CST 5034 CCBS 2026

Course name: CST 5034 - Control and Computation in Biological Systems, Fall 2026

Time: Thursdays 8:50 a.m. - 12:15 p.m.

Location: TBD (2025: E10-306 on Yungu campus)

Website: https://chemaoxfz.github.io/ccbs/2026fall

Lecturer: Fangzhou Xiao Office hour and location: 1 hour/week, by email appointment, E1-321

TAs: Wenqin Zhou (zhouwenqin@westlake.edu.cn); Xinyu Wang (wangxinyu@westlake.edu.cn) — one pair supports each week’s lecturing students (office hours to be announced)

2026 format: the course runs as a co-taught seminar. Week 1 is taught by the lecturer (rules, topic map, format, demo lecture); from week 2, 3-4 students teach each weekly topic — one point of the topic per student, as a 30-minute session, each followed by a short 5-minute comprehension quiz written by the session’s presenter and approved by the TAs — rated 0-3 by the lecturer and by peer ratings, and ending with a 15-minute lecturer addendum. The lecturer steps in as a guaranteed fallback whenever quality does not hold. Every student who is not teaching that week contributes a short weekly extension micro-essay (one example, scenario, opinion, perspective or argument — conflicting positions are welcome), and the TAs assemble them into the week’s “extension by the audience” page. Each week therefore produces three artifacts on the site: the lecturer’s core digest, the students’ exposition, and the audience’s extension. The learning cycle is: ask AI to digest and write, understand and use it to work and iterate, then present it to others to communicate.

Course Description

Biological organisms exhibit many fascinating behaviors, from magical transformation of matter via thousands of steps of metabolic reactions, to robust homeostasis adapting to rapidly shifting environments, to survival and growth that balances persistence in extreme conditions and all-out ventures into opportunistic moments of rich nutrients, to dominance and terraforming of surroundings to its own advantage. Such complex behaviors involving lots of interacting components demand a rigorous and quantitative way of reasoning, like how we reason about complex engineered machines. In this course, we introduce and master tools of reasoning from three different schools of thought pondering about life: physics, system, and industry. Physics asks what life is as an object. System asks how life works as a machine. Industry asks how life could be useful as a tool. These three schools of thought have distinct origins, approaches to analysis, and goals. They shape how we think about life forms. The tools we learn from them span a wide range, from order of magnitude estimate to design of a single protein molecule, from Markov chains to control systems, from simple reasoning based on central dogma to whole-genome models. By the end of the course, you will be able to integrate these tools and perspectives into a cohesive whole and have the confidence to reason about any biological problem thrown at you, from single molecules to populations of organisms. No background needed, but an exuberant love for biology is mandatory.

Learning Objectives

• To understand and master the tools of analysis in quantitative synthetic biology
• To formulate problems encountered in synthetic biology into forms analyzable using the tools in quantitative synthetic biology
• To get familiar with the theoretical background and technical aspects underlying the tools
• To use an AI agent to go from a topic digest to a defensible, teachable presentation (claims traceable to sources, numbers reproducible from code)
• To communicate quantitative biology to peers and to diagnose what an audience actually got

Schedule

(The schedule table below shows only the dates for now; topics, materials and formats will be posted as the course develops. The week of 2026-10-01 is the National Day holiday and is skipped.)

Reference

This course does not have a textbook and all materials are self-contained. But the following reference might be helpful depending on your particular interests.

Westlake AI Campus — Westlake University’s internal platform providing locally deployed LLM models (DeepSeek, GLM, and more) for all students and faculty. Use these AI agents freely throughout the course: every weekly topic is prepared with an AI agent, verified and iterated by you, then presented and defended to the class.

Biomolecular Feedback Systems by Richard Murray. A nice (and free!) reference for general background on modeling of biological circuits (most relevant are the first 3 chapters), time-scale separation by singular perturbation, stochasticity, and some on feedback and control.

Feedback Systems by Karl J. Åström and Richard M. Murray. A great introduction to control systems, freely available online. This book is especially good on giving an intuitive yet rigorous picture of the ideas of control theory.

An Introduction to Systems Biology by Uri Alon. Another good general reference on the interplay between systems thinking based on simple models and biological implications.

biocircuits.github.io. A very good course with abundant online materials! With lots of recent examples, papers, and ready-to-use code implementing analysis and simulations of many biocircuits.

Cell biology by the numbers. A book freely available in easily accessible webpage form! Lots of interesting vignettes for Order of Magnitude (OoM) reasoning about biology. For example, do you know an mRNA molecule is about 10 times larger (volume or mass) than the protein it encodes?

Nonlinear dynamical systems and Chaos by Steven Strogatz. An accessible book, especially good at giving intuitive descriptions of dynamics for 1D and 2D systems.

Foundations of Chemical Reaction Network Theory by Martin Feinberg. A book on the more mathematical aspects of chemical reaction networks, especially equilibrium dynamics. A good reference book. Caution: try not to lose sight of biology, then you won’t be daunted by the math wrappings.

The essays — the research essays on Three Schools, Three Shocks, The Biomachine Perspective, Structure is Sparsity, and the research-essay (idea-to-essay) skill used for the weekly digests.

Research with AI — the stance (the four commandments) and the PCAPS scaffold that presentations in this course are graded against.

2025 course — the previous edition of this course, with lecture notes and scribe notes.