An open-source tool that combines multi-omics data with large language model reasoning to accelerate biological discovery and improve accessibility in bioinformatics analysis.
Hands-on exercise designing and simulating DNA “gel art” using in-silico restriction digests in Benchling, demonstrating principles of DNA reading, writing, and editing through creative gel electrophoresis visualization.
Python-based lab automation exercise using the Opentrons OT-2 platform to design, simulate, and analyze a liquid-handling protocol in Google Colab, emphasizing precision, reproducibility, and workflow optimization in automated experimentation.
In-silico protein exploration and introductory design—choose a protein, visualize structure/features, optionally predict or mutate with ColabFold, and document screenshots plus brief insights.
Peptide-binder design workflow—generate SOD1-targeting peptides with PepMLM, model complexes in AlphaFold-Multimer, compare ipTM scores, and select top candidates with brief analysis.
Intro to genetic circuit design—define a simple logic-based function, select biological parts, visualize the construct with SBOL symbols, and validate the concept using in-silico tools like Cello or iBioSim.
Continuation of genetic circuit design—contrast RNA-level and protein-level regulation (endoribonucleases vs proteases), explore layered and dynamic control, and connect these mechanisms to final project planning.
Introduction to cell-free protein synthesis—exploring transcription–translation systems outside living cells to enable faster, safer, and more controllable protein production for prototyping, biosensing, and on-demand applications.
Exploration of bioproduction and automation—analyzing lycopene and β-carotene synthesis in E. coli, linking pathway design to scale-up thinking, and reflecting on automation’s impact on protein manufacturing and iteration cycles.
Designing and reasoning about genome-scale DNA assembly—plan a multi-fragment Gibson construct, explore large-scale design strategies like recoding and tRNA relocation, and analyze lessons from minimal and synthetic genome projects such as JCVI-syn3.0 and Sc2.0.
Protein characterization and measurement—analyzing eGFP using LC–MS data from Waters Immerse Cambridge to interpret structure, peptide coverage, and post-translational modifications through chromatographic and mass spectrometric analysis.
Engineered living materials—survey key ELM classes, draft a use-case concept (organism, matrix, function, lifecycle), and outline feasibility and safety/containment considerations.
No-homework consolidation week—polish docs and projects, with optional explorations in frugal science (Foldscope, Paperfuge) and microbiome primers to inform design under real-world constraints.
Subsections of Assignments
Week 1: Principles and Practices
New heading
text
more content
graph LR
DNA-->RNA-->proteins
Context This page captures my Week 1 assignment for HTGAA 2025, reformatted for Hugo. It follows the structure of my original notes.
Why I decided not to use ChatGPT
I regularly use ChatGPT and think it’s great—however, per the TAs’ recommendation I completed this week’s thinking without it to practice independent reasoning about safety and governance. I spent significant time on this reflection and write-up.
Class Assignment
Describe a biological engineering application or tool you want to develop and why. It may relate to your HTGAA project, current research, or a curiosity.
Status: Done ✅
Idea
Use multi-omics approaches + a Large Language Model (LLM) to identify and explain key drivers of a biological phenomenon (e.g., mechanisms in health/disease) and assist design decisions.
Description
An open-source software tool that integrates data from multiple omics layers—genomics, transcriptomics, proteomics, metabolomics—then lets an LLM reason over that context to answer questions, surface hypotheses, and suggest next steps.
Pull structured knowledge from established databases:
Context This page captures my Week 2 assignment for HTGAA 2025, reformatted for Hugo. Yellow callouts mark assignment prompts; red callouts (if any) would mark AI-assisted notes.
SynBio Read/Write/Edit — Homework
Warning
About your documentation Document every step of your in-silico and lab work. Include sketches, screenshots, notes, and even failures (plus how you fixed them).
Part 0 — Basics of Gel Electrophoresis
Watch the lecture and recitation videos for this week.
Use one of the references above to obtain the sequence (e.g., the SnapGene page).
Example (searching for lambda sequence):
Example (downloading a sequence/plasmid file):
Add lambda DNA to Benchling
Import the FASTA/GenBank/SnapGene file into a new Benchling project (or paste the sequence).
Confirm length and cohesive ends are recognized (if provided by the source).
Plan restriction digests
Choose enzymes that generate a distinctive band pattern for your gel “art”.
Record enzyme names, buffers, incubation plan, and expected fragment sizes.
Simulate and export
Simulate the digest and generate expected band sizes.
Save screenshots of your enzyme map and gel preview.
Design your gel art
Arrange lanes to create a pattern or message.
Add a ladder lane and any controls you need.
Tip
Deliverables for Part 1
Short write-up of your design idea.
Enzyme list + expected fragment sizes.
Screenshots: annotated sequence map and gel preview.
Notes on what worked/didn’t (troubleshooting).
Submission checklist
Overview paragraph explaining the intent of the gel art.
Sources cited for the lambda sequence.
Benchling screenshots (sequence map with restriction sites, gel preview).
Parameters: enzymes, buffer, temperature, time, and expected band sizes.
Reflection: mistakes, fixes, and next steps.
Week 3: Lab Automation
Context This page recasts my Week 3 assignment (Lab Automation) for Hugo. Instructions reflect the original course guidance for this week. :contentReference[oaicite:0]{index=0}
Recitation
Recitation video: will be posted here. :contentReference[oaicite:1]{index=1}
Deadlines & readiness Submit your code before your lab time; then sign up for a robot time slot. If you hit scripting snags, reach out early—don’t wait until lab. :contentReference[oaicite:3]{index=3}
What to do
Pre-lab — Review the week’s materials and workflow. :contentReference[oaicite:4]{index=4}
Create your protocol — Write and test a Python routine in a Google Colab notebook. :contentReference[oaicite:5]{index=5}
Submit your completed protocol to your TA, and make sure you’ve booked a robot slot. :contentReference[oaicite:6]{index=6}
Book & file your submission using the links below.
You’ll use an Opentrons OT-2–style Python protocol to automate mixing/dispensing steps and generate outputs you’ll report (see Deliverables). If you’re new to the platform, these references help:
Google Colab (run and share notebooks). :contentReference[oaicite:13]{index=13}
Deliverables
Please include (or be prepared to report) the following with your submission:
A shareable link to your Colab notebook cell that holds the metadata + code (ensure “Anyone with the link” is a Viewer). :contentReference[oaicite:14]{index=14}
Simulation-derived totals:
Number of tips used by your protocol.
Volumes (µL) used per color channel: Red, Yellow, Green, Cyan, Blue. :contentReference[oaicite:15]{index=15}
A short notes/reflection on any issues and fixes.
Post-lab questions (mandatory)
Use a few sentences/bullets for each:
What did automation let you do more precisely or reproducibly than manual pipetting?
Where did your protocol struggle (e.g., timing, tip use, residuals), and how would you improve it?
If you repeated this on the robot with new constraints (less time, fewer tips), what would you optimize first?
How might this automation approach accelerate your final project work? :contentReference[oaicite:16]{index=16}
Tips
Keep commands simple and log outputs you’ll need for reporting (tip counts, per-color volumes).
Prefer distribute/transfer helpers when appropriate; fall back to explicit aspirate/dispense for tricky steps. :contentReference[oaicite:17]{index=17}
Test logic in Colab, then export the cell link for submission. :contentReference[oaicite:18]{index=18}
Prelude to this lab What this is
A minimal, well-commented Opentrons Python API v2 protocol that mixes five dye “channels” (Red/Yellow/Green/Cyan/Blue) and logs the total consumables.
Subsections of Week 3: Lab Automation
Week 3: Protocol Skeleton (Colab/OT-2)
Prelude to this lab
What this is A minimal, well-commented Opentrons Python API v2 protocol that mixes five dye “channels” (Red/Yellow/Green/Cyan/Blue) and logs the total consumables.
It will report: tips used and µL per color. It uses OT-2 GEN2 p300, 300 µL tips, a 96-well Corning 360 µL flat plate, and an Eppendorf 1.5 mL 24-tube rack. The log messages appear via protocol.comment().
Opentrons protocol (copy into a single Colab cell or .py)
# metadata / requirements — declare API level (>=2.15 recommended)# You can put apiLevel here or in `metadata["apiLevel"]`.requirements={"apiLevel":"2.15"}# see docs on versioningmetadata={"protocolName":"HTGAA W3 — Gel Art Mix (5 colors) — Skeleton","author":"Alireza Hekmati","description":"Distribute 5 color channels to a 96-well plate; log tip count and per-color volumes."}fromopentronsimportprotocol_apidefrun(protocol:protocol_api.ProtocolContext):# --- Deck / labware -------------------------------------------------------# Tip rack: 300 µL filtered/unfiltered (OT-2 standard)tips300=protocol.load_labware("opentrons_96_tiprack_300ul","8")# Plate: Corning 96-well, 360 µL flat bottomplate=protocol.load_labware("corning_96_wellplate_360ul_flat","2")# Tube rack: Eppendorf 1.5 mL Safe-Lock (24 position)tuberack=protocol.load_labware("opentrons_24_tuberack_eppendorf_1.5ml_safelock_snapcap","5")# Pipette: P300 Single-Channel GEN2 on leftp300=protocol.load_instrument("p300_single_gen2","left",tip_racks=[tips300])# --- Parameters you may tweak ---------------------------------------------VOLUME_PER_WELL=30# µL to dispense per destination wellN_WELLS_PER_COLOR=6# how many wells to fill for each colorMIX_BEFORE_ASPIRATE=(3,150)# (reps, µL) on source tube# Map dye sources in tube rack (prepare physical dyes in these tubes)# A1..A5 will be the 5 color sources.sources={"Red":tuberack["A1"],"Yellow":tuberack["A2"],"Green":tuberack["A3"],"Cyan":tuberack["A4"],"Blue":tuberack["A5"],}# Choose simple destination pattern: first 5 rows (A-E), left to rightrow_index={"A":0,"B":1,"C":2,"D":3,"E":4}dest_rows={"Red":"A","Yellow":"B","Green":"C","Cyan":"D","Blue":"E",}# --- Accounting: tip count & per-color volumes ----------------------------tips_used=0per_color_uL={k:0forkinsources.keys()}# Helper to pick up a tip and track itdefget_tip():nonlocaltips_usedp300.pick_up_tip()tips_used+=1# --- Work loop -------------------------------------------------------------forcolor,srcinsources.items():# Decide the row and the first N destinations in that rowrow=dest_rows[color]dests=plate.rows()[row_index[row]][:N_WELLS_PER_COLOR]# Mix the source (optional, helps with homogeneity)get_tip()p300.mix(MIX_BEFORE_ASPIRATE[0],MIX_BEFORE_ASPIRATE[1],src)# Use one tip per color; distribute to N wellsfordindests:p300.aspirate(VOLUME_PER_WELL,src)# atomic commands: aspirate/dispensep300.dispense(VOLUME_PER_WELL,d)# (optional) touch_tip to minimize droplets# p300.touch_tip(d)per_color_uL[color]+=VOLUME_PER_WELL# Final blowout back into source top to clear residualp300.blow_out(src.top())p300.drop_tip()# --- Log summary to run log -----------------------------------------------protocol.comment("=== HTGAA W3 — RUN SUMMARY ===")protocol.comment(f"Tips used (P300): {tips_used}")forc,uLinper_color_uL.items():protocol.comment(f"{c}: {uL} µL total")protocol.comment("Per-color wells filled: "+str(N_WELLS_PER_COLOR))protocol.comment(f"Volume per well: {VOLUME_PER_WELL} µL")protocol.comment("Plate: corning_96_wellplate_360ul_flat | Tips: opentrons_96_tiprack_300ul")
Week 4: Protein Design Part I
Context Week 4 focuses on in-silico exploration of proteins and introductory design workflows (no wet lab this week).
Goals for this week
Pick a protein of interest (from PDB or literature) and explore its structure.
Context This week extends protein design with a peptide-binder workflow. You’ll generate short peptides with PepMLM, then model peptide–protein complexes with AlphaFold-Multimer and interpret ipTM scores. :contentReference[oaicite:0]{index=0}
Goals
Use PepMLM to propose peptide binders for a chosen target (here: SOD1). :contentReference[oaicite:1]{index=1}
Predict peptide–target complexes with AlphaFold-Multimer and compare ipTM. :contentReference[oaicite:2]{index=2}
Summarize results and pick candidates for follow-up.
Part A — PepMLM → AlphaFold-Multimer (protein–peptide)
Warning
Read this whole assignment before starting. You’ll need a Hugging Face token and a GPU runtime for Colab. :contentReference[oaicite:3]{index=3}
Set model_type = alphafold2_multimer_v3. Benchmarking supports AF-Multimer for peptide–protein docking. :contentReference[oaicite:8]{index=8}
For each peptide, submit the complex SOD1Sequence:PeptideSequence and record ipTM.
Step 5 — Compare and interpret
Plot ipTM for all candidates (higher ≈ more confident interface) and note interface details (contacts, pose variety).
Short write-up: 1 paragraph on which peptide(s) you’d pursue and why (ipTM, pose, chemistry). :contentReference[oaicite:9]{index=9}
Tip
Minimal report for Part A
Table of peptides (PepMLM 3–4 + literature peptide), the edited SOD1 sequence (A4V), and ipTM per complex.
2–3 annotated screenshots from your top model(s).
One-paragraph conclusion + next steps (e.g., mutate peptide residues; re-score with more seeds).
Part B — Final Project (L-Protein mutants)
This is a compute-heavier follow-up for your final project—start early and keep logs of settings, seeds, and runtimes. (Details per course brief.) :contentReference[oaicite:10]{index=10}
Context Week 6 kicks off genetic circuits: specifying a behavior (truth table), choosing parts, drawing a design (SBOL Visual), and validating it in silico with design tools.
Goals
Pick a simple function (e.g., a 1–2 input logic gate that controls a reporter).
Select candidate parts (promoter, RBS, CDS/reporters, terminator) from a public registry.
Draw the construct using SBOL Visual glyphs.
Validate the design concept with a circuit tool and record assumptions/limitations.
Part A — Specify the function
Describe the circuit you want (one paragraph + a truth table). Examples: NOT, AND, NAND controlling a fluorescent reporter. Keep scope modest so results are interpretable.
Deliverables: plain-language description, inputs/outputs, and a truth table.
Part B — Choose parts (catalog references)
Pick plausible parts (or families) for each role; note alternatives and why you chose them.
Where to look: the iGEM Registry of Standard Biological Parts (promoters, RBSs, reporters, terminators, inverters, etc.). Cite the entry pages you consult. :contentReference[oaicite:0]{index=0}
Deliverables: a small parts table (name or ID, role, brief note, source link).
Part C — Sketch the construct (SBOL Visual)
Create a block diagram of your design using SBOL Visual glyphs (promoter → RBS → CDS → terminator; plus regulators/interactions as needed). Keep it consistent and readable. If you don’t use a drawing tool, a neat hand sketch is fine—just label elements clearly.
Reference:SBOL Visual v2 overview/specification for glyphs and best practices. :contentReference[oaicite:1]{index=1}
Deliverables: a single figure (PNG/SVG) and 3–5 bullet notes explaining the design.
Part D — In-silico sanity check (choose one)
Use one tool (or both) to reason about your design. Keep this exploratory—document what you tried and what you learned.
Cello (design automation) — encode logic and map to a part library to synthesize candidate DNA circuits; summarize the output design and limitations. :contentReference[oaicite:2]{index=2}
iBioSim (model/analysis) — build a small model for your construct and explore qualitative behavior. Screenshots of your setup are enough for this week. :contentReference[oaicite:3]{index=3}
Deliverables: 1–2 screenshots and a short paragraph interpreting the result.
Warning
Safety & scope This week is about design reasoning and documentation. Do not perform wet-lab work here. Be careful not to over-interpret tool outputs—treat them as hypotheses to guide thinking.
What to turn in
Problem statement + truth table
Parts table (IDs, roles, links)
SBOL Visual diagram (with a few explanatory bullets)
Q1. How do endoribonucleases (ERNs) decrease protein levels? Name two differences between how ERNs work and how proteases work.
Tip
Helpful context for Q1 (read-only references):
Endoribonucleases cleave RNA internally (phosphodiester bond) and are central to mRNA decay (e.g., RNase E).
Proteases cleave peptide bonds in proteins; many cellular machines (e.g., ClpXP, Lon, proteasome) are ATP-dependent unfoldase–peptidase complexes. Use these distinctions (substrate, chemistry, machinery/energy, cellular fate) to frame your answer.
Suggested explorations (optional, aligns with Part II theme)
Bistability & oscillations: revisit classic circuits (toggle switch; repressilator) and note what parameters control switching/period.
Layering control: combine transcriptional logic with RNA-level decay or proteolysis tags to sharpen responses.
Model quick-checks: use iBioSim/Cello or your preferred simulator to sanity-check qualitative behavior.
What to submit
Answers to Q1–Q3 (Q1 prompt above; Q2–Q3 per lab/recitation).
1–2 screenshots/figures that support your reasoning (e.g., model sketch, truth table, design diagram).
One short paragraph on how this informs your final project plan.
References & reading
mRNA decay / endoribonucleases: RNase E review (Nature Reviews Microbiology); roles in RNA metabolism (Microbiology Spectrum).
ATP-dependent proteases: overview of bacterial energy-dependent proteases (Trends in Biochemical Sciences).
Context Week 9 explores cell-free protein expression (TX–TL) and why it’s useful for prototyping, biosensing, and making proteins on demand. Page structure mirrors your original notes. :contentReference[oaicite:0]{index=0}
Prompt: Explain the main advantages of cell-free protein synthesis over in vivo expression, focusing on flexibility and control. Name at least two cases where cell-free is preferable to cell-based production. :contentReference[oaicite:4]{index=4}
My short answer
Faster start-to-data: no culturing required; reactions can be set up directly with DNA template + TX–TL mix. :contentReference[oaicite:5]{index=5}
Open & controllable chemistry: total control over DNA concentration, energy system, amino acids, cofactors, salts, and additives—without cell-membrane barriers or endogenous regulation. :contentReference[oaicite:6]{index=6}
Safer for the host / tolerant of toxic payloads: express proteins that would kill or stress living hosts (toxins, membrane proteins, lytic peptides). :contentReference[oaicite:7]{index=7}
Convenient for unusual chemistry: easier incorporation of unnatural amino acids or non-standard components by spiking the reaction. :contentReference[oaicite:8]{index=8}
When cell-free wins (examples):
Toxic proteins (e.g., pore-formers, nucleases) that crash cell cultures. :contentReference[oaicite:9]{index=9}
Transcription/translation machinery (from extract or reconstituted system)
Amino acids + nucleotides
Energy system (ATP regeneration) and cofactors These components are combined in an “open” reaction, enabling direct tuning and additions. :contentReference[oaicite:11]{index=11}
Prompt source: Patrick Boyle’s lecture questions (automation + scale; “metric tons” thought experiment).
Q1 — If all the molecular biology could be automated, what new questions would you ask or what new products would you make?
My notes (summary):
Automation would unlock scale (more variants, conditions, and replicates) and tighten iteration loops from in silico → in vitro → in vivo, reducing the gap between lab and real-world tests.
I’d target areas where iteration speed matters (e.g., monoclonal antibodies for oncology), using closed-loop optimization to refine binders and assays.
Q2 — If you could make metric tons of any protein, what would you make and why?
My notes (summary):
Environmental: enzymes for plastic degradation in marine settings (stability/half-life crucial).
Health: high-demand proteins (e.g., therapeutic proteins or vaccine antigens) for global access and outbreak response.
Context This week is about designing and assembling DNA at larger scales—from multi-kb constructs up to synthetic chromosomes—plus what “genome design” actually changes (recoding, tRNAs, loxPsym/SCRaMbLE, etc.). Good background reads are linked below.
Goals
Understand how DNA fragments are assembled into larger constructs (e.g., Gibson assembly). :contentReference[oaicite:0]{index=0}
See how minimal/synthetic genomes are built and why (e.g., JCVI-syn3.0, Sc2.0). :contentReference[oaicite:1]{index=1}
Explore genome-scale design choices: stop-codon recoding, moving tRNA genes to a neochromosome, and adding loxPsym sites for SCRaMbLE. :contentReference[oaicite:2]{index=2}
Background (skim these)
How to build a genome — overview of tools from gene → chromosome. :contentReference[oaicite:3]{index=3}
Building genomes to understand biology — perspective on synthetic genomics & what it enables. :contentReference[oaicite:4]{index=4}
Part A — Plan a multi-fragment assembly (paper design)
Pick a 3–6 fragment construct (total 3–10 kb, your choice). Design it as if you’ll assemble with Gibson:
Fragments & overlaps
Choose fragment boundaries and specify 20–40 bp overlaps compatible with Gibson.
Record lengths and GC%; avoid extreme GC in overlaps. :contentReference[oaicite:8]{index=8}
Primer plan
Draft primer sequences that add each overlap (annotate 5′ overlap vs. 3′ gene-specific portions).
Assembly notes
Note vector backbone, selection marker, and any features (origin, promoter, etc.).
Brief risk list (repeats, homopolymers, secondary structure).
Deliverables (put right on this page):
A small table (Fragment, Size (bp), Left overlap, Right overlap, Notes).
Your primers (5′→3′) with labeled overlap regions.
One paragraph explaining why you chose these cut points/overlaps. :contentReference[oaicite:9]{index=9}
Part B — Genome-scale design thought exercise
Pick one of these scenarios and write ~6–10 bullets + 1 short paragraph:
Recoding a stop codon (TAG→TAA) across a genome to free a codon for future use. Consider impacts on essential genes, synthesis/validation, and compatibility. Background on genome-scale builds helps frame the challenge. :contentReference[oaicite:10]{index=10}
Move all nuclear tRNA genes to a tRNA “neochromosome”. Why do this? What breaks if you don’t (stability, hotspots), and what checks would you run? :contentReference[oaicite:11]{index=11}
Install loxPsym sites (every ~10 kb or at 3′ ends of non-essential genes) to enable SCRaMbLE for rapid in-vivo rearrangement. What are the safety/containment and debugging concerns? :contentReference[oaicite:12]{index=12}
Deliverables:
Your chosen scenario, concise bullet plan, and a 1-paragraph risk/benefit summary with 1–2 citations. :contentReference[oaicite:13]{index=13}
Part C — Case studies (pick one to summarize)
Minimal cell JCVI-syn3.0: what remained, what functions were unknown, and why a minimal chassis matters. :contentReference[oaicite:14]{index=14}
Sc2.0 consolidation: progress, issues uncovered (e.g., growth defects), and how debugging fixed them; summarize a figure/result you find compelling. :contentReference[oaicite:15]{index=15}
Deliverable: 6–8 sentence summary + one “design lesson” you’d reuse.
Context This week pairs measurement with imaging and introduces protein characterization by LC–MS (liquid chromatography–mass spectrometry) using eGFP as a standard. Remote students use data produced at Waters Immerse Cambridge. :contentReference[oaicite:0]{index=0}
Prompt (per course page): Characterize eGFP structure (primary, secondary/tertiary) using liquid chromatography and mass spectrometry data generated in the Waters Immerse Cambridge lab. Remote students will analyze the shared data. :contentReference[oaicite:5]{index=5}
What to do
Skim the instruments & lab context used at Immerse Cambridge. (Public overview: Immerse Cambridge lab; Waters LC–MS systems.) :contentReference[oaicite:6]{index=6}
Review LC–MS basics (what LC does vs. what MS measures; peptide mapping). :contentReference[oaicite:7]{index=7}
Analyze the provided eGFP dataset (protein mass, peptide map coverage, PTM observations if present).
Summarize your findings with 2–3 annotated screenshots (chromatogram, spectrum/peptide map).
LC–MS primer(s): quick refreshers on chromatography, ionization, and peptide mapping.
Deliverables
A short methods note (what you looked at and why).
Protein-level result: observed intact mass (or rationale if using only peptide-level data).
Peptide-level result: coverage map (list or figure) and any notable PTMs.
Figures: 2–3 screenshots (e.g., TIC, MS/MS spectrum with labeled ions).
1–2 paragraphs interpreting what these measurements tell you about eGFP.
References / resources
Course page for Week 12 (schedule, homework text). :contentReference[oaicite:8]{index=8}
Immerse Cambridge (Waters Innovation & Research Lab) overview. :contentReference[oaicite:9]{index=9}
Waters LC–MS systems (platform overview). :contentReference[oaicite:10]{index=10}
LC–MS basics primer (Agilent; Pitt LC–MS overview). :contentReference[oaicite:11]{index=11}
Example eGFP MS dataset (public proteomics archive). :contentReference[oaicite:12]{index=12}
Week 13: Bio Design, Living Materials
Context This week focuses on engineered living materials (ELMs) and bio-based design—materials that incorporate living cells (or biologically grown matter) to achieve properties like sensing, self-healing, and biodegradability.
Goals
Survey the landscape of living materials (bacterial cellulose, engineered biofilms, mycelium composites, algae-based materials).
Frame a use-case and a design concept (function, organism, matrix, lifecycle).
Consider safety, containment, and environmental impact at a concept level.
Part A — Rapid landscape scan (mini-review)
Create a 6–10 bullet mini-review that covers:
At least three classes of living/bio-grown materials (e.g., engineered bacterial biofilms, mycelium composites, algal biopolymers, bacterial cellulose pellicles).
One strength and one limitation for each class (stability, rate of growth, hydration/processing, biosafety).
3–5 external references (papers, reviews, or credible overviews).
Tip: Look for high-level reviews on ELMs, plus concrete examples from design/architecture.
Part B — Concept sketch (your living material)
Write a one-pager describing a living material concept:
Purpose / scenario — What job should the material do (e.g., humidity-responsive façade tile, self-healing fill, low-energy lighting, degradable packaging)?
Biology — Which organism(s) and why (traits, growth conditions, containment considerations)?
Matrix / form — What scaffold or composite? Film, foam, “brick,” hydrogel, textile, or printed shell?
Signals & response — What should it sense or do? (e.g., color change, conductivity change, mechanical strength, VOC capture)
Context Per the course page: no assigned homework this week. Use the time to consolidate notes, polish documentation, and advance final projects.
Optional exploration — Frugal science
Foldscope: <$1 paper microscope enabling field microscopy and STEM education at scale. Over 1.8M units have been distributed globally. https://foldscope.com/ (overview); background summary and impact. :contentReference[oaicite:0]{index=0}
Paperfuge: ~20-cent, hand-powered centrifuge capable of ~125,000 rpm for sample prep in low-resource settings. Paper + string, electricity-free. See the Nature Biomedical Engineering paper and project page. :contentReference[oaicite:1]{index=1}
Talk (TED): Manu Prakash demos paper-based tools (Foldscope, Paperfuge) and the ethos of frugal science. :contentReference[oaicite:2]{index=2}
Warning
Safety & scope These are concepts and references, not lab instructions. Follow institutional biosafety guidance; don’t culture unknown environmental or clinical samples without approvals/training.
Optional reading — Microbiome primers
NIH Human Microbiome Project (HMP): accessible overview slide deck (PDF). Good for scope and key findings. :contentReference[oaicite:3]{index=3}
iHMP / HMP Phase 2: NIH article summarizing focus areas (e.g., preterm birth, IBD, T2D) and cloud analysis efforts. :contentReference[oaicite:4]{index=4}
If you want to add something this week
Tighten your project documentation (figures, captions, citations).
Add a short reflection on how frugal tools or microbiome thinking might inform your project (constraints, data, deployment).
Peptide therapeutic design targeting Cyclophilin D to block mPTP opening in ischemia–reperfusion injury—computational design now, with planned in vitro/in vivo validation and iterative optimization.
MS2 lysis concept—separate L-protein N- and C-termini by introducing a stop in a 33-nt non-overlapping window to preserve overlapping cp/rep genes and enable DnaJ-independent C-terminal function.
Subsections of Projects
Individual Final Project
Goal
Demonstrate that you can apply HTGAA concepts (e.g., Benchling workflows, computational protein design) in a focused, well-documented project.
Timetable (work log)
Date
Duration
Activity
2025/4/4
1:40
mitochondria research and computational tools
2025/4/6
2:00
mitochondria research
2025/4/7
3:00
mitochondria and CHO
2025/4/8
1:18
mitochondria
2025/4/9
3:40
mitochondria
2025/4/10
2:15
mitochondria
2025/4/13
0:40
Filing the Aims
2025/4/1
6:45
List of protein/antibody design models; binding & docking
2025/4/1
5:30
Intro to antibody and nanobody
2025/4/1
8:20
Files related to the protein
2025/4/1
7:10
Buying Colab Pro (done)
2025/4/18
3:15
Binder models test (AfDesign, EvoBind, BindCraft); list/try predictors
2025/5/1
2:00
Abstract art for the presentation
2025/5/2
1:40
Running AF Design and RFdiffusion
—
0:30
Writing presentation text
—
3:10
Update slides
2025/5/1
1:37
Fill out the new description
2025/5/1
1:20
Integrate feedback; Q&A prep; update script; refresh slides (new GIF)
Keep this page in sync with your slides if the idea changes.
SECTION 1 — Abstract
Ischemia-reperfusion (I/R) injury, where blood supply restoration paradoxically worsens tissue damage, is a critical challenge in conditions like heart attacks and organ transplantation. This project focuses on addressing a specific mitochondrial disruption central to I/R injury: the Cyclophilin D (CypD)-mediated opening of the mitochondrial permeability transition pore (mPTP).
The significance lies in pioneering a targeted therapeutic, as no FDA-approved drugs currently address this precise mechanism. The broad objective is to first computationally design and then experimentally validate a peptide inhibitor of CypD. The central hypothesis is that a peptide, designed using computational tools to target a specific sequence on CypD, can prevent mPTP opening, thereby mitigating calcium overload, ATP depletion, and subsequent cell death.
The first specific aim (HTGAA focus) involves identifying the CypD target and computationally designing the therapeutic peptide, followed by initiating its synthesis and preliminary in vitro validation.
Subsequent aims (Master’s thesis) will involve broader in silico, in vitro (e.g., MTT assay), and in vivo evaluations of the peptide, with a long-term aim of progressing successful candidates through clinical trials to become an approved treatment for myocardial infarction and similar I/R-related conditions.
SECTION 2 — Project Aims
Aim 1 (HTGAA scope): Identify a mitochondrial dysfunction implicated in I/R injury and computationally design a peptide-based therapeutic to address it. Use bioinformatics databases to select a target (e.g., Cyclophilin D and its role in mPTP opening) and protein-design tools (AI-assisted approaches) to generate a candidate inhibitory peptide sequence. Begin synthesis planning and initial in vitro validation (e.g., cloning into an expression vector such as pET-28a(+), expression in E. coli, and an initial cell-viability assay like MTT).
Aim 2 (thesis scope): Perform comprehensive evaluation of the designed peptide and explore broader therapeutic pathways related to the targeted mitochondrial disruption—in silico docking/MD of peptide–CypD interaction; in vitro characterization (e.g., assays probing mPTP, ATP levels); progress to in vivo studies for therapeutic potential and safety.
Aim 3 (impact & iteration): Define criteria to select top peptide candidates, iterate designs (e.g., point mutations, length/chemistry variants), and document decision points for potential translational follow-up.
Notes on tooling (search spaces)
When scouting tools and prior work, look across:
Literature search portals (PubMed/Nature journals)
GitHub (open-source repos, pipelines)
Hugging Face (protein/antibody design models)
Model families you tried/plan to try: AfDesign, EvoBind, BindCraft, RFdiffusion (record versions/seeds/params)
Deliverables (for this page)
A clear abstract and three aims (updated as the project evolves)
A timetable (work log)
Links to slides and any relevant external resources
Screenshots/figures you generate during design & evaluation (add as you go)
Group Final Project
Team & credit Team members: Alireza Hekmati and Yousif Graytee (Baghdad, Iraq). Yousif is affiliated with the Designer Cells Lab at Yonsei University (Incheon, South Korea). Page text credit: Yousif Graytee. :contentReference[oaicite:0]{index=0} Lab site: https://designercells.yonsei.ac.kr
Our goal
Make lysis independent of DnaJ by separating the C-terminal from the N-terminal of the MS2 L-protein. :contentReference[oaicite:1]{index=1}
Core idea (high level)
The L-protein’s productive interaction is believed to depend on DnaJ. Its N-terminal is highly soluble and has been reported dispensable for function, while the functional, transmembrane region resides toward the C-terminal. The MS2 lys gene substantially overlaps with the coat protein (cp) and replicase (rep) genes, but a 33-nt (11-codon) window exists with no overlap. :contentReference[oaicite:2]{index=2}
Zoom on the non-overlapping window :contentReference[oaicite:7]{index=7}
Explanation (from our notes)
The lys coding region overlaps with both cp and rep genes for most of its length. :contentReference[oaicite:8]{index=8}
There is a 33-nt region that does not overlap any other gene and maps to part of the N-terminal (not the functional C-terminal transmembrane domain). :contentReference[oaicite:9]{index=9}
Idea: introduce a stop codon within that non-overlapping window so the N-terminal is translated separately from the C-terminal segment, avoiding changes to cp/rep while allowing the C-terminal to function independently for lysis. :contentReference[oaicite:10]{index=10}
This mirrors prior observations that C-terminal truncations can be sufficient for lysis; here, rather than deleting the N-terminal, it is separated to minimize effects on overlapping genes. Hypothesis: DnaJ-dependence and steric hindrance relate to the N-terminal; separating termini could let the C-terminal act without DnaJ. :contentReference[oaicite:11]{index=11}
Warning
Safety note This page summarizes ideas at a conceptual level. It does not include wet-lab steps, organism handling, or execution details.
What we plan to document next
Rationale and design sketches (already above).
In-silico checks & controls (non-overlapping region annotations, reading-frame sanity).
Literature pointers on MS2 L-protein function and DnaJ dependence (to be added as we curate references).
