The two most important terms about my teaching philosophy are “fundamentals” and “integration”. Today, diverse fields of science and engineering are converging. When we attempt to make connections between these heterogeneous fields, which is often not trivial and requires substantial efforts and creativity, understanding the fundamentals becomes critical. In this context, I have a passion to educate students to acquire the capability of integrating fundamental knowledge of various disciplines with creativity. That is why I’ve developed highly interdisciplinary computational and systems medicine/biology courses, “Computational Foundations of Systems Biology” and “Computational Modeling/BioMEMS for Systems Biology”, at the University of Connecticut. The course materials are composed of fundamental concepts and principles from biology, medicine, mathematics, physics, computer science, and engineering. The key objectives of these courses are to understand the dynamics of multi-scale biological and healthcare systems, ranging from gene networks to endocrinology and distributed patients, and to use the learned knowledge and insights to create new medical diagnosis and treatment approaches. The courses also introduce emerging standards/technologies such as FHIR (Fast Healthcare Interoperability Resources), Cloud Computing, Microservices, Internet of Things, Web of Things, and Edge Computing in the context of practical applications. I’m also deeply interested in innovating outreach activities through MOOC (Massively Open Online Course) development. In this respect, with the generous support from IEEE and edX, my course titled “Introduction to Systems Biology” was developed in 2016  (currently not available due to revisions) which was geared toward secondary/high school STEM educators and students. The total number of enrollment for the spring 2016 session was 3,374.



1. Computational Foundations of Systems Biology  

BME 4985/BME 6086/CSE 4095/CSE 5095

The use of computers has become critical in many fields of science and engineering. In this course students will be introduced to computational systems biology which focuses on studying the dynamics and intelligent features (e.g., adaptation and robustness) of biological systems. It will be emphasized the tools and methods of  computational systems biology come from other computation-oriented fields such as computational physics, digital signal processing, control engineering, and digital logic. Students will also learn skills in programming using MATLAB, LabVIEW, and C# in the context of modeling, analyzing, estimating, and controlling real biological systems. Through a variety of assignments and projects, students will obtain a deeper understanding of physical and engineering principles applied to biological systems. Last but not least, students will read and present journal articles on topics covered in class, which will expose them to interdisciplinary approaches and views.

2. Computational Modeling/BioMEMS for Systems Biology  

BME 6086/ECE 6095 

Systems biology is a relatively new field, which studies complex interactions within biological systems. Computational modeling plays an important role in the study of systems biology as it can unravel complex dynamics often difficult to appreciate without mathematics. However, considering the complex nature of any biological systems, biological models should always be validated using relevant experiments, and BioMEMS (Biological or Biomedical MicroElectroMechanical Systems) provides an innovative platform for such experimental validation. BioMEMS is the science and technology of constructing devices or systems, using methods inspired from micro or nano-scale fabrication, that are used for processing, delivery, manipulation, analysis,or construction of biological and chemical entities. In this course, students will be introduced to BioMEMS with an emphasis on systems biology applications. Integrating BioMEMS with computational modeling for innovative systems biology research is highly interdisciplinary and requires knowledge and skills for applying molecular biology, chemistry, physics, medicine, engineering, computer science, etc. Through a variety of projects, students will obtain a basic understanding of integrating BioMEMS and computational modeling for systems biology applications.