Homework
Weekly homework submissions:
Professor Jacobson’s Questions Q1: Polymerase Error Rate vs. the Human Genome Raw polymerase error rate: DNA polymerase III (the baseline replicative polymerase) misincorporates roughly 1 in 10^4 to 10⁵ nucleotides during synthesis. I fyou factor in built-in proofreading checkpoints this error rate reduces to about 1 in 10⁷.
Week 1 HW: Principles and Practices
First, describe a biological engineering application or tool you want to develop and why. This could be inspired by an idea for your HTGAA class project and/or something for which you are already doing in your research, or something you are just curious about.
Week 2 HW :DNA Read Write Edit
Molecular Biology 101 1. Nucleotides In Silico Several free tools let you visualize and manipulate DNA/RNA sequences on your computer. Key options: SnapGene Viewer (plasmid maps), NCBI BLAST (sequence alignment), UCSC Genome Browser (reference genomes), and Benchling (all-in-one cloud platform). Benchling is a great starting point — it’s free, browser-based, and lets you import sequences (GenBank, FASTA, or raw), view annotated maps, design primers, run in silico digests, and align sequencing data. It also supports team collaboration and version control.
Week 3: Lab Automation HTGAA 2026 — Fiona Connolly What Lab Automation Can Do for Us? Lab automation is simply automating the processes in the lab. Scripted protocols, and integrated instruments to carry out experimental procedures with ideally minimal manual intervention. Particularly in molecular biology, this typically translates to very precise , temporally and temperature controlled liquid handling across the scale from picoL to Litres. The precise transfer of reagents, cultures, or genetic constructs between wells, plates, and vessels.
Week 4 Review: Protein Design Part I
Week 4 — Protein Design Part I At a glance. This guide covers the amino-acid alphabet, secondary structure geometry, β-sheet aggregation and amyloid, and the modern ML protein design approaches and tools then applies f it to E. coli DHFR (Part B/C) and the MS2 L-protein engineering proposal (Part D) as worked examples. Written as an inegrated field primer.
Week 5 Review: Protein Design Part II
Week 5 — Protein Design II AI-driven peptide and protein engineering, worked end-to-end on two targets. TL;DR Tool stack for peptide design: PepMLM (generate) → AlphaFold3 (validate) → PeptiVerse (triage) → moPPIt (re-target). Each tool catches a failure the others miss. Target 1: SOD1-A4V (ALS). PepMLM alone produces mode-collapsed peptides that all dock at the wrong AF3 default surface. moPPIt with motif guidance produces target-aware chemistry. Advance: B3 PAEKWFVFWHPT (sub-µM predicted Kd, dimer-interface targeted). Target 2: MS2 L-protein. ESM-style saturation scan vs random vs experiment-led picks. Big finding: language-model preference and experimental lysis function have r = +0.007 correlation. The model’s top picks would have destroyed function. Meta-lesson: Unsupervised protein language models predict sequence plausibility, not function. On under-represented protein families they can be actively misleading. Course: HTGAA Spring 2026 · Lecture (Mar 3): Gabriele Corso, Pranam Chatterjee — Protein Design Part II · Author: Fiona C (Committed Listener BioPunk Node)
Week 6 Review: Genetic Circuits I
Week 6 — Genetic Circuits I: Assembly Technologies Part 1 — DNA Assembly: PCR, Gibson, Golden Gate, and transformation A topic guide on the molecular-biology toolkit that underpins all of synthetic biology: amplifying DNA (PCR), cutting it (restriction enzymes), joining it (Gibson and Golden Gate), and getting it into cells (transformation). Written as a stand-alone primer rather than a homework Q&A. Part 2 (Asimov Kernel: building genetic circuits computationally) will follow as a separate page once the simulation work is complete.
Week 7 Review: Genetic Circuits Part II
Week 7 — Genetic Circuits II: Neuromorphic Circuits & Fungal Materials TL;DR: Cells can do more than switch genes on or off. By encoding signal weights in promoter and RBS strengths, and using RNA-cleaving enzymes as nonlinear activation functions, genetic circuits can implement perceptron-style neural computation — graded, multi-input, noise-averaging. This week also covers fungal materials: from mycelium composites and leather alternatives to engineering fungi as autonomous building repair agents. The worked DNA design demonstrates how to prepare a codon-optimised insert for two different assembly strategies.
Week 9 Review: Cell Free Systems
Cell-Free Systems At a glance. Cell-free protein synthesis (CFPS) is transcription and translation in a tube — the molecular machinery a cell uses to read DNA and make protein, decanted into a defined buffer. Because the reaction is open and tunable from the moment you set it up, CFPS does things a living cell cannot: it expresses host-killing proteins, it incorporates non-canonical amino acids at scale, it can be freeze-dried into ambient-stable point-of-care diagnostics, and it can be encapsulated in lipid vesicles to build synthetic minimal cells from the bottom up. This page is a topic guide to the platform — what it is, when to reach for it, how it fails, and how the field has used it over the past decade to move from a lab curiosity to a clinical and field-deployable technology.
Week 10 Review: Advanced Imaging and Measurement
Week 10 — Advanced Imaging & Measurement: How do we know what we made? Course: HTGAA Spring 2026 Lecture (Tues, Apr 7, 2026): Evan Daugharthy, Lindsay Morrison — Advanced Imaging & Measurement Tech Recitation (Wed, Apr 8): Waters Corp Team — Mass spectrometry Author: Fiona (Committed Listener track) At a glance. Mass spectrometry asks a precise quantitative question: did the molecule that came out of the column have the mass we predicted from the sequence? When the answer is yes within a few parts per million, it’s the same molecule. When it isn’t, the difference itself tells you what went wrong. This page builds the logic of intact-protein LC-MS, peptide mapping, and charge detection MS from first principles, with eGFP as the example throughout.
Week 11 Review: Bioproduction & Cloud Labs
Week 11 — Bioproduction & Cloud Labs One-line takeaway. A cloud lab is a wet-lab you drive from a laptop. This week you design a cell-free protein synthesis (CFPS) reaction that will run on one, in a global 1,536-well bioart canvas. Course HTGAA Spring 2026 Lecture Tues, Apr 14, 2026 — Reshma Shetty, Bioproduction & Cloud Labs Recitation Wed, Apr 15 — Ronan Donovan, Cloud laboratories | Author | Fiona Commited Listener BioPunk SF |
Week 12 Review: Building Genomes
Week 12 — Building Genomes How to rewrite an organism, one chromosome at a time At a glance. Synthetic biology spent its first two decades learning to read DNA. This week is about writing it — not gene by gene, but genome by genome. We’ll meet the smallest free-living cell ever built (473 genes, and we still don’t know what 149 of them do), the E. coli strain whose entire genetic code was rewritten by hand, the yeast whose chromosomes are being replaced one at a time, and the CRISPR tricks that let you dial metabolic pathways like an audio mixer. The final two sections bring the toolkit home to my own work: the MS2 phage L-protein group project (where the whole 3.5 kb genome is small enough to redesign from scratch) and the Cholera Shield final project (where genome-scale tools become the obvious answer to B. subtilis protease degradation, biocontainment, and multi-function spore-display optimization). This is the chapter where synthetic biology stops asking “can we edit this?” and starts asking “what if we just typed the whole thing from scratch?”
Week 13 Review: AI, SynBio, and Scaling Health Innovation with ARPA-H
Week 13 — AI, SynBio, and Scaling Health Innovation (ARPA-H) Why most synthetic-biology breakthroughs never become products — and what observability of the lab bench can do about it At a glance. Modern synthetic biology has a discovery surplus and a scaling deficit. We can engineer cells to make almost anything; we cannot reliably get those protocols to run in a second lab, a contract manufacturer, or a robot without burning a year on tech transfer.
Week 14 Review: Bio-design and Bio-fabrication - live from SynBioBeta
Week 14 — Bio Design & Bio Fabrication The dream of “real engineering” is what’s holding biology back. Bio-fabrication platforms are how we earn it. About this lecture. Week 14 of HTGAA Spring 2026 was delivered live from SynBioBeta 2026 in San Jose and simulcast back to the MIT classroom and to the global HTGAA cohort. David Kong called it “our first time ever doing this kind of coast-to-coast interaction”. George Church watched from the chat; Joe Jacobson — who co-founded the company whose displays became the bottom layer of the platform Michael Chen would demo twenty minutes later — stood up during Q&A. The week ran with two co-speakers in dialogue rather than two consecutive lectures: Christina Agapakis on bio-design as philosophy and practice, and Michael Chen on bio-fabrication as an actual platform.