EST 5053 CCBS 2026
Course materials for EST 5053 - Control and Computation in Biological Systems, Fall 2026
Course name: EST 5053 - Control and Computation in Biological Systems, Fall 2026
Time: Tuesdays & Thursdays 10:40 a.m. - 12:15 p.m. (16 lectures)
Location: E10-305 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) — they support every teaching team (office hours to be announced)
Start here: How this course works — the rules, the topics, the format, the rubric, and the four assignments, in one page. Taught in the first 15 minutes of Lecture 1.
Submissions: github.com/chemaoxfz/ccbs-2026-submissions — teaching sign-ups, extension mini-essays, expositions and the final research essay are all collected here by pull request, with automatic merging.
2026 format: the course is 16 lectures, one topic each. I teach lectures 1 and 2. Lectures 3 to 16 are scheduled for student teaching: one to three students may own one entire 95-minute lecture, supported by the TAs. Every student teaches at least one lecture. After that required sign-up merges, a student may volunteer for another lecture with room; completing each additional lecture earns extra credit. I teach any lecture that remains unclaimed. The last 5 minutes of every student-taught lecture are mine, for the addendum: the gap, the take-away, and what the field says next. Each student-taught lecture is rated 0 to 3 by me and the TAs together, and separately by peer rating, and if a lecture does not hold up I step in and teach it, so the class cannot lose the material. That is what students present, lecturer-backed means in the table below. I hand out a core digest page for each lecture one week ahead; the team posts its exposition afterwards; and every student writes a weekly extension mini-essay that the TAs assemble into the audience’s own page. 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.
The course also has an argument. Lectures 1 to 7 build two components: binding, which is fast and reversible and therefore equilibrates, and catalysis, which is slow and effectively irreversible and therefore does not. Lecture 8 is the claim those seven were for, that to reason about a cell you need those two reaction types and nothing else. Lectures 9 to 16 apply that framework to one biological machine at a time, and end on open research problems. Lecture 8’s claim is itself live research: it is the founding claim of how my group works, that the components of a cell as a system and as a machine have been identified, and that binding-plus-catalysis is the model class of a cell rather than a teaching simplification. Lectures 11, 14 and 16 follow from it and are current research programmes too. If one of them grabs you, preparing that lecture is the first week of a research project.
How the topics were chosen
There is one judgement behind the list below. I imagined myself back in the first year of a PhD, wanting to learn what I would need in order to go and work on understanding and engineering biological systems, and asked of each candidate topic whether its absence would leave that person short. What survived is this list. The claim is not that it is complete, but that a first-year student who has this material is powerful: powerful enough to walk up to essentially any problem in biological systems with several ways to reason about it, know which one to reach for, know roughly what the answer should be before computing it, and be right that they can do it. That last part, the confidence, is what decides which problems you are willing to attempt.
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
- To go past the digest you are handed and come back with something the course did not already know
The three artifacts
Every lecture ends up with three pages, one written from each side of the room. They are the Materials column below, and they are also three of the four things you are graded on.
| who writes it | when | what it is for | |
|---|---|---|---|
| core | me | posted one week ahead | The spine of the ninety-five minutes: the argument, the derivations, every number with its source, and a list of what is deliberately left open. It is the floor for the team teaching that week, not the ceiling. |
| exposition | the teaching team | posted after they teach | The fuller read on the same topic, tutorial in shape, with the context and the history filled in. This is what you send someone who missed the lecture or wants more than it had room for. |
| extension | everyone in the audience | due the Monday after | One thing from the week taken past where the lecture stopped, told clearly and illustrated well. Short. The TAs assemble each week’s into one themed page, so the audience writes a chapter too. |
Lecture 1 carries a complete set, and its extension is written out as the worked example of the weekly assignment. Lectures 1 and 2 also have a lineage page, tracing where the ideas came from and who got them wrong first. That is a lecturer extra rather than part of the routine.
A core marked core (stub) fixes the lecture’s scope and spine but is not yet ready to teach from. Once its literature, derivations, worked examples, figures and checks are done, the label changes to core (full). Lectures 1 to 8 and lecture 10 have passed that check so far.
Schedule
The National Day window (Oct 1 to Oct 7) is skipped, so lecture 9 sits before it and lecture 10 after.
| # | Date | Topics | Materials | Format |
|---|---|---|---|---|
| 1 | 20260901 (Tue) | How this course works, and order of magnitude: the physics. Dimensional analysis, scaling, estimating from constraints, energy and motion, heat and diffusion, t ≈ x²/κ, null models | how this course works · core (full) · exposition · extension example · lineage | lecturer-taught |
| 2 | 20260903 (Thu) | From order of magnitude in biology to binding and catalysis. The cell’s ledger and crowding, mixing times, elementary vs composite reactions, the diffusion-limited on-rate derived, the catalytic bottleneck | core (full) · exposition · lineage · extension example | lecturer-taught |
| 3 | 20260908 (Tue) | From reactions to systems: how biochemical behavior emerges. Reactant, product and change vectors; flux and addition-removal views; fast-binding reductions in gene expression and metabolism; conserved signaling pools; growth dilution; the stability handoff | core (full) · exposition · lineage | lecturer-taught |
| 4 | 20260910 (Thu) | Dynamics: from simulations to possible behaviors. Addition-removal crossings, bistable memory, saddle-node bifurcations, the biological toggle, dimension, Jacobian and Hurwitz stability, state and energy, chemical realizability | core (full) | students present, lecturer-backed |
| 5 | 20260915 (Tue) | Stochastic dynamics: the master equation and Gillespie. Counts, jumps and propensities; probability flow and the zero boundary; Poisson calibration; exact event sampling; path, ensemble and deterministic limits | core (full) | students present, lecturer-backed |
| 6 | 20260917 (Thu) | Timescale separation: QSSA and the telegraph model. The fast-to-slow ratio and boundary layer; rapid equilibrium, standard and total QSSA; the physical root; promoter switching and exact moments | core (full) | students present, lecturer-backed |
| 7 | 20260922 (Tue) | Equilibrium is a local privilege. State weights and MWC; stationarity versus detailed balance; cycle affinity and current; a forbidden promoter response; proofreading and sensing tradeoffs | core (full) | students present, lecturer-backed |
| 8 | 20260924 (Thu) | The cell as a biomachine: binding and catalysis. Binding sets state and catalytic or fuel-coupled transitions commit change; three complete translations; rule composition, motor cycles and failure tests | core (full) | students present, lecturer-backed |
| 9 | 20260929 (Tue) | Multistability and oscillations. Toggle switches, MultiFate, cell fate as attractors; repressilator, relaxation oscillators, circadian clocks, cell cycle, replication initiation. Each as binding-catalysis, then simulated | core (stub) | students present, lecturer-backed |
| 20261001 + 20261006 | National Day holiday — no class | |||
| 10 | 20261008 (Thu) | The adaptation biomachine. Set-points, why feedforward fails, perfect adaptation as integral feedback, the internal model principle, chemotaxis, and the incoherent feedforward twist | core (full) | students present, lecturer-backed |
| 11 | 20261013 (Tue) | Analysis of binding and catalysis via reaction-order geometry. Reaction order as a log-derivative, reaction-order polyhedra, the three archetypes, regimes and trajectories, homotopy continuation | core (stub) | students present, lecturer-backed |
| 12 | 20261015 (Thu) | The computation biomachine. Logic gates in cells and the counting argument that ruins them, three genes as a perceptron, and what the natural computational task of a cell actually is | core (stub) | students present, lecturer-backed |
| 13 | 20261020 (Tue) | The metabolic biomachine. Stoichiometry, flux balance analysis as a linear program, bioenergetics, flux exponent control, genome-scale models, metabolic engineering | core (stub) | students present, lecturer-backed |
| 14 | 20261022 (Thu) | The growth biomachine. Proteome partition, the growth law λ = εφ_R, diauxie and shifts, growth rate as a maximum eigenvalue, and bet-hedging as a population-level strategy | core (stub) | students present, lecturer-backed |
| 15 | 20261027 (Tue) | Introduction to protein design. The de novo binder pipeline, RFdiffusion, ProteinMPNN, AlphaFold, protein language models, and how to judge what these models actually know | core (stub) | students present, lecturer-backed |
| 16 | 20261029 (Thu) | Foundational virtual cells that are fully mechanistic. What whole-cell models achieve, why they do not compose, and the proposal that one language for the whole cell makes them reachable | core (stub) | students present, lecturer-backed |
Assignments
Four things are graded. The full rules are on the course organization page.
| What | Weight | When |
|---|---|---|
| Teach at least one lecture, alone or in a team of up to 3 students. Sign up by pull request to the submissions repo. Additional lectures are voluntary and earn extra credit. | 40 | required sign-up closes Thu 2026-09-03 |
| Extension mini-essay, one per teaching week, best 6 of 8 counted at 5 points each. One thing from the week taken past where the lecture left it, clearly told and well illustrated. Worked examples: lecture 1, lecture 2, and a shorter one at the low end of the length | 30 | Mondays 23:59 |
| Research essay on a topic you care about, as a standalone tutorial-like HTML page with the full PCAPS chain | 20 | Thu 2026-11-05 |
| Class participation: questions, discussion, and submitting your peer ratings | 10 | every lecture |
Everything except the lecture is graded on whether the work was done properly, not on a rubric that subtracts points for not being excellent. Excellence is invited and rewarded with attention; it cannot be manufactured by deductions.
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 lecture 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. Lecture 9 reads much of this book in the binding-catalysis light.
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, The Order of a Reaction, and the research-essay (idea-to-essay) skill used for the lecture core pages.
Research with AI — the stance (the four commandments) and the PCAPS scaffold that presentations and essays in this course are graded against.
2025 course: the previous edition of this course, with lecture notes and scribe notes.