How this course works

EST 5053 · Control and Computation in Biological Systems · Fall 2026. The rules, the topics, the format, the rubric, and the four things you hand in. Westlake University · Tue & Thu 10:40–12:15 · E10-305, Yungu campus · lecturer Fangzhou Xiao · TAs Wenqin Zhou and Xinyu Wang · 16 lectures, 2026-09-01 to 2026-11-03 Schedule updated 19 September 2026. With School of Engineering approval, there is no lecture on 29 September. Lecture 9 moves to 8 October and each later lecture moves one meeting later. The final lecture is 3 November. Presenter assignments and preparation lead times are unchanged. See the revised lecture schedule and assignment deadlines. Teaching format updated 11 September 2026. The format below applies from Lecture 5 (15 September). What changed and why.

The whole course in five lines

Teach one concrete point, in roughly 10 minutes or less. Choose a question, worked example, figure or claim from your assigned lecture and make it exceptionally clear. This is the complete required teaching assignment.
I lead the lecture and hand over when your point comes. Up to three students contribute to one lecture. I cover the rest and manage the connections and pace.
Prepare from the core page and rehearse with the TAs. The preparation timeline stays the same. Your exposition covers the point or points you teach.
More points are an optional challenge, agreed at rehearsal. Only a full normal score of 3 for the whole approved contribution earns the +5-course-point bonus.
The weights stay the same: teaching (40), extension mini-essays (30), research essay (20), participation (10). One excellent point can earn all 40 ordinary teaching points.

Part 1

The course

What it is about, the stance on working with AI that reshaped it, and the sixteen topics.

1What this course is about

Three schools ask three questions about life. This course teaches you to answer all three quantitatively, and then makes one claim of its own. Biological organisms do extraordinary things. They transform matter through thousands of metabolic steps. They hold themselves steady while the world around them shifts. They balance persistence in famine against all-out growth in plenty. They reshape their surroundings to their own advantage. Behaviors like these involve enormous numbers of interacting components, and reasoning about them demands the same rigor we bring to a complicated engineered machine. Three schools of thought have taken up that reasoning, and this course teaches the tools of all three.

Table 1. The three schools, and where each one lives in this course.
School	Its question	Its tools here	Lectures
Physics	What is life, as an object?	Order-of-magnitude estimates, scaling laws, statistical mechanics, energy and information	1, 2, 7
System	How does life work, as a machine?	Reaction networks, dynamics, stochastic processes, feedback and control, computation	3–12
Industry	How could life be useful, as a tool?	Metabolic engineering, growth laws, protein design, whole-cell models	13–16

The course has an argument, not just a syllabus. If a cell is a machine, then a machine has components, and the first question is what a cell's components actually are. This course's answer is that there are two.

Figure 1. The two components. Binding equilibrates, so a whole layer of the cell can be summarized by statistical mechanics. Catalysis does not, so it carries the dynamics. The two run on separated timescales, which is what lets one model hold both. The claim of lecture 8 is that this is not a simplification for teaching: it is the model class of a cell.

So the shape of the term is: lectures 1 to 7 build those two components and the tools for handling them, lecture 8 states the claim, and lectures 9 to 16 spend the claim, one biological machine at a time. No background is needed. An exuberant love for biology is mandatory.

2The AI-native take

Use AI to prepare material. Use the course to learn, check and explain it. The learning loop is input, learn, output. An AI agent can help you find explanations, draft code and produce a first figure. It cannot remove your task of checking the result, understanding why it works and explaining it to another person. This course asks you to practice that whole loop.

Figure 2. The design idea. A conceptual picture, not a measured throughput comparison. Ready access to drafts and explanations does not make understanding or communication automatic. Those are central tasks in this course.

So this course puts its contact hours where the narrow part is. The loop we run, every week, is:

ask AI to digest and write → understand it and use it to work and iterate → present it to others to communicate

Teaching makes your understanding visible. You will explain a point and answer questions about it. A polished AI-generated page is preparation material, not a substitute for being able to do that yourself.

The four commandments of research with AI

AI is our minion, not our peer. Own every claim, or do not use it. If you cannot explain it, it is vapor.
AI is a cyber-arm. Attach it to the work you care about most, not the chores you dislike. If an idea is alive in your mind, put it into the loop before it cools.
Your input-output bandwidth is the bottleneck, not the model's power. Specification in, comprehension out.
Be above AI on everything you think about. Generic AI output is the floor, not the ceiling. Know the generic answer before you claim your own expertise.

The full stance is at pebble-biofusion.github.io/workshop/method. Every presentation and essay in this course is graded against it.

Two practical consequences, and they are graded.

Every claim must survive a question. Not "the agent said so". You must be able to say why it is true, or say plainly that you do not know.
Every number must be reproducible from code you ran. Quoting a number you did not check is the single most common way an agent-assisted presentation goes wrong.

3The contents: 16 lectures

One topic per lecture, and one argument running through all of them. See the course schedule for student presenters.

The test these sixteen topics had to pass Would a first-year PhD student be short of an important tool without this topic? That is how I chose the list. It is not exhaustive. It aims to give you several ways to approach a biological problem, the judgment to choose among them, and a rough expectation of the answer before computing it. The confidence to attempt a new problem is what I most want you to leave with.

Figure 3. The arc. Seven lectures of build-up, one keystone, eight of payoff. This is why the topics are in this order and not another one, and it is why lecture 5 comes before lecture 6: the telegraph model in 6 is the stochastic version of the same reduction, so the two sit adjacent and make one argument twice.

Table 2. The 16 lectures. Fangzhou leads the course. Student assignments are on the course schedule.
#	Date	Topic
1	09-01 Tue	How this course works, and order of magnitude: the physics. Dimensional analysis, scaling, estimating from constraints, energy and motion, heat and diffusion, $t \approx x^{2}/\kappa$ , null models. Lecturer.
2	09-03 Thu	From order of magnitude in biology to binding and catalysis. The cell's ledger and crowding, mixing times, elementary versus composite reactions, the diffusion-limited on-rate derived, the catalytic bottleneck. Lecturer.
3	09-08 Tue	From reactions to systems: how biochemical behavior emerges. Reactant, product and change vectors; stoichiometric and production-loss views; fast-binding reductions in gene expression and metabolism; conserved signaling pools; growth dilution; the stability handoff.
4	09-10 Thu	Dynamics: local stability, phase portraits, bifurcations. Flows, fixed points, nullclines, the four local shapes, limit cycles, what reaction networks may and may not do.
5	09-15 Tue	Stochastic dynamics: the master equation and Gillespie. The counting regime, the CME, Markov chains and stationary distributions, exact sampling, Poisson universality, linear noise analysis.
6	09-17 Thu	Timescale separation: singular perturbation, QSSA, and the telegraph model. Fast and slow made rigorous, the enzyme derivation done properly, when it fails, and the stochastic version that produces bursting.
7	09-22 Tue	Equilibrium parts driven out of equilibrium. Boltzmann weights, the energy model of a regulated gene, detailed balance, dissipation, kinetic proofreading, limits on what a cell can know.
8	09-24 Thu	The cell as a biomachine: binding and catalysis. The restriction stated as a thesis, the three regimes of one binding reaction, and the same object found across metabolism, signalling, chromatin and motors. The climax of the first half.
9	09-29 Tue	Multistability and oscillations. Toggle switches, MultiFate, cell fate as attractors; the repressilator, relaxation oscillators, circadian clocks, the cell cycle, replication initiation. Every one rewritten as binding and catalysis, then simulated.
10	10-08 Thu	The adaptation biomachine. Set-points, why feedforward fails, perfect adaptation as integral feedback, the internal model principle, chemotaxis, and the incoherent feedforward twist.
11	10-13 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.
12	10-15 Thu	The computation biomachine. Logic gates in cells and the counting argument that ruins them, three genes as a perceptron, and the real question: what is the natural computational task of a cell?
13	10-20 Tue	The metabolic biomachine. Stoichiometry, flux balance analysis as a linear program, bioenergetics, flux exponent control, genome-scale models, metabolic engineering.
14	10-22 Thu	The growth biomachine. Proteome partition, the growth law $\lambda = \epsilon \phi_{R}$ , diauxie and shifts, growth rate as a maximum eigenvalue, and bet-hedging as a population-level strategy.
15	10-27 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.
16	10-29 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. The course's closing argument.

The National Day window, 2026-10-01 to 10-07, is skipped. Lecture 9 sits before it and lecture 10 after it.

Four of these lectures are live research, not settled material

Lecture 8 first of all. The claim that binding and catalysis are all of a cell's biochemistry, that this is not a teaching simplification but the actual model class of a cell, is the founding claim of how my group works. It says we have identified what the components of a cell are, as a system, as a machine. It is a research position, and you are allowed to attack it.

Then the three that follow from it: lecture 11, reaction-order geometry; the bet-hedging half of lecture 14; and lecture 16, the mechanistic virtual cell. These are my current research programmes.

Putting them in a course is deliberate. If one of them grabs you, preparing that lecture is the first week of a research project, and I would be glad to keep working on it with you afterwards.

Part 2

How it runs

The format, the rules of each part, the rubric that grades them, and the tools you will use.

4The format

One point per student by default. The whole lecture remains my responsibility.

Your assignment is to teach one concrete point. Choose something within your assigned lecture that you can explain, demonstrate and defend in roughly ten minutes or less. A point is something the audience should understand or be able to do afterwards, not a broad topic heading. For example: explain why a particular approximation fails, work one example through, or teach the conclusion a figure establishes.

Each lecture has up to three student contributors. Each student proposes their own point and receives an individual teaching score. Coordinate with the other presenters so the points fit together. Existing lecture assignments remain valid, and nobody has to take a second assignment to fill an empty lecture.

Figure 4. The handover. Your point is part of one continuous lecture, not a separate talk. This shows the sequence, not proportions of the class time.

I normally deliver most of the lecture. I handle its overall argument, harder connections, pacing and conclusion, and hand over when your point comes. A live simulation, a worked calculation or a question to the room is welcome when it helps make that point. I teach any lecture with no student contributor.

Optional challenge: more teaching points

Propose more only if you can deliver all of it exceptionally well. Bring the actual presentation to rehearsal. The TAs and I may approve it, suggest changes or narrow it to one point. The ordinary limit is half of the lecture's main teaching points per student, rounded down. We count the agreed live points, not slides, sub-bullets or optional reading. Timing and handovers are agreed at rehearsal, not awarded automatically with each extra point.

A whole lecture needs exceptional, explicit approval. A student or team may request this as an exception to the normal scope limit. I must approve the complete plan after rehearsal, including its coverage, timing and each student's contribution. Requesting it does not reserve the whole lecture or displace another student's agreed point. Without that approval, the lecturer-led format applies.

The core page is your starting point

The core page is posted one week ahead. Read the whole argument so you know where your point fits, then concentrate your preparation on that point. Teach it with your own explanation, a useful illustration and checked reasoning. You are not required to survey beyond the whole core or produce new research to earn full marks. The research-essay skill can help you investigate and build the material. You remain responsible for understanding it.

5The rules, part by part

The preparation dates stay the same. The scope you prepare is smaller.

Before: the preparation loop

Figure 5. The week before your lecture. Seven days from core page to lecture, with one required rehearsal in the middle and an optional second pass at the end.

Seven days out, the core page goes up. Choose your point and tell the TAs what the audience should learn, which example or figure you will use, and where it fits in the lecture. Check the claims and rerun the calculations you will present.
The rehearsal is on the Friday before if you teach on a Tuesday, and on the Monday before if you teach on a Thursday. Roughly 30 minutes with the TAs. Deliver your point, including the illustration and likely questions. If you want to cover more, propose and demonstrate all of it here. Fix the content, clarity and timing problems they find. Agree the final points and handovers with us. Nothing teaches that was not rehearsed.
The day before, there is still room for a second pass or another round of feedback if you want it, and that is also when your materials go final and get posted.

During: teach your agreed point

Answering questions is part of the assignment. Leave room for the audience to think and ask. “I do not know, but here is how I would find out” is an honest answer. Bluffing is not.

I may clarify, connect, correct or take over to keep the lecture on track. A routine handover or correction is not an automatic zero. Your score reflects the quality of your approved contribution, including any substantive errors or unfinished reasoning. If it cannot communicate the material and receives a 0, the existing make-up remains: an oral presentation of that material to me after the sixteen lectures.

After: the artifacts

Your exposition matches what you taught. Submit your corrected teaching material after class, in the teaching week. Explain your approved point or points clearly enough for someone who missed them. No full-lecture chapter is required for a ten-minute contribution. Joint materials should identify who taught which point.

core: mine, posted a week ahead.
exposition: yours, submitted to lectureNN/ as self-contained HTML, a PDF, notebook or slide deck after teaching.
extension: the audience's, assembled by the TAs from that week's mini-essays into one themed writeup.

A merged submission is a receipt, not a grade or approval of extra teaching scope. Staff check expositions before publishing them on the course site with your name.

6The rubric and the grade

The same 0–3 scale, applied to your agreed contribution. One excellent point earns full ordinary marks.

Your teaching is rated 0 to 3 by me and the TAs, averaged, and separately by your peers. The two are combined 60% the teaching side and 40% the trimmed mean of the class. Peer ratings are anonymous and published only in aggregate. The combined score is scaled to the 40-point teaching component: a 3 earns 40 points.

Table 3. The 0–3 scale, and what each score looks like from the back of the room.
Score	Meaning	Detectable in class
3	Excellent. The audience can use the point, and it was a pleasure to follow.	A classmate can explain the reasoning or redo the worked example afterwards. The claims withstand substantive questions. The presentation is accurate, clear, well illustrated and well paced, with assumptions stated and calculations checked.
2	Good. The main idea was delivered.	The argument landed, but an important qualification within the agreed scope was missing or hand-waved, or a question got a fuzzy answer.
1	The key ideas landed, a lot of the detail did not.	The worked example failed, or the class and the TA had to supply the fix. The room has the headline but not the mechanism.
0	The contribution did not communicate the material.	Communication and understanding broke down enough that the audience could not learn the agreed point. A lecturer intervention alone does not establish this. Make-up by oral presentation after the term.

The sub-criteria I use, so the number is reproducible rather than a mood:

Correctness and depth. Is the reasoning right? Are the mechanism, assumptions and important limits of your point understood?
Pedagogy. Does the room end up able to use it, not just to have heard it?
Communication. Structure, figures, pacing, and whether the argument has a spine.
AI-use standards. Every claim owned and explainable. Commandments 1 and 4.
Defense under questioning. What happens when someone pushes.

Optional extra credit: +5 course points

More than one approved point earns a bonus only with a full combined score of 3 for the whole contribution. The same voting and staff assessment above apply. A score rounded up to 3 does not qualify. More material or more minutes alone earn nothing. Weak additional points can lower your ordinary score: we do not grade only your best point.

The bonus is five points on the 100-point course total. It is separate from the ordinary 40-point teaching component, awarded at most once per student, with the final total capped at 100. One excellent point earns 40 teaching points. Qualifying multi-point teaching earns those same 40 points plus the five-point bonus.

A distinct contribution at another lecture may also be proposed in advance. It can earn the same one-time bonus if both the required and the additional contribution score 3 and complete the preparation and exposition requirements. It does not replace a lower required score. Any earlier individual extra-credit agreement will be honored.

Figure 6. The grade. Grade scale A 90–100, B 80–89, C 70–79, D 60–69, F below 60.

How the other 60 points are actually graded

The other assignments are graded on doing the work properly. Hand in a mini-essay that is genuinely an extension, clearly told and honestly sourced, and you get the five points. Hand in a research essay that does the four things asked of it, and you get the twenty.

Excellence is invited and rewarded with attention. It is not a reason to deduct points from an otherwise complete, honest essay.

7The tools, and where to get them

Use an AI agent, then check and own what you present.

Westlake AI Campus: free, internal, use it

Westlake AI Campus is the university's AI platform and the starting point for this course. If you have never used it, start with a question you actually care about.

Two more things you will use constantly:

The research-essay skill (also called idea-to-essay), on chemaoxfz.github.io/essays and at github.com/chemaoxfz/idea-to-essay. It provides a workflow from an idea through verified literature and reproducible analysis to a self-contained HTML essay. Use it for your exposition and research essay, keeping the scope appropriate to the assignment.
The essays at the same address. Three Schools, Three Shocks frames the whole course. The Biomachine Perspective is the lens. Structure is Sparsity and The Order of a Reaction sit behind lecture 11.

You are expected to use AI heavily. You are not permitted to use it as an oracle. The difference is whether you can defend the output, and that is exactly what is graded.

Part 3

What you hand in

Four assignments. The weights and weekly deadlines are unchanged.

8Assignment 1: teach one point

Keep your lecture assignment. Propose a point, rehearse it, teach it, and submit its exposition.

Every student has one required teaching assignment, worth 40%. The course schedule lists the presenters, reconciled with the final 11 September registration roster. If your lecture was assigned by me, complete the GitHub signup below so the submission system recognizes your account. Ask me if you need to change the assignment.

How to sign up

You sign up by adding one small file to the course submissions repository and opening a pull request. Once the checks run, merging is automatic, so your sign-up normally lands within a minute or two. GitHub may hold the first workflow run from a brand-new account for course-staff approval; if you see that message, tell a TA and keep the same pull request.

Figure 7. Every submission in this course works this way: the sign-up, the weekly mini-essays, your exposition and the final research essay. Learn it once and reuse it throughout the term.

Sign-up, step by step

Get a GitHub account if you do not have one.
Go to github.com/chemaoxfz/ccbs-2026-submissions and press Fork.
In your fork, create a file named signup/lectureNN-yourname.md, for example signup/lecture09-tongli.md. Copy signup/TEMPLATE.md and fill in your name, student ID, email, GitHub handle, the lecture number, and who your teammates are. The github field must be the account that opens the pull request.
Open a pull request against main. Nothing else. Do not edit any other file.
A bot checks the name, size, sign-up fields, lecture capacity and ownership. On a repeat sign-up it also checks that your identity matches your first one. It then merges the pull request. If it refuses, it tells you every problem in a comment. Fix and push again to the same branch.
signup/ROSTER.md records merged GitHub signups. The course schedule is the current teaching allocation, including lecturer assignments and enrollment changes.
To volunteer for another lecture, repeat these steps with that lecture number in a new file and a new pull request. Reuse the same yourname, student ID, email and GitHub account. One student may occupy only one place in any one lecture.

Existing signups stay valid

Registration closes on 11 September. You do not need a new signup for the one-point format. A lecture accepts at most three students. Discuss any move or voluntary additional assignment with me first, then update your signup. If the bot's ledger still includes an outdated record or blocks an agreed change, contact a TA. Do not edit another student's file.

If you get stuck with GitHub, ask the TAs, a classmate or an agent. Once the signup works, the same submission routine serves the rest of the course.

9Assignment 2: the extension mini-essay

One short piece per week, on one thing the lecture did not say. Eight of them, best six counted.

What it is

An extension mini-essay is not a summary. A summary of the lecture proves nothing: I was there. An extension takes one thing the week touched and pushes past it, and it is short on purpose so that the pushing has to be real.

Two families of angle, and you pick one thing from either, each week.

Bring something in from outside.

One example. A system, organism, circuit or experiment the lecture did not use, worked through with the lecture's tools.
One scenario. What happens if you change something. A parameter, a constraint, an assumption.
One opinion. Something in the lecture you think is wrong, oversold, or under-sold, argued rather than asserted.
One perspective. The same content seen from another field: physics, engineering, ecology, medicine, economics.
One argument. A claim of your own, with the reasoning that supports it.

Or push on what was taught.

Go deeper into one step. The derivation the lecture compressed into a line; work it out properly.
Examine an assumption. Every model in this course is a reduced model. Pick one reduction and ask what it costs and when it breaks.
Go one level more advanced. What does the current literature do with this topic that a first course does not say?
A different method. The same problem attacked another way, and what the two ways disagree about.
The complementary thing. The idea the lecture gestured at and moved past.

You do not have to invent these from a blank page. Point the research-essay skill at the week's topic and it will hand you the surrounding literature, the open questions and the disagreements. Picking one of those and doing it properly is exactly the assignment.

The five requirements

Short. A few paragraphs. Long enough to make one point properly, short enough that the room will actually read it. If it is running past a couple of screens you have picked two topics; keep one.
Clearly told. This one matters most. One clear chain of logic from start to finish, almost tutorial-like, so that anyone in the class can read it quickly and come away understanding it. Not notes to yourself. Not a wall of results. A story.
Well illustrated. Wherever a diagram, a picture or a graph is possible at all, make one. A drawn mechanism or a plotted curve does more work than three paragraphs, and making it is usually when you find out whether you understood.
One thing you made. A number you computed, a figure you plotted, an estimate you carried out. Not a number you copied. The code that produced it goes in the file or beside it.
Traceable. Every factual claim points at something a reader can check: a paper you actually opened, the core page, a database entry. One good source that you read beats ten you did not.

Conflicting positions are welcome and actively wanted. Two of you may take opposite sides on the same question in the same week. The TAs assemble each week's essays into one themed writeup for the site.

Two worked examples, written by me

One for each of the first two lectures, so you can see the same format applied to different kinds of idea.

“Two scaling laws, one theorem” (lecture 1). Takes three derivations the lecture kept apart, shows that two of them are the same counting argument, and is honest that the third is not. That is the connect two things shape.
“A ribosome's speed sets the shortest possible cell cycle” (lecture 2). Takes one number, computes with it, gets a hard bound, checks the bound against the fastest-growing bacterium known, and lands somewhere lecture 14 will pick up. That is the push one number until it says something shape.

Both end with a section saying what was actually read and at what depth. Copy that habit. It is worth more than another paragraph of argument.

When and how

One essay per lecture pair listed below, eight in total, including the pair containing your teaching contribution. Your best six scores count, so you have two free skips. This written assignment is unchanged and is separate from volunteering to teach additional points.

Use the Monday deadlines in the table. After the schedule change, the lecture pairs stay the same and the last four deadlines move one week later. The TAs assemble each pair's audience extension after submissions arrive.

Table 4. The eight extension mini-essays.
Week	Lectures	Due, 23:59
1	1, 2	Mon 2026-09-07
2	3, 4	Mon 2026-09-14
3	5, 6	Mon 2026-09-21
4	7, 8	Mon 2026-09-28
5	9, 10	Mon 2026-10-19
6	11, 12	Mon 2026-10-26
7	13, 14	Mon 2026-11-02
8	15, 16	Mon 2026-11-09

Submit the same way as the sign-up: a pull request to the submissions repo, adding weekN/weekN-yourname-ext.html. Start from TEMPLATE_ext.html. A single self-contained HTML file, no external assets. Use the same yourname as in your merged sign-up; the bot checks ownership and merges automatically.

Five points each, best six counted. You get them for doing it properly: a real extension, clearly told, illustrated, with something you made and sources you read.

10Assignment 3: participation

Ten percent, and it is the cheapest ten percent in the course.

Participation means three things, all of them observable:

You ask questions. During lectures, of your classmates. This is the one that matters. A room that does not push is a room the presenter cannot learn from, and it makes the 0 to 3 rating meaningless. You will find it much easier to interrogate a fellow student than to interrogate me, and that is precisely the advantage this format buys.
You take part in discussion and exercises led by the lecturer or a student.
You submit your peer ratings for the student contributions you hear. Not submitting is the one reliable way to lose these points.

Attendance alone is not participation. Being present and silent for sixteen lectures is worth very little here.

11Assignment 4: the research essay

One topic you actually care about, developed properly, due one week after the last lecture.

The capstone. Choose a topic you are genuinely interested in. It may come from the course, from your own research, or from somewhere else entirely, as long as you can bring this course's tools to bear on it. Then write it up as a standalone HTML essay: one self-contained file, readable on its own by someone who was not in this class.

Start with the research-essay pipeline. Not because the output is the essay, it is not, but because running it is how you find out what your idea actually is. It sweeps the literature, fetches the full texts and shows you where your topic sits among things people have already said. Once you can see that, you can formulate the PCAPS chain that follows, and formulating it is the real work.

PCAPS: the chain of logic every essay must have

Problem: what question, and why it matters.
Challenge: why it is not already answered. What makes it hard.
Approach: what you do about it.
Proof: the evidence, the derivation, the numbers, the simulation.
Significance: what changes if you are right.

These do not need to be five headed sections. They need to be visibly there, in that order, so a reader can follow the argument without reconstructing it.

Table 5. The four things the research essay is checked for.
Criterion	What I look for
The PCAPS logic is cleanly presented	The chain holds. The problem is stated sharply, the challenge is real rather than rhetorical, the approach addresses that challenge, and the proof supports the significance you claim. No step is missing and none is smuggled.
The literature and field context are clear	Your topic is visibly in conversation with existing work. You know who has asked this before and what they found. You read the full texts, not the abstracts, and it shows.
There is real insight, analyzed with some depth	Something is obtained: a derivation, a calculation, a simulation, a synthesis nobody handed you. Depth beats breadth. One thing understood properly beats five things surveyed.
The presentation is very clear	Tutorial-like. Any student in this class can read it start to finish and learn from it, without you in the room. Well illustrated: figures that carry the argument rather than decorate it.

Twenty points, and as with the mini-essays they are for doing the four things, not for being brilliant at them. An essay that does all four honestly gets full marks. Write it as if it were the core page for a lecture that does not exist yet, because the good ones will be exactly that.

Due Tuesday 2026-11-10, 23:59

One week after lecture 16. Submit by pull request to the submissions repo, as research-essay/yourname.html. Talk to me about your topic before then, ideally by the middle of October. A topic chosen badly is the most common reason a strong essay is not possible.

Part 4

Reference

Every date in one place, your next steps, and an optional record of the format change.

12Every deadline

Print this, or bookmark it.

Figure 8. The revised term. Sixteen lectures above the line, eight mini-essay deadlines below it, and no lecture between 24 September and 8 October. The final lecture is 3 November and the research essay is due 10 November.

Table 6. All deadlines for the term.
When	What
Fri 2026-09-11	Registration closes. Check your lecture assignment on the course schedule.
Mon 2026-09-07, 23:59	Extension mini-essay, week 1
Mon 2026-09-14, 23:59	Extension mini-essay, week 2
Mon 2026-09-21, 23:59	Extension mini-essay, week 3
Mon 2026-09-28, 23:59	Extension mini-essay, week 4
Mon 2026-10-19, 23:59	Extension mini-essay, week 5
Mon 2026-10-26, 23:59	Extension mini-essay, week 6
Mon 2026-11-02, 23:59	Extension mini-essay, week 7
Mon 2026-11-09, 23:59	Extension mini-essay, week 8
Tue 2026-11-10, 23:59	Research essay
rolling	Your core page 7 days before you teach; rehearsal with the TAs on the Friday (for a Tuesday lecture) or the Monday (for a Thursday one); materials posted the day before

13Your next steps

Keep your date. Choose your point. Rehearse it well.

Check your assignment on the course schedule. If it was lecturer-assigned, complete your GitHub signup. Contact me if the date needs changing.
Choose one point from your lecture and contact the TAs about the rehearsal. If you are already preparing, narrow your existing material with them. Additional points need rehearsal approval.
Keep the weekly routine. Extension mini-essays still follow the deadlines above. Teaching does not replace that week's mini-essay.

Links you will need all term

Course site: chemaoxfz.github.io/ccbs/2026fall
Submissions repo: github.com/chemaoxfz/ccbs-2026-submissions
Westlake AI Campus: hpc.westlake.edu.cn/#/ai-campus
Research with AI, the stance: pebble-biofusion.github.io/workshop/method
The essays and the research-essay skill: chemaoxfz.github.io/essays
Last year's course: chemaoxfz.github.io/ccbs

Lecturer: Fangzhou Xiao, E1-321, office hour one hour a week by email appointment. TAs: Wenqin Zhou (zhouwenqin@westlake.edu.cn), Xinyu Wang (wangxinyu68@westlake.edu.cn).

Format history (optional)

11 September 2026: what changed, why, and the previous version

The original assignment was to teach most of a whole lecture. One to three students shared the 95-minute meeting, with five minutes of opening administration and five minutes for my closing addendum. I normally intervened only if the teaching broke down. Registration closes today with 17 students enrolled.

Yesterday's Lecture 4 made me reconsider the workload I had assigned. Álvaro's teaching was excellent, patient, detailed and very well prepared. It also helped me see how much whole-lecture preparation asks of a student: not only understanding the content, but choosing its depth, managing dependencies and keeping a dense lecture moving. That is a course-design responsibility for me to address, not a reason to ask students to work still harder.

From Lecture 5, the required task is one carefully prepared point. I lead the lecture and hand over at each student's point. Broader teaching remains an approved challenge, with a five-course-point bonus only for full-score work. The aim is a more manageable preparation load and consistently high-quality contributions. We will keep discussing how this works in practice.

Earlier work keeps the terms under which it was prepared. Completed teaching is not retrospectively regraded. Existing assignments, the preparation timeline, ordinary grading weights and written assignments remain. Students already preparing can adapt their material with the TAs, without a new retrospective deadline.

Read the archived pre-update course guide. It records the former rules, including the original 3 September signup deadline. You do not need to read it to follow the current course.