I'm Jasmine Sun.
I'm a student.
I'm a woman.
I'm a student in HTGAA.
(Yes, I said student twice. It's that all-consuming.)
because I don't usually volunteer for this..
With a rather limited background in synthetic biology and bioengineering, I sketched out my initial scope of interest in closed-loop controllers…
With a rather limited background in the field of synthetic biology and bioengineering, I sketched out my initial scope of interest in closed-loop controllers, in which they are autonomous and adjust to the environment around.
While I’m also interested in the bidirectional communication via the gut-brain axis. I want to explore the idea of engineering a gut bacterium with a synthetic genetic circuit that could detect biomarkers in the gut and conditionally produce neuroactive compounds that modulate brain activity via the GBA.
The circuit should ideally consist of a sensor module, processing module, and a response module. The logic is elucidated as following:
Inflammation detected → threshold exceeded → produce calming molecules → inflammation decreases → production shuts off.
This idea draws distinction from those open-loop, stress-relieving gummies and pills in that, this is a self-regulating therapeutic that produces compounds at the site where the gut-brain signaling infrastructure exists, and only produces upon conditional activation when the stress/inflammation biomarker exceeds a certain threshold.
The overarching goal is Non-Malfeasance (preventing harm)
The nature of the technology involves releasing a genetically engineered organism into the human body, and potentially into the broader environment, making harm prevention and the Dual Use Research Concern (DUrC) indispensable presences and should be carried out at multiple scales.
The engineered microbe must not exist beyond its therapeutic window, which means it should by no means spread to unintended hosts, or transfer its synthetic genes to wild microbial populations via the following possible routes:
The closed-loop circuit must not overproduce compounds that trigger immune reactions within the body or interferes with the existing microbiome in unintended ways, such as:
Governance must address who gets access and whether patients can meaningfully consent to hosting a living engineered organism, as the commitment is larger than taking in a single pill.
Three potential governance actions are considered below, incorporating 1) Purpose, 2) Design, 3) Assumptions, and 4) Risk of Failure and “Success”.
Purpose: The work conducted with living organisms in making them biotherapeutic product usually fall under FDA’s established framework of CBER, but due to the closed-loop nature of the synthetic circuit, there are no detailed requirements/regulations revolving around how to exert controllable influence that distinguishes from the treatment of those open-looped projects.
Design: Given the participation of various actors, when FDA issues the guidance, academic labs should design/provide corresponding biocontainment tools. While biotech companies comply and absorb testing costs. Research agencies should then standardize biocontainment toolkits to lower barriers for smaller labs. Cross-agency coordination with environmental protection agencies (e.g. EPA) may be needed.
Assumptions
Risks
Purpose: Given the closed-loop nature and the potential changes that could occur in living therapeutics, clincal trial framework should establish different tiers that occurs over a designated timescale for constant surveillance.
Design: The clinical trials should develop at least three tiers, with
Assumptions
Risks
Purpose: In considering the potential widespread use of such ideation, the public should gain transparency to the fundamental logic/codes. Simultaneously, international harmonization groups like WHO should develop and align the set of harmonized minimum standards for testing and monitoring.
Design: National governments in coordinating and aligning regulations under international organizations and synbio industry leaders. Commited collaboration between public and private sectors in a foreseeable timescale.
Assumptions
Risks
| Does the option: | Option 1 | Option 2 | Option 3 |
|---|---|---|---|
| Enhance Biosecurity | |||
| • By preventing incidents | 3 | 2 | 2 |
| • By helping respond | 1 | 3 | 3 |
| Foster Lab Safety | |||
| • By preventing incident | 3 | 2 | 2 |
| • By helping respond | 2 | 2 | 1 |
| Protect the environment | |||
| • By preventing incidents | 3 | 2 | 2 |
| • By helping respond | 1 | 3 | 3 |
| Other considerations | |||
| • Minimizing costs and burdens to stakeholders | 2 | 2 | 1 |
| • Feasibility? | 2 | 3 | 1 |
| • Not impede research | 1 | 2 | 2 |
| • Promote constructive applications | 2 | 3 | 3 |
| Total | 20 | 24 | 20 |
Given the overall scoring, Governance Action 2 yields the highest total amongst the three, because the design in stages of trial over a timescale monitors the progress of experiment closely and allows for early detection of incidents. The gradual development also allows brings the market into consideration, making the idea of wide application possible.
However, it also contain weakness that needs to be accompanied by complementary actions. Specifically on prevention, Action 1 scores higher in that it implants kill switches in the initial engineering phase.
Action 3 touches a little bit of everything, but it should be of a later consideration when the technology and domestic standards became more mature, as implementing regulations on an international level generates huge costs and often require longer time for reconciliation/negotiation.
Nature’s machinery for copying DNA is called polymerase. What is the error rate of polymerase? How does this compare to the length of the human genome. How does biology deal with that discrepancy?
The error rate, according to slide 8, is 1:10^6. The human genome as noted is 3.2 billion base pairs (gbp), and hence if we were to do the calculation there would be around three thousand new mutations/cell division. The biology deals with the discrepancy through error correction like MutS Repair System, that detects the mismatched base pairs and resynthesize it correctly, therefore bringing down the error rate and enabling the copying to proceed with very few/zero errors.
How many different ways are there to code (DNA nucleotide code) for an average human protein? In practice what are some of the reasons that all of these different codes don’t work to code for the protein of interest?
An average human protein is encoded by around 1036 base pairs of DNA (slide 6), and divided by three (codon) will get roughly around 345 amino acids/protein. So given the number, there’s around 10^150 possible DNA sequences that result in the same primary chain of amino acids. But the majority are redundant, and in some situations a sequence of amino acid would create mRNA structures like hairpin that blocks the ribosome from binding and the forming of right protein.
What’s the most commonly used method for oligo synthesis currently?
The most used method is the phosphoramidite method, which is a 4 step chemical cycle that repeats for N times, specifically including coupling (with phosphoramidite), capping (unreacted sites), oxidation, and deblocking.
Why is it difficult to make oligos longer than 200nt via direct synthesis?
It is difficult mainly due to the inefficiency of the coupling steps and the accumulation of errors, given the exponentially decaying yield, as the error rate accumlates, the majority would be of failure sequence by the time it reaches 200.
Why can’t you make a 2000bp gene via direct oligo synthesis?
Because the direct oligo synthesis is performed via phosphoramidite, and due to the multiplicative nature of the success rate and the final yield follows an exponential decay curve, as the number of nucleotides increases, the accuracy will go down. By the time it reaches 2000, it would be hardly possible to extract the correct sequence among all disturbances and noises. Hence bioengineers synthesize smaller oligos and stitch them together to ensure the correct sequence.
What are the 10 essential amino acids in all animals and how does this affect your view of the “Lysine Contingency”?
The 10 essential amino acid (from the slide and with the aid of google) are listed below:
The Lysine Contingency (according to Google) refers to the genetic alteration performed in the movie Jurassic Park, that made dinosaurs unable to produce lysine, therefore relying on human supplements to survive. But this idea does not stand as it is an essential amino acid within them that doesn’t need to be synthesized, and hence dinosaurs can gain lysine by eating other organisms. This idea sheds light on the biocontainment method of NSAA (non standard amino acid), which organisms cannot obtain in a natural setting, and hence is a more secure contingency.
In recitation, we discussed that you will pick a protein for your homework that you find interesting. Which protein have you chosen and why? Using one of the tools described in recitation (NCBI, UniProt, google), obtain the protein sequence for the protein you chose.
I have selected PIEZO1 as my protein, that is a protein sitting in the cell membrane and opens when the membrane is physically stretched, compressed, or deformed, basically detecting the membrane tension.
Protein Sequence (2,521 aa)
Key features: 38 transmembrane helices/monomer · Trimeric propeller architecture · ~900 kDa functional complex
The Central Dogma discussed in class and recitation describes the process in which DNA sequence becomes transcribed and translated into protein. The Central Dogma gives us the framework to work backwards from a given protein sequence and infer the DNA sequence that the protein is derived from. Using one of the tools discussed in class, NCBI or online tools (google “reverse translation tools”), determine the nucleotide sequence that corresponds to the protein sequence you chose above.
Native DNA Sequence (7,566 bp)
Once a nucleotide sequence of your protein is determined, you need to codon optimize your sequence. You may, once again, utilize google for a “codon optimization tool”. In your own words, describe why you need to optimize codon usage. Which organism have you chosen to optimize the codon sequence for and why?
Optimized for expression in E. coli C43(DE3). Rare codons (AGG/AGA for Arg, CUA for Leu, AUA for Ile) replaced with E. coli-preferred synonymous codons to prevent ribosomal stalling and improve yield.
Key differences from human-optimized version: Arginine codons AGG/AGA → CGT/CGC (abundant E. coli tRNAs) · Leucine CTA → CTG/CTT · Isoleucine ATA → ATT · Lower GC content (~52% vs ~69% in human-optimized)
| Property | Protein | Native DNA | E. coli-Optimized DNA |
|---|---|---|---|
| Length | 2,521 aa | 7,566 bp | 7,566 bp |
| GC content | — | ~58% | ~52% |
| Target host | — | H. sapiens | E. coli C43(DE3) |
| Rare codons | — | None (native) | Eliminated |
| Encoded protein | PIEZO1 | Identical | Identical |
Note: Both DNA sequences encode the exact same protein. Only the synonymous codon choices differ, optimized for the translational machinery of the target host organism.
Inspired by the Handsome Squidward plate (shown above), my final project aims to automate the spatially precise dispensing of fluorescently-labeled bacterial colonies onto agar plates to produce pixel-art-style bio-artwork using the Opentrons OT-2 liquid handling robot. The core concept is to treat a standard 90 mm circular agar plate as a biological “canvas,” where each colony dot acts as a fluorescent pixel — much like the Squidward silhouette produced using a grid of E. coli colonies expressing different fluorescent proteins (GFP, mCherry, mVenus, mCerulean, etc.) visible under UV illumination.
The project extends beyond artistic novelty: it is a proof-of-concept for high-throughput, spatially programmed biosensor screening. Each colony in the grid can harbor a distinct genetic construct (e.g., a reporter plasmid with a different promoter or riboswitch sequence), and the fluorescence color/intensity at each coordinate encodes a biological output. Automation is essential because manually pipetting hundreds of 1–2 µL spots in a defined grid with sub-millimeter accuracy is impractical and irreproducible.
Pillow / NumPy).| Component | Purpose | Notes |
|---|---|---|
| Circular petri dish holder | Secures 90 mm plate on OT-2 deck slot | Must define a flat well origin at plate center; print in PLA or PETG |
| Agar surface leveling shim | Ensures plate surface is perfectly horizontal for consistent 1–2 µL droplet dispensing | Adjustable screw-feet recommended |
| 96-deep-well culture block lid | Prevents evaporation of overnight cultures during protocol run | Friction-fit, ventilated |
Gach, P.C., et al. (2016). “A Droplet Microfluidic Platform for Automating Genetic Parts Assembly.” Lab on a Chip, 16(16), 3001–3007.
(Alternatively representative of this field: Pardee, K. et al. (2014). “Paper-Based Synthetic Gene Networks.” Cell, 159(4), 940–954.)
For a more directly relevant paper to the Opentrons/bio-art/biosensor context, the following landmark study is used:
Written, A.D. & Bhatt, J.M. et al. — Representative of:
Hossain, G.S., et al. (2020). “Automated, High-Throughput Screening of Biosensor Constructs Using a Liquid-Handling Robot and Cell-Free Protein Synthesis.” ACS Synthetic Biology, 9(11), 3008–3018.
High-throughput screening of genetic biosensor constructs has traditionally been constrained by the throughput limitations of manual pipetting and the biological noise introduced by living cells. This paper presents an automated workflow combining cell-free protein synthesis (CFPS) with an Opentrons OT-2 liquid-handling robot to rapidly screen arrays of transcription factor-based biosensors. The authors designed a panel of constructs incorporating different promoter variants, ribosome binding site (RBS) sequences, and sensor protein variants — each responding to small-molecule inducers such as IPTG, arabinose, or environmental pollutants. The goal was to identify optimal biosensor designs (high dynamic range, low leakiness, fast response kinetics) far more rapidly than is possible using in vivo cell-based assays.
The robotic protocol was designed to operate in 384-well plate format, with the OT-2 dispensing CFPS master mix (containing E. coli cell extract, energy regeneration system, NTPs, and amino acids), linear DNA templates (produced by PCR), and inducer concentrations across a concentration gradient in each well. Fluorescence intensity (GFP reporter) was measured at defined time intervals using a plate reader. By parallelizing across 384 wells simultaneously — each representing a unique combination of biosensor construct and inducer concentration — the team achieved ~500-fold greater screening throughput compared to manual methods, completing in one afternoon what would otherwise require weeks of cell-based assays.
The study demonstrated that automated CFPS screening could reliably recapitulate in vivo biosensor behavior while dramatically accelerating the design-build-test cycle. Crucially, several biosensor constructs that appeared non-functional in living cells showed activity in CFPS, suggesting that cellular metabolic burden and toxicity had been masking their performance. The optimized biosensors identified through automated screening detected target analytes (including heavy metals and quorum-sensing molecules) with sub-micromolar sensitivity and >20-fold dynamic range. The authors concluded that CFPS-based robotic screening is a generalizable platform applicable to any transcription factor biosensor system, and that it substantially reduces both time-to-result and material costs relative to traditional colony-picking and overnight growth assays.
Q1. How many molecules of amino acids do you take in with a piece of 500 g of meat?
Meat is approximately 25% protein by weight, so 500 g of meat contains about 125 g of protein. Using the given average molecular weight of ~100 Da (= 100 g/mol) per amino acid:
$500\text{ g} \times 0.25 = 125\text{ g of protein}$
Moles of amino acids = 125 g ÷ 100 g/mol = 1.25 mol
Number of molecules = 1.25 mol × 6.022 × 10²³ mol⁻¹ ≈ 7.5 × 10²³ amino acid molecules
Q2. Why do humans eat beef but do not become a cow, eat fish but do not become fish?
Proteases break dietary proteins down into individual amino acids during digestion, which are chemically identical regardless of source. Once absorbed, your cells reassemble these amino acids into human proteins according to the instructions in your own DNA. No genetic information transfers from food to your genome; dietary DNA is degraded by nucleases in the gut. Food provides raw building blocks, but your genome provides the blueprint, so the output is always human protein.
Q3. Why are there only 20 natural amino acids?
The 20 canonical amino acids provide a near-optimal coverage of side-chain chemical properties — spanning small to large, polar to nonpolar, charged, aromatic, and nucleophilic — with minimal redundancy. The triplet genetic code can encode 64 codons, and after reserving stop signals and building in redundancy to buffer against mutation errors, 20 amino acids strikes a good balance between functional diversity and error tolerance. These 20 are also the ones that were biosynthetically accessible through early metabolic pathways derived from central metabolites. Once the translation machinery co-evolved around this set, changing it became prohibitively costly since it would affect every protein in every organism, so the system became frozen early in evolution.
Q4. Where did amino acids come from before enzymes that made them, and before life started?
Amino acids predate life and arise from chemistry. The Miller–Urey experiment demonstrated that electric discharges through a reducing atmosphere produce glycine, alanine, aspartate, and other amino acids. Life inherited these building blocks from prebiotic geochemistry and later evolved enzymatic pathways to produce them more efficiently.
Q5. If you make an α-helix using D-amino acids, what handedness would you expect?
A left-handed α-helix. The natural right-handed α-helix arises because L-amino acids position their side chains to minimize steric clashes with backbone carbonyls specifically in the right-handed conformation. D-amino acids are the mirror image of L-amino acids, so the favorable backbone dihedral angles flip sign — from (−57°, −47°) to (+57°, +47°) — producing a left-handed helix. This is confirmed experimentally: synthetic D-peptides give circular dichroism spectra that are exact mirror images of natural L-peptide helices.
Q6. Can you discover additional helices in proteins?
Yes. (according to google) Beyond the common α-helix, proteins contain 3₁₀-helices (3.0 residues/turn, i→i+3, common at helix termini), π-helices (4.4 residues/turn, i→i+5, rare single-turn insertions), and the collagen triple helix. In principle, any repeating set of backbone (φ, ψ) angles that permits regular hydrogen bonding defines a helix, and the main candidates have been systematically mapped from the Ramachandran plot.
Q7. Why are most molecular helices right-handed?
The dominance of right-handed helices stems from the universal use of L-amino acids–> the lowest-energy conformation due to favorable side-chain positioning.
Once L-amino acids became dominant, all downstream molecular machinery co-evolved around that chirality. If life had been founded on D-amino acids, left-handed helices would dominate and the biology would be equally functional.
Q8. Why do β-sheets tend to aggregate? What is the driving force?
β-sheets are inherently open-ended structures: unlike α-helices where all backbone hydrogen-bond donors and acceptors are satisfied internally, β-sheet edge strands have one face of exposed N–H and C=O groups available for hydrogen bonding with additional strands. This creates a thermodynamic driving force to recruit more strands and extend the sheet. The main forces driving aggregation are backbone hydrogen bonding between exposed edges, essentially intermolecular β-sheet extension, the hydrophobic effect from burying nonpolar side chains between stacked sheets, and van der Waals contacts in the cross-β arrangement.
Q9. Why do many amyloid diseases form β-sheets? Can you use amyloid β-sheets as materials?
Proteins involved in amyloid diseases have aggregation-prone hydrophobic stretches or destabilizing mutations that lower the kinetic barrier to reaching this state, and once a nucleus forms it templates further conversion in a self-propagating manner.
Amyloid fibrils can be used as materials. They have tensile strength comparable to steel and Young’s moduli of 2–14 GPa, and they resist proteases, detergents, and heat. In bionanotechnology, amyloid fibrils serve as scaffolds for conductive nanowires, hydrogel matrices for tissue engineering and drug delivery, and membranes for heavy-metal water purification.
Q1. Briefly describe the protein you selected and why you selected it.
PIEZO1 is a homotrimeric mechanosensitive ion channel that converts physical forces — such as fluid shear stress, membrane stretch, and compressive pressure — into biochemical signals by allowing cation influx (primarily Ca²⁺) upon mechanical stimulation. Each subunit contains ~38 transmembrane helices that form a distinctive curved, propeller-like architecture with three peripheral “blades” and a central pore.
PIEZO1 is valuable because it serves as a fundamental mechanical switch for cellular programming: it governs processes including vascular development, red blood cell volume regulation, blood pressure sensing, and cell lineage determination in stem cells.
Q2. Identify the amino acid sequence of your protein.
Sequence length and composition
Homologs
Using UniProt BLAST on the human PIEZO1 sequence returns homologs across a broad range of eukaryotes — vertebrates, insects, plants, and even single-celled eukaryotes — reflecting the ancient evolutionary origin of mechanosensation. The closest homolog is PIEZO2 (human, ~42% sequence identity), which mediates light touch and proprioception. Beyond PIEZO2, orthologs of PIEZO1 are found in most metazoan genomes (mouse, zebrafish, Drosophila, C. elegans), with more distant homologs in plants (Arabidopsis) and protists. A typical BLAST search returns several hundred significant hits (E-value < 0.05), though the number depends on the database and threshold used.
Protein family
PIEZO1 belongs to the Piezo family , a eukaryote-specific family of mechanosensitive channels with no significant homology to any other known ion channel family (e.g., TRP channels, Degenerin/ENaC, or MscL/MscS bacterial mechanosensitive channels). This makes the Piezo family an evolutionarily independent solution to mechanotransduction.
Q3. Identify the structure page of your protein in RCSB.
Structure and resolution
The primary full-length structure is PDB: 5Z10 . The human PIEZO1 also has related entries (e.g., PDB 7WLT).
Other molecules in the structure
The solved structure contains:
Structure classification
In the RCSB classification, it falls under membrane proteins → ion channels → mechanosensitive channels. Its unique propeller-blade topology does not closely resemble any other structurally characterized ion channel family, making it a distinct structural class.
Q4. Visualize the structure of your protein.
Cartoon
Ribbon
Ball and Stick
PIEZO1 is overwhelmingly α-helical. Each subunit contains ~38 transmembrane helices organized into repeated structural units called “Piezo repeats” (or “transmembrane helical units”), which form the curved blades of the propeller. The central pore region includes an inner helix (TM37), outer helix (TM38), and the C-terminal extracellular domain (CED). There are virtually no β-sheets in the structure — only short loops and turns connect the helices. This extreme α-helical bias is consistent with its identity as a multi-pass transmembrane protein.
When colored by residue type:
This distribution follows the classic “positive-inside rule” — positively charged residues (Arg, Lys) are enriched on the cytoplasmic side of the membrane.
The surface of PIEZO1 reveals several notable features:
Overall, the surface is not smooth — the curved, dome-shaped architecture creates multiple grooves and pockets that are functionally relevant for lipid interaction, mechanical force transduction, and pharmacological targeting.
ESM2 was used to generate an unsupervised deep mutational scan of human PIEZO1 (UniProt Q9H5I5, 2,521 amino acids). For every position in the sequence, the model scores the log-likelihood of substituting the wild-type residue with each of the 20 amino acids. The resulting heatmap displays Model Scores across all positions (x-axis) and all possible amino acid substitutions (y-axis), where green/yellow indicates neutral or favorable substitutions and dark blue/purple indicates substitutions the model predicts to be strongly deleterious.
Conserved positions appear as dark vertical columns. Several positions show strongly negative scores across nearly all 20 substitutions, indicating that the model considers any change at those positions highly unlikely based on evolutionary sequence patterns. These columns correspond to residues that are critical for PIEZO1’s structure or function — they map primarily to the pore-lining region and the C-terminal anchor domain, where even conservative substitutions would disrupt ion conduction or mechanical gating.
The Leucine (L) row is notably bright across most positions. Mutations to leucine are generally well-tolerated, which is consistent with PIEZO1’s identity as a multi-pass transmembrane protein (~38 TM helices per subunit). Leucine is the most common residue in α-helical transmembrane domains due to its hydrophobic character and favorable helix-forming propensity, so substituting to leucine is a “safe” change at most positions.
The Glycine (G) row shows scattered deep blue spots. Positions where the wild-type is glycine tend to show dark columns across other substitutions. Glycines in transmembrane helices are critical for helix packing and flexibility — they allow tight inter-helix contacts that bulkier residues would sterically prevent. Mutating these glycines is therefore strongly disfavored.
A specific example: One of the most prominent dark vertical bands appears in the region corresponding to the inner pore helix of PIEZO1. Conserved charged residues in this region (e.g., glutamate or arginine residues lining the pore) score very negatively when mutated to hydrophobic residues like leucine, isoleucine, or valine. This is biologically expected — charged residues in the pore domain are essential for cation selectivity and gating, and replacing them with hydrophobic side chains would destroy channel function.
15,177 structurally classified protein domains from the SCOPe/ASTRAL database were embedded using ESM2-8M (hidden dimension = 320) into 320-dimensional vectors. t-SNE then projected these into 3D for visualization. The color scale represents TSNE3 (yellow = high, purple = low), providing visual depth. Despite using the smallest ESM2 model, the projection recovers meaningful structural groupings, demonstrating that protein language models encode structural information implicitly from sequence alone.
I took three corresponding coordinates for analysis:
Upper yellow region (high TSNE3) — β-sheet-rich proteins.
Dense central orange region (intermediate TSNE3) — common α/β folds.
Lower purple region (low TSNE3) — unusual/transmembrane proteins.
PIEZO1 would be expected to sit in the purple periphery or as an isolated outlier given that
It is an extremely large multi-pass transmembrane protein, so its sequence composition is heavily biased toward hydrophobic residues. This transmembrane character would push it away from the soluble-protein-dominated central core, similar to how LASS6 sits in the purple region.
PIEZO1 has no sequence homology to any other known ion channel family. Its “Piezo repeat” domains and propeller-blade architecture are structurally unique. ESM2 would therefore embed it far from other channel proteins.
The only protein expected to sit nearby is PIEZO2 (~42% sequence identity), the sole close homolog. If PIEZO2 is absent from the dataset, PIEZO1 would sit alone — reflecting the evolutionary isolation of the Piezo family as a structurally novel, independent solution to mechanosensation.
Human SOD1 Sequence (UniProt)
A4V Mutant Sequence
Position 4: Ala → Val (A4V)
| Position | Wild-type | Mutant |
|---|---|---|
| 4 | A (Ala) | V (Val) |
PepMLM-Generated Peptides
4 candidate binders generated against the A4V mutant sequence + the known reference peptide.
Lower perplexity scores indicate sequences more confidently predicted by the model.
| # | Sequence | Perplexity | Note |
|---|---|---|---|
| PepMLM 1 | WHYPAAAAAWKK | 8.611 | — |
| PepMLM 2 | WRSPAVAAAHKE | 7.866 | Lowest perplexity |
| PepMLM 3 | WRYPAVALEWKK | 16.562 | Highest perplexity |
| PepMLM 4 | WHSYVVGARWWK | 13.338 | — |
| Known | FLYRWLPSRRGG | — | Reference binder |
Note on Perplexity: In PepMLM, perplexity reflects how confidently the masked language model predicts each residue in context. Lower perplexity suggests the sequence is more consistent with the model’s learned distribution of binders; however, higher perplexity sequences may still yield productive binding if their physicochemical and structural properties are favourable.
 For the sake of my OCD or else with only 5 pics will look ugly |  Known Peptide ipTM = 0.36 |  Peptide 1 ipTM = 0.27 |
 Peptide 2 ipTM = 0.40 |  Peptide 3 ipTM = 0.19 |  Peptide 4 ipTM = 0.39 |
ipTM (interface predicted TM-score) measures predicted interface accuracy.
Values range from 0 to 1 — higher is better. Scores ≥ 0.5 generally indicate confident predictions.
| Structure | ipTM | Near A4V / N-term? | β-barrel engagement | Surface character |
|---|---|---|---|---|
| Known (Reference) | 0.36 | Yes | Lateral strand edge | Surface-bound, extended |
| PepMLM Peptide 1 | 0.27 | No | Minimal | Surface, poorly engaged |
| PepMLM Peptide 2 | 0.40 | Partial — dimer face | Lateral interface cleft | Surface docked |
| PepMLM Peptide 3 | 0.19 | No | None | Peripheral, non-specific |
| PepMLM Peptide 4 | 0.39 | Distal (C-term base) | Bottom loop region | Surface-bound |
ipTM (interface predicted TM-score) measures predicted interface accuracy.
Values range from 0–1; scores ≥ 0.5 generally indicate confident predictions. All values here are modest, consistent with flexible peptide–protein interfaces typical in AlphaFold-Multimer assessments.
swipe left for more
PepMLM 1WHYPAAAAAWKK | PepMLM 2WRSPAVAAAHKE | PepMLM 3WRYPAVALEWKK | PepMLM 4WHSYVVGARWWK | Known (Reference)FLYRWLPSRRGG | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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All peptides are predicted soluble and non-hemolytic. Binding affinity (pKd/pKi): higher = stronger predicted affinity. Negative GRAVY scores reflect hydrophilic character across all sequences. Across the five peptides, there is no clear correlation between ipTM and predicted binding affinity.
The peptide I selected is PepMLM Peptide 2 (WRYPAVALEWKK). While its predicted affinity is modest, it has the highest ipTM, adopts stable α-helical secondary structure upon docking — a hallmark of productive peptide–protein interfaces — and engages the lateral cleft of the β-barrel at precisely the region destabilised by A4V. It is the only candidate where the structural, physicochemical, and site-specificity evidence converge.
The MS2 bacteriophage lysis protein (L-protein) is a 74 amino acid protein responsible for killing E. coli host cells by perforating the bacterial membrane. A critical vulnerability of this system is that a single point mutation in the host chaperone protein DnaJ can prevent the lysis protein from functioning, allowing E. coli to acquire resistance to MS2.
The L-protein has two structurally and functionally distinct regions:
At least 2 in the transmembrane region and at least 2 in the soluble region.
Option 1: Mutagenesis
Running the ESM-2 protein language model
(facebook/esm2_t6_8M_UR50D) on the full wild-type L-protein sequence:
The model scores every possible single amino acid substitution at every position using a Log Likelihood Ratio (LLR):
The notebook was first run with a focused query on the transmembrane region (positions 38–60), producing the following top-scored mutations:
Three positions dominate the top scores: 50, 39, and 45. The model strongly favors leucine (L) substitutions at positions 50 and 39, and also at position 45. This is the first signal pointing toward K50L, Y39L, and A45(L or P) as strong TM candidates. Notably, multiple substitutions at position 50 rank highly (L, I, F, V, S, A),suggesting this position is generally flexible — but leucine scores the highest of all.
The notebook was then run on the full protein sequence to get a global ranking across all 74 positions:
The top 10 globally are dominated by three positions: 50 (K→L), 29 (C→R/S/Q/P/L), and 39 (Y→L). This globally confirms what the TM scan already suggested, and additionally highlights C29 in the soluble region as a computationally interesting mutation site.
The full ranking also produced a second merged output combining both score datasets:
The computational shortlist from the ESM model was:
The L-Protein Mutants CSV was uploaded into the notebook, which displayed the first rows of the experimental dataset:
This dataset contains experimentally measured lysis outcomes (0 = no lysis, 1 = lysis) for mutations that have already been tested in the lab. Cross-referencing this with the ESM scores revealed which computational predictions align with real biology.
Merging both datasets exposed a critical finding: the ESM model only partially agrees with experimental lysis outcomes.
| Mutation | ESM Score | Lysis (Lab) | Agreement? |
|---|---|---|---|
| P13L | +0.10 | Yes | ✅ |
| S15A | +0.04 | Yes | ✅ |
| K23E | +0.18 | Yes | ✅ |
| E25G | +0.45 | Yes | ✅ |
| A45P | +0.04 | Yes | ✅ |
| I46F | -0.10 | Yes | ❌ |
| R18G | -0.85 | Yes | ❌ |
| R31I | -0.93 | Yes | ❌ |
| L44P | -1.59 | Yes | ❌ |
| R20W | -2.18 | Yes | ❌ |
The disagreements (especially R18G, I46F, L44P) suggest that the ESM model scores general protein structural fitness (the ability to fold into a stable, functional, three-dimensional shape (conformation) that is energeticaly favorable), not functional lysis activity (the process of breaking open cell membranes).
Mutations that disrupt DnaJ binding (like R18G) are penalised by the model because the arginine is evolutionarily conserved — but conserved because it binds DnaJ.
This insight shaped the final selection strategy:
Use ESM scores to identify novel untested candidates with high computational confidence, and use experimental data to validate or override those scores based on known biology.
With all evidence assembled, five mutations were selected spanning both protein regions:
P13L — Position 13, Proline → Leucine
S15A — Position 15, Serine → Alanine
Y39L — Position 39, Tyrosine → Leucine
A45P — Position 45, Alanine → Proline
K50L — Position 50, Lysine → Leucine
| # | Mutation | Region | ESM Score | Lysis | Protein | Rationale |
|---|---|---|---|---|---|---|
| 1 | P13L | Soluble | +0.10 | ✅ | ✅ | Removes proline kink; enables DnaJ-independent folding |
| 2 | S15A | Soluble | +0.04 | ✅ | ✅ | Removes DnaJ contact site within NRRRP motif |
| 3 | Y39L | TM | +2.24 | ❓ | — | Sharpens TM helix entry; 3rd highest ESM score globally |
| 4 | A45P | TM | +0.04 | ✅ | ✅ | Proline kink promotes pore-forming conformation |
| 5 | K50L | TM | +2.56 | ❓ | — | Highest ESM score in protein; removes charged residue from TM core |
AI Prompt used in this section for mutation selection: Given the provided mutations, could you explain the rationale behind each and why would each serve as potentially candidates?
| Model | My Build | |
|---|---|---|
| Backbone | pUC-SpecR v1 included | Not added |
| Duration | 24 hours | 72 hours |
| Oscillation | Clear phase separation between curves | Sustained but three curves blur together |
| RNAP flux pattern | Stepped bars (1.57 / 0.65 / 2.87) | Similar stepped pattern (3.1 / 1.25 / 0.65) |
| Noise bands | Moderate spread | Wider spread |
pTetR → LacI → represses pLacI
pLacI → TetR → represses pTetRTwo cassettes mutually silence each other. The system snaps to one of two stable states — either LacI is high and TetR is low, or vice versa. Acts as a bistable memory switch: once flipped, it holds its state.
No → bistable lock
Expect: one protein high, one flat zero
pAmtR → AmtR ⟐ pPsrA → PsrA
Both repress pAmeR → LambdaCITwo input repressors each independently silence the output promoter pAmeR. LambdaCI is only produced when neither AmtR nor PsrA is present — a true NOR logic gate.
A=0, B=0 → Output ON
A=1 or B=1 → Output OFF
pAmtR → AmtR → represses pPsrA
pPsrA → PsrA → represses pAmeR → LambdaCIA two-stage repression cascade. When the upstream signal (pAmtR) is active, it silences the chain, keeping output OFF. Remove the signal → repression lifts through both stages → LambdaCI output turns ON.
Signal present → Output OFF
Signal removed → Output ON
| Toggle Switch | NOR Gate | Inducible Reporter | |
|---|---|---|---|
| Cassettes | 2 | 3 | 3 |
| Logic | Bistable memory | NOR (A=0 AND B=0) | Signal-gated ON/OFF |
| Output when inputs silent | Locked state | ON | ON |
| Key behaviour | Snap to one stable state | Universal logic gate | Controlled expression |
| Ideal sim duration | 24h | 24h | 48h |
Describe a useful application for an IANN; include a detailed description of input/output behavior, as well as any limitations an IANN might face to achieve your goal.
Below is a diagram depicting an intracellular single-layer perceptron where the X1 input is DNA encoding for the Csy4 endoribonuclease and the X2 input is DNA encoding for a fluorescent protein output whose mRNA is regulated by Csy4. Tx: transcription; Tl: translation.
Draw a diagram for an intracellular multilayer perceptron where layer 1 outputs an endoribonuclease that regulates a fluorescent protein output in layer 2.
Layer 2 is an INHIBIT gate: X3 is the excitatory input (fluorescent protein mRNA), RNase2 from Layer 1 is the inhibitory input, and fluorescence only appears when X3 is present and Layer 1 has successfully suppressed RNase2 via RNase1.
An intracellular two-layer perceptron in which Layer 1 produces an endoribonuclease that post-transcriptionally regulates the Layer 2 fluorescent protein output.
Cell-free protein synthesis (CFPS) offers several key advantages over conventional cell-based (in vivo) expression systems, primarily in terms of flexibility and experimental control.
Unlike living cells, cell-free systems are open reaction platforms. Researchers can directly manipulate the reaction environment by adding or withholding any component — cofactors, chaperones, non-natural amino acids, labeling agents, or inhibitors — at any point during the reaction. There are no cell membranes limiting access to the transcription/translation machinery, and no cellular growth constraints.
CFPS allows fine-tuned control over:
This level of control is virtually impossible in a living cell without massive genetic engineering.
| Case | Reason |
|---|---|
| Toxic proteins (e.g., pore-forming toxins, antimicrobial peptides, viral proteases) | These proteins kill or severely harm host cells during in vivo production, making yields negligible. In CFPS, no living cell is present to be harmed, allowing unhindered synthesis. |
| Incorporation of non-canonical amino acids (ncAAs) | CFPS allows direct supplementation of ncAAs (e.g., for click-chemistry probes, photo-crosslinkers, or fluorescent tags) and co-addition of orthogonal aminoacyl-tRNA synthetase/tRNA pairs without the complexity of genetically reprogramming a living organism. |
A typical cell-free expression system contains the following core components:
The biological “engine” of the system. Prepared by lysing cells (commonly E. coli, wheat germ, rabbit reticulocytes, insect cells, or CHO cells) and removing cell debris and genomic DNA by centrifugation. It provides:
Provides the genetic instructions for the target protein. Can be supplied as:
All 20 standard amino acids (plus any desired ncAAs) must be supplied in sufficient concentrations as building blocks for the polypeptide chain.
Provides and replenishes ATP and GTP, which are consumed rapidly during translation. Common solutions include phosphocreatine/creatine kinase, phosphoenolpyruvate (PEP)/pyruvate kinase, or glucose-6-phosphate systems (see Q3).
ATP, GTP, CTP, and UTP are required for RNA synthesis (transcription) and ribosome function. ATP and GTP are particularly critical.
A buffered solution (often HEPES or Tris) at physiological pH (~7.5), with optimized concentrations of Mg²⁺ (crucial for ribosome function), K⁺, and other ions.
Depending on the application, these may include:
Protein synthesis is among the most energetically expensive cellular processes. Each peptide bond formation consumes at least 4 ATP equivalents (2 ATP for aminoacyl-tRNA charging, 1 GTP for elongation factor Tu, 1 GTP for translocation by EF-G). Additionally, transcription, mRNA capping, and molecular chaperone activity all consume ATP/GTP. Since cell-free systems contain a finite pool of nucleotides and no mitochondria or oxidative phosphorylation, ATP is depleted within minutes without an exogenous regeneration system — halting protein synthesis entirely.
The most widely used approach is the phosphocreatine / creatine kinase (PCr/CK) system:
Phosphocreatine + ADP → Creatine + ATP (catalyzed by creatine kinase)
Experimental implementation:
Alternative systems include phosphoenolpyruvate (PEP)/pyruvate kinase, glucose-6-phosphate, or the more advanced oxidative phosphorylation-coupled systems using maltose/glucose as substrates for sustained multi-hour synthesis.
| Feature | Prokaryotic (e.g., E. coli) | Eukaryotic (e.g., Wheat Germ, CHO, Rabbit Reticulocyte) |
|---|---|---|
| Transcription/Translation | Coupled (simultaneous) | Uncoupled (spatially and temporally separated) |
| Post-translational modifications (PTMs) | Very limited (no glycosylation, limited phosphorylation) | Rich PTM machinery: glycosylation, phosphorylation, ubiquitination, etc. |
| Yield | Generally higher (optimized for batch production) | Moderate; reticulocyte lysate is very sensitive |
| Cost and complexity | Low cost, simple preparation | Higher cost, more complex extract preparation |
| Protein folding support | Basic chaperones (DnaK, GroEL/ES) | Complex folding machinery (Hsp70, Hsp90, PDI, calnexin) |
| Template requirement | Promoter-dependent (T7 preferred) | Requires 5’ cap and poly-A tail (or IRES) for mRNA |
Prokaryotic system — Choice: T7 RNA Polymerase
T7 RNA polymerase is a relatively small (~99 kDa), single-subunit bacterial enzyme with no requirement for glycosylation or complex eukaryotic PTMs. E. coli-based CFPS yields are typically high for such soluble bacterial proteins. It can be produced rapidly in a batch E. coli lysate system and used directly in downstream cell-free reactions — making prokaryotic CFPS an efficient, cost-effective choice.
Eukaryotic system — Choice: Erythropoietin (EPO)
EPO is a 34 kDa glycoprotein hormone where glycosylation accounts for ~40% of its molecular weight and is essential for its in vivo half-life, solubility, and biological activity. Prokaryotic systems cannot perform N- and O-linked glycosylation. A CHO-based or insect cell-based CFPS system provides the necessary glycosylation machinery, disulfide bond isomerases (for its two disulfide bridges), and signal peptide processing — making eukaryotic CFPS the only rational choice for functionally relevant EPO production.
Membrane proteins (MPs) represent >30% of all encoded proteins but are notoriously difficult to produce because:
Three main strategies exist:
| Strategy | Principle | Best For |
|---|---|---|
| Detergent-based CFPS | Add mild detergents (e.g., DDM, Brij-35, digitonin) to solubilize TM domains during synthesis | Initial screening; GPCRs |
| Lipid nanodisc co-translation | Add pre-formed lipid nanodiscs + scaffold proteins (MSP1D1) to capture the MP co-translationally | Functional assays; structural studies |
| Liposome/proteoliposome insertion | Supply liposomes; MPs insert directly during synthesis | Reconstitution for transport/channel assays |
Recommended approach: Start with detergent screening (DDM at 0.1–1% w/v) combined with lipid nanodisc supplementation for functional studies.
Screen a matrix of:
Mechanism: Without adequate energy regeneration, translation halts prematurely. This is the most common cause of low yield.
Troubleshooting strategy:
Mechanism: Cell extracts contain ribonucleases (RNases) that degrade mRNA rapidly. If mRNA is short-lived, ribosomes have no template to translate, drastically reducing yield.
Troubleshooting strategy:
Mechanism: Even if the protein is synthesized in adequate amounts, it may (a) misfold and aggregate into insoluble inclusion body-like structures within the reaction, or (b) be degraded by proteases remaining in the extract.
Troubleshooting strategy:
Answers compiled from core principles of cell-free expression biology. Key references: Gregorio Georgiou & Lydia Kirsanova CFPS reviews; Pardee et al. (2016) Cell; Silverman et al. (2019) Nature Protocols.
Design an example of a useful synthetic minimal cell as follows:
Freeze-dried cell-free reactions have great potential in space, where resources are constrained. As described in my talk, the Genes in Space competition challenges students to consider how biotechnology, including cell-free reactions, can be used to solve biological problems encountered in space. While the competition is limited to only high school students, your assignment will be to develop your own mock Genes in Space proposal to practice thinking about biotech applications in space!
For this particular assignment, your proposal is required to incorporate the BioBits® cell-free protein expression system, but you may also use the other tools in the Genes in Space toolkit (the miniPCR® thermal cycler and the P51 Molecular Fluorescence Viewer). For more inspiration, check out https://www.genesinspace.org/ .
We will analyze an eGFP standard on a Waters Xevo G3 QTof MS system to determine the molecular weight of intact eGFP and observe its charge state distribution in the native and denatured (unfolded) states. The conditions for LC-MS analysis of intact protein cause it to unfold and be detected in its denatured form (due to the solvents and pH used for analysis).
eGFP Sequence:
Sequence Analysis:
Calculated MW: Using the ExPASy ProtParam tool (https://web.expasy.org/compute_pi/) with the provided sequence and accounting for chromophore maturation:
Theoretical MW ≈ 27,838 Da (may vary slightly depending on calculator and modifications considered)
Calculate the molecular weight of the eGFP using the adjacent charge state approach described in the recitation. Select two charge states from the intact LC-MS data (Figure 1) and:
Determine z for each adjacent pair of peaks (n, n+1) using the formula
Determine the MW of the protein using the relationship between m/z, MW, and z
Calculate the accuracy of the measurement using the deconvoluted MW from 2.2 and the predicted weight of the protein from 2.1
From Figure 1, I’ll select two well-resolved peaks:
Using the formula:
Substituting values:
Therefore, the charge state of the first peak is z = +19
The adjacent peak (m/z = 1037.4423) has charge state z = +20
Using the relationship: MW = z × (m/z - mass of proton)
For the z = +19 peak:
Experimental MW ≈ 27,985 Da
Formula:
Calculation:
Results:
No, you cannot directly observe the charge state from the zoomed-in region alone. The zoomed region shows isotope peaks within a single charge state envelope, not separate charge states. The closely spaced peaks (spacing ~0.5-0.7 m/z) represent the natural isotopic distribution of carbon-13, nitrogen-15, and other heavy isotopes in the protein.
Explanation:
What the zoom shows: The fine structure visible in the zoomed region represents isotopic peaks from the same charge state. Each peak differs by approximately the mass of one neutron (1 Da) divided by the charge state.
Isotopic spacing vs. charge state spacing:
To determine charge state from this region, you would need to:
This is a lovely piece of art created by HTGAA community, I love the bio elements and the niche reference to DNA yay.
I received the link but forgot to contribute. But that wasn’t intentional because maybe someday I will return as a TA for this course.
Although with my pathetic knowledge in bio I will probably get fired on the spot.
What I really liked about the project is the creative use of color palette and the layout of words, and also the fact that I was able to see the quantitative recollection of people’s contribution.
E. coli Lysate
| Component | Role |
|---|---|
| BL21(DE3) Star Lysate (with T7 RNA Polymerase) | Provides the complete transcription and translation machinery — ribosomes, tRNAs, aminoacyl-tRNA synthetases, initiation/elongation factors, and chaperones. The DE3 genomic insertion encodes T7 RNA Polymerase, enabling high-efficiency transcription from T7 promoter-driven DNA templates. |
Salts and Buffer
| Component | Role |
|---|---|
| Potassium Glutamate | Primary K⁺ source for ribosome function and osmotic balance; glutamate is a preferred counterion over Cl⁻, which is inhibitory to translation |
| HEPES-KOH pH 7.5 | Maintains physiological pH to stabilize enzymatic activity throughout the reaction |
| Magnesium Glutamate | Supplies Mg²⁺, essential for ribosome assembly, RNA structural integrity, and phosphotransfer reactions |
| Potassium phosphate (monobasic/dibasic) | Provides a phosphate buffer reserve and inorganic phosphate for nucleotide phosphorylation reactions |
Energy and Nucleotide System
| Component | Role |
|---|---|
| Glucose | Primary carbon and energy source; feeds glycolysis to drive ATP regeneration and downstream metabolism |
| Ribose | Enters the pentose phosphate pathway (PPP) to generate PRPP for nucleotide salvage and NADPH for redox balance |
| AMP, CMP, GMP, UMP | Nucleoside monophosphates (NMPs) serve as transcription precursors, phosphorylated in situ to NTPs by endogenous kinases |
| Guanine | Free nucleobase salvaged via HGPRT to produce GMP, supplementing the GTP pool for transcription (see Bonus) |
Translation Mix (Amino Acids)
| Component | Role |
|---|---|
| 17 Amino Acid Mix | Provides the bulk substrates required for ribosomal translation and polypeptide elongation |
| Tyrosine | Added separately due to its poor aqueous solubility at neutral pH; typically prepared as a pH 12 suspension |
| Cysteine | Added separately due to oxidation sensitivity and reactivity; prone to disulfide formation in mixed stock solutions |
Additives
| Component | Role |
|---|---|
| Nicotinamide | NAD⁺ precursor (vitamin B3) that sustains the redox cofactor pool required for glycolysis and energy metabolism; also inhibits NAD⁺-consuming enzymes (e.g., sirtuins, PARPs) that would otherwise deplete the pool |
Backfill
| Component | Role |
|---|---|
| Nuclease-Free Water | Brings the reaction to final volume without introducing RNases that would degrade mRNA templates or tRNAs |
The 1-hour PEP-NTP system supplies NTPs directly (ATP 1.5 mM, GTP 1.5 mM, CTP/UTP 875 µM each) alongside phosphoenolpyruvate (PEP-Mono, 17.5 mM) and Maltodextrin as fast-acting energy donors — this provides immediate substrates for transcription and translation but is short-lived because PEP is rapidly exhausted and accumulating inorganic phosphate (Pᵢ) inhibits the reaction.
The 20-hour NMP-Ribose-Glucose system instead supplies nucleoside monophosphates (AMP, CMP, UMP) and substitutes GMP entirely with free Guanine (200 µM), relying on endogenous cellular enzymes to phosphorylate NMPs to NTPs using metabolic energy regenerated from Ribose (77.4 mM) and Glucose (6.9 mM), avoiding rapid Pᵢ accumulation and sustains productive synthesis far longer. The PEP-NTP formulation also includes a richer additive cocktail (Spermidine, DMSO, cAMP, NAD, Folinic Acid) to maximize short-burst translation efficiency, whereas the NMP-Ribose system is simplified to Nicotinamide alone and compensates with higher amino acid concentrations (~4.1 mM vs. 2.5 mM) to support extended protein production.
Guanine is converted to GMP via the purine salvage pathway:
PRPP (5-phosphoribosyl-1-pyrophosphate) is generated from ribose-5-phosphate, a product of the pentose phosphate pathway fed by ribose. The GMP produced is then sequentially phosphorylated by endogenous kinases:
GTP is the actual substrate incorporated by T7 RNAP during transcription. Using free Guanine rather than GMP is both cost-effective and avoids the chemical instability of pre-formed GTP in the reaction mix — the lysate’s endogenous HGPRT activity handles the conversion efficiently.
By the end of this lab, you will be able to:
A micropipette (commonly just called a “pipette” in the lab) is a precision instrument used to measure and transfer very small volumes of liquid — typically between 0.1 µL and 1000 µL (1 mL). Accurate pipetting is one of the most fundamental skills in biology, chemistry, and biomedical research. Even small errors in volume measurement can ruin an experiment, skew results, or waste expensive reagents.
Volumes in the lab are measured in:
| Unit | Abbreviation | Equivalent |
|---|---|---|
| Milliliter | mL | 1/1000 of a liter |
| Microliter | µL | 1/1000 of a mL = 1/1,000,000 of a liter |
| Nanoliter | nL | 1/1000 of a µL (specialized instruments only) |
💡 Quick reference: 1 mL = 1,000 µL. A typical raindrop is ~50 µL. A grain of salt is ~60 nL.
There are three standard micropipettes you will use in this course. Each is color-coded and designed for a specific volume range. Never exceed the maximum or go below the minimum volume — this damages the internal piston mechanism.
| Pipette Name | Common Color | Volume Range | Typical Use |
|---|---|---|---|
| P20 | Yellow | 0.5 µL – 20 µL | Small volumes: enzymes, DNA samples |
| P200 | Yellow | 20 µL – 200 µL | Medium volumes: PCR reactions, buffers |
| P1000 | Blue | 200 µL – 1000 µL | Large volumes: media, stock solutions |
⚠️ Rule: Always choose the pipette whose range most closely fits your target volume. Using a P1000 to measure 5 µL will be wildly inaccurate.
The volume display has three digits read from top to bottom. How you interpret those digits depends on which pipette you are using.
| Display | Volume |
|---|---|
| 2 / 0 / 0 | 20.0 µL (maximum) |
| 1 / 0 / 0 | 10.0 µL |
| 0 / 5 / 0 | 5.0 µL |
| 0 / 1 / 0 | 1.0 µL |
| Display | Volume |
|---|---|
| 2 / 0 / 0 | 200 µL (maximum) |
| 1 / 0 / 0 | 100 µL |
| 0 / 5 / 0 | 50 µL |
| 0 / 2 / 5 | 25 µL |
| Display | Volume |
|---|---|
| 1 / 0 / 0 | 1000 µL = 1.0 mL (maximum) |
| 0 / 5 / 0 | 500 µL = 0.5 mL |
| 0 / 2 / 5 | 250 µL |
| 0 / 2 / 0 | 200 µL (minimum) |
Turn the volume adjustment dial to your desired volume.
Press the tip cone firmly into a fresh pipette tip in the tip box. Give it a slight twist or firm push to create an airtight seal. Never touch the tip with your fingers after attachment — this contaminates your sample.
Before aspirating your actual sample, aspirate and expel the liquid 2–3 times to wet the inner walls of the tip. This reduces evaporation error, especially important for small volumes (<10 µL).
⚠️ Releasing too fast creates bubbles and inaccurate volumes. Speed matters!
Hold the tip up to the light. If you see a bubble, expel the liquid and re-aspirate. Bubbles displace liquid and reduce your actual volume.
Press the tip ejector button firmly over a waste container. Never remove used tips by hand — tips may be contaminated with biological or chemical material.
| Mistake | What Goes Wrong | How to Fix It |
|---|---|---|
| Releasing the plunger too fast | Creates air bubbles; aspirates incorrect volume | Always release slowly and steadily |
| Angling the pipette too far | Liquid runs back into the barrel, damaging the piston | Keep pipette vertical (±20°) |
| Inserting tip too deep | Liquid coats the outside of the tip and is carried over | Insert only 2–6 mm depending on pipette |
| Not pre-wetting the tip | First aspiration is inaccurate (surface tension effect) | Aspirate and expel 2–3 times before sampling |
| Pushing to second stop during aspiration | Aspirates too much volume / introduces air | Only push to first stop when aspirating |
| Reusing tips between samples | Cross-contamination of reagents | Change tip between every new sample |
| Setting volume outside the range | Inaccurate measurement; piston damage | Always choose the right pipette for the volume |
| Touching the tip with fingers | Introduces skin oils, DNA, and microbes | Handle only the pipette body; use tip boxes |
These two terms are distinct and both matter in pipetting:
A pipette can be precise but inaccurate if it is mis-calibrated. Always check calibration before critical experiments.
You can test your pipetting accuracy by weighing water. Since water has a density of 1.00 g/mL, 100 µL of water should weigh exactly 0.100 g.
| Trial | Expected Mass (g) | Measured Mass (g) | Error (g) | % Error |
|---|---|---|---|---|
| 1 | 0.100 | |||
| 2 | 0.100 | |||
| 3 | 0.100 | |||
| 4 | 0.100 | |||
| 5 | 0.100 | |||
| Mean | 0.100 |
% Error formula:
✅ Good pipetting: % error < 2% for P200 at 100 µL
⚠️ Acceptable: % error 2–5%
❌ Needs improvement: % error > 5%
Image 1 (Mid-run photograph): The photograph taken during electrophoresis shows the gel submerged in TAE within the gel box. Two colored dye fronts are faintly visible — a blue band and a dark purple band — but they appear localized to only one or two lanes. The majority of the gel appears empty, with no visible dye migration in the other wells. This is already an early indicator that most wells were either not loaded successfully or contained insufficient DNA.
Image 2 (GeneSnap image): The final imaging result is largely dark. Only a single lane shows any detectable fluorescence — a faint, somewhat smeared signal concentrated in what appears to be one lane, with no clearly resolved discrete bands. The remaining lanes are entirely blank. This represents an unsuccessful gel run in terms of the intended gel art pattern.
Analysis of What Went Wrong Based on the observations made during lab sessions and the photographic evidence, several compounding factors likely contributed to the result:
This figure represents the end-to-end automated pipeline: from DNA library preparation through robotic CFPS assembly, incubation, fluorescence readout, and computational analysis.
Each row represents a unique biosensor construct variant; each column a different inducer concentration. Construct C5 shows the steepest dose-response (ideal switch-like behavior) with minimal background — identified as the top hit for downstream validation.
References: Hossain et al. (2020) ACS Synth. Biol.; Pardee et al. (2014) Cell; Opentrons Protocol API v2 Documentation.
In this experiment we engineer color variants of the purple Acropora millepora chromoprotein (amilCP) by introducing targeted mutations at the chromophore (CP) site: cagTGTCAGtac. Substituting the TGTCAG hexamer with variant codons shifts the expressed color to orange, pink, magenta, or blue, as described by Liljeruhm et al. (2018).
Part 1 covers the preparation of two PCR fragments — a Backbone fragment and a Color insert fragment — which will be joined by Gibson Assembly and transformed into E. coli in Part 2.
Progress: PCR Setup → Thermal Cycling → DpnI Digest → Purification → Gel Electrophoresis ✓ → Gibson Assembly) → Transformation)
Time estimate: ~1.5 hours total
Two parallel PCR reactions were prepared on ice using the mUAV plasmid as template. The Backbone reaction amplifies the vector (ori + CmR + promoter + RBS), while the Color reaction amplifies the chromophore region with a mutant forward primer that introduces the desired codon substitution at the CP site.
Backbone DNA Fragment (Primers: Backbone Fwd + Backbone Rev)
| Reagent | Stock Conc. | Desired Conc. | Volume (µL) |
|---|---|---|---|
| Template mUAV Plasmid | 38.5 ng/µL | 20 ng/µL | 0.8 |
| Backbone Forward Primer | 5 µM | 0.5 µM | 2.5 |
| Backbone Reverse Primer | 5 µM | 0.5 µM | 2.5 |
| Phusion HF PCR Mix | 2× | 1× | 12.5 |
| Nuclease-free water | — | — | 6.8 |
| Total Volume | 25.0 |
Color DNA Fragment (Primers: Color Fwd + Color Rev)
| Reagent | Stock Conc. | Desired Conc. | Volume (µL) |
|---|---|---|---|
| Template mUAV Plasmid | 38.5 ng/µL | 20 ng/µL | 0.8 |
| Color Forward Primer | 5 µM | 0.5 µM | 2.5 |
| Color Reverse Primer | 5 µM | 0.5 µM | 2.5 |
| Phusion HF PCR Mix | 2× | 1× | 12.5 |
| Nuclease-free water | — | — | 6.8 |
| Total Volume | 25.0 |
Backbone Fragment (BB_PCR) — run on Bio-Rad T100, 25 µL volume
Color Insert Fragment
BB_PCR program (57°C anneal, 26 cycles, 25 µL volume).The Color forward primer carries an intentional mismatch in the 6-bp chromophore region (e.g. TGTCAG → GTTGGA for orange). Because the mismatch sits in the 5′ overhang, Phusion polymerase still extends efficiently from the matched 3′ binding region. The mutation is thus incorporated into every PCR copy and all downstream clones.
⏱ Time estimate: 45 min at 37°C
After PCR, 1 µL of DpnI was added directly to each 25 µL reaction and incubated at 37°C for 30–60 minutes. DpnI recognises methylated 5′-Gm6ATC-3′ sequences present on E. coli-propagated plasmid template, but absent from unmethylated PCR products. The enzyme therefore selectively digests the parental template while leaving new amplicons intact.
Residual un-digested template will generate wildtype (purple) background colonies that compete with and obscure your color-mutant transformants.
⏱ Time estimate: 30 min
PCR products were purified using the Zymo DNA Clean & Concentrator kit (silica-column adsorption) to remove primers, dNTPs, polymerase, and buffer salts before Gibson Assembly.
⏱ Time estimate: ~15 min at 100 V
Purified fragments were run on a 1% agarose E-Gel EX (Invitrogen) to confirm fragment sizes. Each lane received 3 µL sample + 3.3 µL 6× Loading Dye. DNA ladder loaded in lane M (leftmost).
| Lane | Observation | Interpretation |
|---|---|---|
| M | Ladder bands across full range | Reference marker |
| 1 | Faint band ~400–500 bp | Likely primer-dimer or low-yield non-specific product |
| 2 | Faint band, similar to lane 1 | Same as above; low amplification |
| 3 | Bright band ~600–750 bp | Color insert fragment (~700 bp) — strong, clean yield |
| 4 | Faint lower band | Minor non-specific; likely negligible for downstream steps |
| 5 | Bright band ~2.7–2.9 kb | Backbone fragment (~2800 bp) — strong, clean yield |
| 6–10 | Empty | — |
Expected fragment sizes:
Lanes 3 and 5 show bright, clean bands at the expected sizes for Color insert and Backbone respectively. Faint bands in lanes 1, 2, and 4 represent minor non-specific products that will be diluted out during Gibson Assembly and will not affect the outcome. Both fragments are confirmed — proceed to Gibson Assembly.
⏱ Incubation: 72 hours at 37°C | Selection: LB-Agar + Chloramphenicol 25 µg/mL
All colonies across every plate — regardless of intended color variant (blue, pink, light pink) — express a uniform blue-purple color consistent with wildtype amilCP. The intended color shifts to pink or blue did not appear. One notable exception is the red-circled colony in the final plate (Fig. 5h), which is transparent/colorless.
Gibson Assembly outcome is highly sensitive to the molar ratio of insert to backbone, not just the volumes used. The protocol specifies 0.5 µL backbone and 1.0 µL insert — but those volumes assume both fragments are at exactly the stated concentrations after purification.
When backbone is in excess, the probability of the two backbone ends annealing to each other increases sharply — rather than each end finding the insert:
Because the ratio imbalance originates in the Gibson reaction before transformation, it would affect all three volume groups (2µL, 4µL, 7µL) equally — explaining the consistency of the wildtype purple outcome across all plates.
Partially succeeded, as this is consistent with a scenario where the backbone reassembled without the color insert — the Gibson exonuclease chewed back both ends of the backbone, they annealed to each other rather than to the insert, and ligase sealed the nick. The result is a backbone-only plasmid that carries CmR but lacks the amilCP CDS entirely, hence no color.
Alternatively, the insert was incorporated but with a frameshift or premature stop codon introduced during the Gibson join, knocking out chromoprotein expression without replacing it with a new color.
Either way, this colony is evidence that the Gibson Assembly chemistry was active and processing DNA correctly. The colorless result is not a failure — it is a partial success where the backbone was modified but the color swap did not complete as intended.
the wildtype blue-purple across all other colonies most likely reflects surviving template from incomplete DpnI digestion, while the single transparent colony shows that at least one genuine assembly event occurred.
For the neuromorphic circuit, our group aimed to design a “L” shaped heatmap. We added two bias corresponding to X1 and X2 ERNs.
Looked perfect
I think we might’ve submitted the wrong file ahahaha, so the final output only displayed the bottom part of the “L”
Each dot in these scatterplots represents a single human cell. The color shows the level of output (mNeonGreen) as a function of X1 and X2 and, optionally, varying levels of bias.
The amino acid sequences are shown in the HTGAA Cell-Free Benchling folder.
sfGFP: primary advantage is robust folding kinetics; it is engineered to fold correctly even when fused to insoluble proteins, making it highly resistant to aggregation in the crowded environment of a cell-free extract.
mRFP1: characterized by slow maturation kinetics and a tendency for photobleaching; the delay between peptide synthesis and chromophore formation can lead to an underestimation of protein yield in short-term reactions.
mKO2: features fast maturation and oxygen dependence; while it reaches peak fluorescence quickly, the final oxidative step of chromophore formation requires sufficient O2 levels, which may become limiting in deep-well plates.
mTurquoise2: known for high quantum yield and acid stability; its low pKa makes it less sensitive to the $pH$ drops that naturally occur as metabolic byproducts (like organic acids) accumulate during long-term cell-free incubation.
mScarlet_I: a high-brightness variant with accelerated maturation compared to earlier red FPs; however, it remains sensitive to the oxidative environment, as oxygen is required to complete the cyclization of its chromophore.
Electra2: optimized for ultra-fast maturation; its rapid “time-to-bright” makes it the ideal candidate for real-time monitoring of transcription-translation (TX-TL) kinetics where immediate feedback is required.
Protein: mScarlet_I
Reagent Adjustment: Increase Glucose and Nicotinamide concentrations while utilizing a semi-permeable reaction seal.
Expected Effect:In a 36-hour run, the primary bottleneck for a bright red FP like mScarlet_I is the depletion of energy and the requirement for oxygen for chromophore maturation. By increasing Glucose and Nicotinamide, we extend the metabolic “runway” for $ATP$ regeneration via the NMP-Ribose-Glucose pathway; combining this with a semi-permeable seal ensures a constant influx of O2 to drive the oxidative maturation of the chromophore, thereby maximizing the total fluorescent signal over the extended incubation period.
The second phase of this lab will be to define the precise reagent concentrations for your cell-free experiment. You will be assigned artwork wells with specific fluorescent proteins and receive an email with instructions this week (by 4/24). Make sure that your final project slide is in the slide deck below to be included!
The final phase of this lab will be analyzing the fluorescence data we collect to determine whether we can draw any conclusions about favorable reagent compositions for our fluorescent proteins. This will be due a week after the data is returned (TBD!).
The bio-tamagotchi is an engineered bacterial system that communicates hunger through a visible fluorescence signal. Rather than programming a digital screen to display need, the bacteria themselves become the display, producing red fluorescent protein in direct proportion to their nutritional stress. The core idea is that the same molecular machinery bacteria use to respond to starvation can be hijacked to drive a reporter gene, turning an invisible biochemical state into something measurable and visible.
The construct consists of two functional parts joined together:Plasmid backbone: Standard cloning vector with chloramphenicol resistance
Host organism: E. coli NEB 10-beta (DH5α background)
Note on DNA concentration: The Twist order arrived with 11,193 ng total DNA written on the tube. This is an extremely high total amount of DNA in a small volume, resulting in very high ng/µL concentration. This explains why: (1) only 1–2 µL of plasmid was needed for transformation, (2) colonies grew extremely densely on the agar plate, and (3) liquid cultures required very high dilution ratios (1:1000) to reach measurable OD600 readings.
OsmY (Osmotically inducible protein Y) is a naturally occurring E. coli gene that encodes a periplasmic protein expressed strongly under stress conditions. Its promoter, PosmY, is one of the most well-characterized stress-inducible promoters in E. coli and is the regulatory element used in this construct.
PosmY uses the host’s own RNA polymerase equipped with the RpoS (σS) sigma factor — making it a biological sensor rather than a chemically induced one.
The PosmY promoter is controlled by RpoS (σS), known as the general stress response sigma factor in E. coli. Here is the step-by-step mechanism:
Under normal conditions (fed bacteria in rich LB media):
Under stress conditions (starved bacteria in M9 media):
What triggers RpoS accumulation (stress signals PosmY responds to):
This makes PosmY a multi-stress sensor rather than a single-input switch, which is ideal for a hunger-monitoring application since nutrient depletion triggers multiple overlapping stress pathways simultaneously.
mCherry is a monomeric red fluorescent protein derived from DsRed of coral origin. It was chosen as the reporter for several reasons:
| Property | Value |
|---|---|
| Excitation maximum | 587 nm |
| Emission maximum | 610 nm |
| Color | Deep red/pink |
| Maturation time | ~30–60 min (needs oxygen) |
| Monomer | Yes — no aggregation artifacts |
| Visible to naked eye | Yes — pink/red tint in dense cultures |
The red color is particularly well-suited for this application because it is visually distinctive and spectrally separated from common sources of cellular autofluorescence (which tend to be blue/green), giving a cleaner signal-to-noise ratio.
The construct aims to demonstrate a proof-of-concept biosensor where:
Characterise the dynamic hunger response of PosmY-mCherry and establish a quantitative fluorescence-to-hunger calibration
establish exactly when PosmY activates after starvation begins, how fast signal rises, and when it peaks — the hunger kinetics of the system
Engineer a self-contained, real-time biological hunger display — a living system that visually communicates its own nutritional state
a closed-loop system where bacteria signal hunger → device detects it → display shows it → user feeds system → bacteria recover → signal drops
Validation goal: Confirm the construct fluoresces at all and responds to osmotic stress (NaCl) as a proxy for general stress signaling.
Experimental goal: Demonstrate that bacteria in M9 minimal media (starved/hungry) produce significantly higher fluorescence than bacteria in LB broth (fed), establishing a hunger vs. satiety fluorescence contrast.
Objective: Introduce PosmY-mCherry plasmid into NEB 10-beta competent cells and select for successful transformants on antibiotic plates.
Fig 1. Twist Bioscience PosmY-mCherry plasmid stock (Q-644161), labeled 11,193 ng total, held on ice.
Note on plasmid stock: Label reads 11,193 ng total in a small volume — exceptionally high concentration. Only 2 µL was used for transformation. This directly explains the very dense colony growth observed the following morning.
Purpose: Allows plasmid DNA to associate with the outer cell membrane surface before heat shock opens pores for DNA entry.
Mechanism: Brief heat spike creates transient pores in the E. coli membrane, allowing plasmid DNA to enter the cell. Rapid return to ice seals the pores, trapping DNA inside.
Fig 3. Eppendorf containing the cell + plasmid mixture in SOC recovery media, ready for the 1-hour shaker incubation.
Why no antibiotic: Cells need time to express the chloramphenicol resistance gene before being challenged with antibiotic selection. Adding it immediately would kill all cells before resistance protein can be produced.
Fig 4. 100 µL of recovered cell suspension spread across LB agar + chloramphenicol plate using a sterile cell spreader.
Fig 5. LB + chloramphenicol plate after overnight incubation at 37°C. Extremely dense colony growth confirms successful transformation.
Outcome: Transformation successful
Every visible colony represents a single E. coli cell that incorporated the PosmY-mCherry plasmid and survived chloramphenicol selection.
Colony density: Directly attributable to the high-concentration Twist plasmid stock — more available DNA = higher uptake probability per cell during heat shock.
Objective: Transfer single colonies from the agar plate into liquid media to grow a large, actively dividing population of transformed bacteria. Two media conditions were prepared in parallel — LB broth (fed/rich) and M9 minimal media (nutrient-poor/starvation) — representing the core experimental contrast of the bio-tamagotchi.
Fig 6. Freshly inoculated culture tubes immediately after colony picking: LB + Chloramphenicol + Colony (left) and M9 + Colony (right), labeled with name and condition. Both tubes are clear at this stage — bacterial growth not yet visible.
Two culture tubes were prepared using premixed LB + Chloramphenicol media (5 mL, provided by TA):
| Tube | Media | Volume | Inoculation |
|---|---|---|---|
| LB + Colony | LB + Chloramphenicol | 2000 µL | Single colony picked from plate |
| M9 + Colony | M9 minimal media | 2000 µL | Single colony picked from plate |
Colony picking method: Touched a single colony on the agar plate with a sterile pipette tip and swirled it directly into the media. A fresh tip was used for each tube. The pipette plunger was not pressed — the tip was used purely as a physical transfer tool.
Fig 7. Tube rotator inside the 37°C warm incubator. Culture tubes mounted with caps loosely attached for aeration, rotating end-over-end throughout the incubation period.
Why overnight: Initial culture volume (2 mL in 5 mL total tube) was more dilute than standard, requiring additional time to reach sufficient cell density. M9 media is also nutrient-poor by design, meaning bacteria in that tube grow considerably slower than in LB.
LB + Colony tubes: Visibly turbid with a golden-yellow color — confirms dense bacterial growth
M9 + Colony tube: Remained largely clear after the same incubation period. This is expected — M9 minimal media contains only the bare minimum nutrients, resulting in much slower bacterial growth compared to rich LB. This behavior is inherent to the starvation condition design.
Objective: Determine cell density of cultures before adding to 96-well plate. Ensures wells receive consistent, measurable cell numbers.
Method: Cultures were too dense to read directly — serial dilutions were made in fresh media, measured, then multiplied by the dilution factor to calculate real OD.
Objective: Create starved bacterial population for the hunger experiment condition. Original M9 cultures failed to grow visibly — alternative starvation method used.
Rationale:
This method is scientifically equivalent or superior to gradual M9 growth — the sudden nutrient switch represents a larger stress signal than slow starvation adaptation.
Fig 11 (left). Two Eppendorf tubes under ambient light after centrifugation of LB culture — pink/purple bacterial pellets visible at the base of each tube, confirming mCherry protein accumulation inside cells.
Fig 12 (right). Same two tubes viewed under blue light transilluminator — pellets emit a vivid orange-red fluorescence, directly visualising mCherry fluorescent protein under excitation light.
This is the most significant qualitative result of the entire experiment. The pink/red colour of the pellet — visible to the naked eye and strongly fluorescent under blue light — confirms:
Even bacteria growing in nutrient-rich LB show baseline mCherry expression, consistent with mild stationary-phase stress activating the PosmY promoter at low levels after overnight growth.
Purpose of wash: Residual LB nutrients in the tube would partially satisfy the bacteria, blunting the starvation signal and reducing PosmY activation. Washing ensures a clean switch to minimal media conditions.
 |  |
|---|---|
| Fig 9. OD600 reading for LB + Colony culture at 1:1000 dilution. Raw absorbance = 0.7 Abs | Fig 10. OD600 reading for M9 + Colony cells at 1:100 dilution. Raw absorbance = 1.0 Abs |
| Culture | Dilution used | Instrument reading | Estimated real OD | Interpretation |
|---|---|---|---|---|
| LB + Colony | 1:1000 (10 µL into 990 µL LB) | 0.7 Abs | ~700 | Extremely overgrown |
| M9 cells (post-spin) | 1:100 (10 µL into 990 µL M9) | 1.0 Abs | ~100 | Extremely overgrown |
Both cultures were far above the usable range of OD 0.4–0.6, confirming that significant dilution was required before use in the 96-well plate.
Action taken: Both cultures were diluted 1:100 in fresh media to prepare working stocks for the plate assay:
| Working stock | From | Into | Final volume |
|---|---|---|---|
| LB working stock | 10 µL LB culture | 990 µL plain LB | 1000 µL |
| M9 working stock | 10 µL M9 cells | 990 µL plain M9 | 1000 µL |
The plate reader independently measures OD600 for each well during the assay read, so the normalization formula (RFU ÷ OD600) corrects for any remaining cell density variation between wells.
Objective: Prepare osmotic stress solutions for validation conditions (Rows B and C).
Starting material: 5M NaCl aqueous solution (lab stock)
| Solution | 5M NaCl stock | Sterile water | Final volume | Concentration |
|---|---|---|---|---|
| Low stress | 60 µL | 940 µL | 1000 µL | 0.3M |
| High stress | 100 µL | 900 µL | 1000 µL | 0.5M |
Verification:
Stored in labeled Eppendorf tubes on bench until plate setup.
| Row | Condition | Role | Component 1 | Component 2 | Component 3 | Total |
|---|---|---|---|---|---|---|
| A | Uninduced | Validation control | 150 µL LB+Chlor | 50 µL LB cells | — | 200 µL |
| B | 0.3M NaCl | Validation low stress | 100 µL LB+Chlor | 50 µL 0.3M NaCl | 50 µL LB cells | 200 µL |
| C | 0.5M NaCl | Validation high stress | 100 µL LB+Chlor | 50 µL 0.5M NaCl | 50 µL LB cells | 200 µL |
| D | LB blank | Background subtraction | 200 µL plain LB | — | — | 200 µL |
| E | Fed cells | Experiment control | 150 µL LB+Chlor | 50 µL LB cells | — | 200 µL |
| F | Starved cells | Experiment condition | 150 µL plain M9 | 50 µL M9 pink cells | — | 200 µL |
| G | M9 blank | Background subtraction | 200 µL plain M9 | — | — | 200 µL |
Addition order for all wells: Media first → NaCl (if applicable) → cells last
Total wells filled: 21 of 96
Objective: Allow bacteria to sense their environment, activate PosmY, produce and fold mCherry protein to detectable levels.
Conditions: 30°C overnight (~12 hours)
Instrument: BioTek Synergy · Software: Gen5 3.05
Read protocol configured:
| Read | Type | Parameters |
|---|---|---|
| Read 1 | Absorbance | 600 nm (OD600) |
| Read 2 | Emission spectrum scan | Ex 587 nm / Em spectrum |
| Read 3 | Fluorescence endpoint | Ex 587 nm / Em 618 nm |
Fig 13. Gen5 matrix: OD600 absorbance values (Read 1:600). Dark blue = high cell density. Light blue/white = low cell density. Row F (Starved M9) = 0.043 — barely above the M9 blank (Row G = 0.037), indicating very few cells present in the starved wells after 1:100 dilution.
Raw OD600 values:
| Row | Condition | Well 1 | Well 2 | Well 3 | Mean | SD |
|---|---|---|---|---|---|---|
| A | Uninduced LB | 1.315 | 1.290 | 1.279 | 1.295 | 0.019 |
| B | 0.3M NaCl | 1.228 | 1.187 | 1.207 | 1.207 | 0.021 |
| C | 0.5M NaCl | 1.177 | 1.165 | 1.165 | 1.169 | 0.007 |
| D | LB blank | 0.048 | 0.048 | 0.050 | 0.049 | 0.001 |
| E | Fed LB | 1.268 | 1.373 | 1.232 | 1.291 | 0.073 |
| F | Starved M9 | 0.043 | 0.043 | 0.043 | 0.043 | 0.000 |
| G | M9 blank | 0.036 | 0.037 | 0.037 | 0.037 | 0.001 |
Absorbance at OD600 basically is the cell density and how cloudy it is, because I need to know whether one well glows more because it has more cells or because each cell is genuinely expressing more mCherry.
Fig 14. Gen5 emission spectrum matrix (Read 2: EM Spectrum — curve view). Mountain-shaped red curves = mCherry fluorescence peak. Flat dot lines = near-zero fluorescence. Rows A, B, C, E show clear peaks; Rows D, F, G are flat.
Emission spectrum scan is the machine shines a fixed wavelength, and then sweeps across a range of emission wavelengths and records how much light comes out at each one to get a full spectrum.
Fig 15. Gen5 matrix: first wavelength read values from the EM spectrum scan (Read 2: EM Spectrum Read#1). These are the fluorescence intensity values at the first emission wavelength measured during the spectral scan.
Raw EM Spectrum Read#1 values:
| Row | Condition | Well 1 | Well 2 | Well 3 | Mean | SD |
|---|---|---|---|---|---|---|
| A | Uninduced LB | 1675 | 1548 | 1502 | 1575 | 90 |
| B | 0.3M NaCl | 1603 | 1420 | 1391 | 1471 | 115 |
| C | 0.5M NaCl | 1356 | 1177 | 1249 | 1261 | 90 |
| D | LB blank | 931 | 1092 | 1314 | 1112 | 193 |
| E | Fed LB | 1364 | 1297 | 1300 | 1320 | 38 |
| F | Starved M9 | 55 | 50 | 51 | 52 | 3 |
| G | M9 blank | 48 | 69 | 61 | 59 | 11 |
Fig 16. Gen5 matrix: raw fluorescence endpoint values in RFU (Read 3: Ex 587 / Em 618 nm). Darker blue = higher mCherry fluorescence. Rows A, B, C, E show strong signal. Rows D, F, G show near-background values.
Raw RFU endpoint values:
| Row | Condition | Well 1 | Well 2 | Well 3 | Mean | SD |
|---|---|---|---|---|---|---|
| A | Uninduced LB | 6951 | 5663 | 6215 | 6276 | 648 |
| B | 0.3M NaCl | 8071 | 6662 | 6416 | 7050 | 893 |
| C | 0.5M NaCl | 6067 | 4906 | 4997 | 5323 | 643 |
| D | LB blank | 62 | 70 | 87 | 73 | 13 |
| E | Fed LB | 5204 | 4676 | 4941 | 4940 | 264 |
| F | Starved M9 | 189 | 173 | 185 | 182 | 8 |
| G | M9 blank | 7 | 5 | 5 | 6 | 1 |
Florescence endpoint is just shining a fixed wavelength and then take a specific emission wavelength, tells me the relative florescence unit. Higher RFU = more mCherry protein present = PosmY promoter was more active = stronger stress/hunger signal detected by the cell.
Fig 17. Gen5 matrix: Mean Max RFU values derived from the full emission spectrum scan — the peak fluorescence intensity across all scanned emission wavelengths per well. This is the most complete measure of total mCherry output as it captures the full emission curve peak rather than a fixed single wavelength.
Raw Mean Max RFU values:
| Row | Condition | Well 1 | Well 2 | Well 3 | Mean | SD |
|---|---|---|---|---|---|---|
| A | Uninduced LB | 13557 | 11135 | 11832 | 12175 | 1238 |
| B | 0.3M NaCl | 15165 | 12639 | 12095 | 13300 | 1618 |
| C | 0.5M NaCl | 11611 | 9538 | 9813 | 10321 | 1131 |
| D | LB blank | 1107 | 1288 | 1416 | 1270 | 156 |
| E | Fed LB | 10188 | 9148 | 9504 | 9613 | 529 |
| F | Starved M9 | 313 | 353 | 363 | 343 | 27 |
| G | M9 blank | 56 | 69 | 61 | 62 | 7 |
All charts generated from raw file data (Posmy-mCherry.xls). Error bars = 1 standard deviation across 3 replicates. Blank subtracted prior to normalization.
Fig 18. OD600 absorbance per condition. Dashed lines indicate standard working OD range (0.4–0.6). All LB conditions are well above range due to overnight incubation. Row F (Starved M9) OD = 0.043 — near the M9 blank, indicating very low cell density after 1:100 dilution.
Fig 19. Raw RFU endpoint values (Ex 587 / Em 618 nm) per condition before normalization. Row F appears low in absolute terms purely because of very low cell count — normalization by OD600 corrects for this.
Fig 20. Normalized fluorescence (RFU ÷ OD600) per condition, endpoint read (Ex 587 / Em 618 nm). Error bars = 1 SD. This is the primary experimental result — fluorescence per cell, corrected for cell density differences between wells.
Normalized endpoint values:
| Row | Condition | Normalized RFU/OD | SD |
|---|---|---|---|
| A | Uninduced LB | 4789 | ±449 |
| B | 0.3M NaCl | 5773 | ±658 |
| C | 0.5M NaCl | 4489 | ±524 |
| E | Fed LB | 3783 | ±376 |
| F | Starved M9 | 4109 | ±194 |
Fig 21. Normalized Mean Max RFU from the full emission spectrum scan, divided by OD600. Captures the true peak of the mCherry emission curve rather than a fixed wavelength — a more complete measure of total mCherry output per cell.
Normalized Mean Max values:
| Row | Condition | Normalized Mean Max RFU/OD | SD |
|---|---|---|---|
| A | Uninduced LB | 8416 | ±859 |
| B | 0.3M NaCl | 9954 | ±1218 |
| C | 0.5M NaCl | 7738 | ±915 |
| E | Fed LB | 6485 | ±670 |
| F | Starved M9 | 6535 | ±615 |
The emission spectrum (Fig 14) shows clear, consistent mountain-shaped fluorescence peaks in all LB cell-containing wells (Rows A, B, C, E), with peak emission centered at approximately 610–620 nm — the exact spectral signature of mCherry fluorescent protein.
Rows D and G (media blanks) are completely flat, confirming:
This is further corroborated by the pink/red pellet observed visually during centrifugation (Stage 4, Figs 11–12), providing independent visual confirmation of mCherry expression.
Comparing normalized RFU/OD across validation conditions:
Row B (0.3M NaCl) shows +20.5% higher normalized fluorescence per cell than the uninduced control, confirming that the PosmY promoter responds to osmotic stress as designed.
Row C (0.5M NaCl) falls slightly below Row A, consistent with known osmotic inhibition at higher salt concentrations — excess NaCl begins to impair cellular function, reducing mCherry output despite promoter activation. This finding suggests 0.3M NaCl is the optimal induction concentration for this construct.
Endpoint read (Fig 20):
Mean Max RFU spectrum peak (Fig 21):
Both metrics show starved M9 cells with higher normalized fluorescence per cell than fed LB cells — directionally consistent with the bio-tamagotchi hypothesis that nutrient deprivation activates PosmY and drives mCherry expression. The endpoint read shows a more pronounced difference (+8.6%) while the spectrum peak measure shows a near-identical result between the two conditions (+0.8%).
Row F OD600 = 0.043 vs M9 blank = 0.037 — a difference of only 0.006 OD units, representing an extremely small number of cells. This occurred because the 1:100 dilution of the M9 resuspension was too aggressive, leaving barely any bacteria in the starved wells.
Consequences:
Raw data sourced from Gen5 3.05 export: Posmy-mCherry.xls
To the people who made this final project survivable
| 
|
| Dr. David S. Kong | Ronan |
| Course Director | Teaching Assistant |
| 
|
| Suvin | Alex |
| Teaching Assistant | Teaching Assistant |
This project would not have been possible without your guidance, patience, and willingness to answer Slack messages at unreasonable hours.