. Using R students will learn to plot data-distributions, calculate summary statistics, perform dimension reduction analysis (PCA, and other related techniques) and to run elementary bioinformatics scripts. In the modeling part students will work with simple mathematical models for synthetic biology (biological switch and oscillator) and basic predictive models (KNN, decision trees and SVM) using MATLAB.