Introduction to Bioengineering

Author

Iain Clark

This is the first year that I am offering my notes and course material in this format. I hope you find it useful, but you should view it as a work in progress. There are likely errors - your feedback can improve it for future students.

What this course covers

This course introduces the problems bioengineers solve and the concepts they use to solve them.

  • Bioengineering systems ranging from genetically engineered bacteria to biosensors.
  • Core physics and biology needed to understand and design these systems.
  • Problem-solving approaches used across bioengineering applications.

Source of material

In addition to new content, material was adapted from a previous version of the course taught by Terry Johnson and content from UC Berkeley’s Data 8 course. AI was used to extract and format text from existing content, revise text for clarity, create practice problems, check sources, and identify gaps in knowledge.

Advice to Students

STEM is difficult. That’s why it’s worth learning. Try not to compare your progress with someone else’s. Learning is not about keeping pace with others; it comes from working through ideas until they make sense to you.

A word of caution: AI offers an easy way around the pain of learning. It can often produce the right answer, but getting the right answer is not the same as understanding. When you ask AI to think for you, you are outsourcing the work and sacrificing your chance to grow.

It might be constructive, when you use AI, to self-check the concept or answer. Can you:

  • Explain why it is true.

  • Apply it to a new problem you’ve never seen before.

  • Recognize when an answer is wrong.

  • Defend (or reject) assumptions used to solve a problem.

  • Define the limitations of an approach or solution.

Developing that kind of understanding requires practice. You will need to solve problems on your own, make mistakes, and work through moments of confusion. It should feel difficult at times; that difficulty is a normal part of learning.

My view is that, even in a world shaped by AI, the more deeply you understand, the better prepared you will be to succeed after Berkeley. Perhaps the most sought-after engineers will still be those who have spent years training their own neural networks.

The contents of this book are licensed under Creative Commons Attribution-ShareAlike 4.0 International (CC BY-SA 4.0).