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.
Write a description about what you intend to do with automation tools for your final project. You may include example pseudocode or Python scripts, procedures you may need to automate, 3D printed holders you may need, and more.
Example ideas that you can create a protocol for: Use the cloud laboratory to screen an array of biosensors constructs that you design, synthesize, and express using cell-free protein synthesis Use Opentrons to dispense microorganisms onto fabric to design “living textiles” as “bio artwork”
Find and briefly summarize a published paper that utilizes laboratory automation to achieve novel biological applications Include in your summary: General overview (2 paragraphs) Findings (1 paragraph) Relevant Figures (1 - 2 max)
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.
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/ .
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.
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:
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!).
Construct: PosmY→mCherry (Hunger/Nutrient Depletion Reporter)
PosmY is an osmotic and nutrient-responsive promoter that becomes transcriptionally active as available carbon sources and nitrogen sources deplete in the surrounding media. As bacteria consume LB broth over hours, PosmY activity increases and mCherry accumulates. mCherry is a fast-maturing, photostable red fluorescent protein.
Why mCherry specifically: mCherry’s 610nm emission is far enough from cellular autofluorescence (peak ~500–550nm) that signal-to-noise is excellent even with a simple longpass filter. Its large Stokes shift (590nm ex → 610nm em) allows the amber LED to excite it without the emission overlapping the excitation wavelength. It matures faster than mKate or mRFP and is more photostable under repeated amber LED illumination than DsRed variants.
Ideally a thyA⁻ auxotrophic strain (cannot survive outside thymidine-supplemented media). BSL-1 organism. Standard good microbiological practice applies. Transformed with pBad/T7 or equivalent low-copy plasmid carrying PosmY→mCherry + antibiotic resistance marker (kanamycin recommended; ampicillin degrades in media and loses selective pressure over hours).
The agar layer immobilizes cells, giving consistent geometry for fluorescence reads and EIS measurements. The liquid layer on top depletes over hours to drive PosmY induction (hunger signal) and acidification (pH signal).
Project: PosmY-mCherry Induction & Recovery Characterization
Objective: To quantitatively validate the transition of E. coli through three metabolic states—Thriving, Hungry, and Recovering—using fluorescence intensity (RFU) and optical density (OD600) as indicators of osmotic stress response.
| Item | Specifications | Function |
|---|---|---|
| Multi-mode Microplate Reader | Capable of OD600 (±5 nm) and fluorescence (Ex: 587 nm / Em: 610 nm for mCherry) | Performs kinetic assays measuring bacterial density and mCherry expression |
| Inverted Fluorescence Microscope | mCherry filter set (Ex: 580/20 nm, Em: 630/60 nm) | Visual validation of fluorescence in PDMS microfluidic chambers |
| Shaking Incubator | 37°C ± 0.5°C, orbital shaking at 220 RPM | Aerobic growth conditions for starter cultures |
| Vacuum Desiccator | -15 to -20 inHg | Degassing PDMS during device fabrication |
| Pipettes | P20, P200, P1000 with sterile filtered tips | Aseptic liquid handling |
| Item | Specifications | Function |
|---|---|---|
| E. coli Biosensor Strain | DH5α or similar, transformed with pTwist-Cm-PosmY-mCherry | Osmotic stress reporter strain |
| Luria-Bertani (LB) Broth | Miller formulation (10 g/L tryptone, 5 g/L yeast extract, 10 g/L NaCl) | Standard growth medium |
| Chloramphenicol (Cm) | Working concentration: 34 µg/mL in 100% ethanol stock (1000x) | Plasmid selection and maintenance |
| 10× Concentrated LB | Autoclaved and filter-sterilized | Nutrient spike for recovery phase without volume dilution |
| 96-well Plate | Black-walled, clear flat bottom, tissue culture treated | Minimizes optical crosstalk; OD600 and fluorescence compatible |
| Breathable Sealing Film | Gas-permeable, adhesive | Prevents evaporation while maintaining aerobic conditions |
| Phosphate Buffered Saline (PBS) | pH 7.4, sterile | Microscopy mounting and wash buffer |
Duration: 12–16 hours
Goal: Generate overnight culture entering stationary phase (“Hungry” state)
Starter Culture Inoculation:
Incubation:
Quality Check:
Duration: 16 hours (pre-feeding)
Goal: Characterize “Thriving” → “Hungry” transition
Back-Dilution to Thriving State:
96-Well Plate Layout:
Plate Reader Configuration:
Execution:
Duration: 8 hours (post-feeding)
Goal: Demonstrate signal dilution upon nutrient restoration (“Recovery” state)
Trigger Criteria:
Pause and Feeding:
Post-Feeding Kinetic Run:
Goal: Visually confirm mCherry expression patterns
Sample Preparation:
Imaging:
For every timepoint (t):
If background-subtracted OD600 < 0.01, set to 0.01 to avoid division errors
Calculate the normalized fluorescence for each experimental well:
Normalized Fluorescence = RFUnet / OD600,net
Units: Relative Fluorescence Units per OD (RFU/OD)
For each timepoint:
Baseline Fluorescence (Thriving State):
Peak Fluorescence (Hungry State):
Fold Induction:
Recovery Rate:
Figure 1: Bio-Tamagotchi Metabolic State Transitions
Plot Specifications:
Data to Include:
Caption Example: “Figure 1. Characterization of PosmY-mCherry biosensor dynamics. E. coli cultures were monitored for growth (OD600, blue) and osmotic stress response (normalized mCherry fluorescence, red) over 24 hours. Nutrient depletion triggers PosmY activation (Hungry state), which is reversed upon feeding with 10× LB (dashed line, t = 12 h). Data represent mean ± SEM (n=3 biological replicates).”
Figure 2: Single-Cell Fluorescence Validation
Panel A: Phase contrast image of cells at t = 2 h (Thriving)
Panel B: mCherry fluorescence of same field (Thriving) - minimal signal
Panel C: Phase contrast image of cells at t = 12 h (Hungry)
Panel D: mCherry fluorescence of same field (Hungry) - bright cytoplasmic signal
Caption Example: “Figure 2. Single-cell validation of metabolic state. Representative micrographs showing (A,B) Thriving cells with minimal mCherry expression and (C,D) Hungry cells with elevated osmotic stress response. Scale bar = 5 µm.”
| Metric | Value | Units |
|---|---|---|
| Baseline Fluorescence (Thriving) | 150 ± 20 | RFU/OD |
| Peak Fluorescence (Hungry) | 1200 ± 180 | RFU/OD |
| Fold Induction | 8.0 ± 1.2 | Fold |
| Time to Peak | 11.5 ± 0.8 | Hours |
| Recovery Half-Time | 2.3 ± 0.4 | Hours |
| Growth Rate (Exponential Phase) | 0.65 ± 0.05 | h⁻¹ |
Values reported as mean ± SD from n=3 independent experiments
| Problem | Possible Cause | Solution |
|---|---|---|
| No fluorescence increase | Plasmid loss | Re-streak from glycerol stock; verify Cm resistance |
| Incorrect filter set | Confirm mCherry-compatible wavelengths (Ex: 587, Em: 610) | |
| High blank fluorescence | Media autofluorescence | Use fresh LB; avoid over-autoclaving |
| Plate contamination | Check sterile technique; replace sealing film | |
| Erratic OD600 readings | Bubbles in wells | Centrifuge plate briefly (200×g, 1 min) |
| Condensation on lid | Pre-warm plate reader; use anti-evaporation lids | |
| No recovery after feeding | Feeding volume too small | Verify 10× LB concentration; increase to 25 µL |
| Cells too stressed | Reduce pre-feeding duration to 10 hours | |
| High variability between replicates | Inconsistent inoculation | Use overnight culture in mid-log phase (OD ~ 2.5) |
| Temperature fluctuations | Verify incubator/reader temperature stability |
Growth Dynamics:
Fluorescence Response:
Reproducibility:
This system models a simplified “digital pet” where fluorescence serves as a real-time readout of bacterial metabolic health, demonstrating principles of:
Visionary Lookout:
Arduino Nano Pin → Component ───────────────────────────────────────────────────────── A0 → pH electrode Vout (via MCP6001 unity-gain buffer) A1 → FSR feed button (+ 10kΩ to GND, 5V supply) A2 → OPT101 Vout (direct, no resistor needed) A4 (SDA) → SSD1306 OLED (0x3C) + AD5933 (0x0D) — shared I2C bus A5 (SCL) → SSD1306 OLED (0x3C) + AD5933 (0x0D) — shared I2C bus D6 (PWM) → Amber LED 590nm (+ 220Ω to LED anode) 5V → FSR supply, LED supply, OPT101 V+, MCP6001 VDD 3.3V → OLED VCC, AD5933 VDD GND → Everything
Off-Arduino (in well, wired to PCB pads): PCB pad “pH_W” → IrOx/Pt working electrode wire PCB pad “pH_R” → Ag/AgCl reference electrode wire PCB pad “EIS_1” → AD5933 VIN → Pt electrode 1 PCB pad “EIS_2” → AD5933 VOUT via RFB (1kΩ) → Pt electrode 2
ΔpH = pH_current − pH_baseline (negative = acidifying = active metabolism) ΔZ = Z_current − Z_baseline (negative = losing membrane integrity) ΔmCherry = mCherry_current − mCherry_baseline (positive = rising hunger signal)
STATE pH SIGNAL IMPEDANCE mCHERRY INTERPRETATION ────────────────────────────────────────────────────────────────────────────────── THRIVING ΔpH < −0.3 ΔZ within ±15% ΔmCherry low Active growth, happy (acidifying) of baseline (<+20%) “Alive and eating well”
HUNGRY ΔpH flat ΔZ within ±15% ΔmCherry high Nutrients depleted, (near baseline) of baseline (>+50%) PosmY active “Feed me”
SICK ΔpH flat ΔZ < −20% ΔmCherry high Cells dying, membranes or rising (dropping) (>+50%) compromised “Sick, may not recover”
RECOVERING ΔpH drops ΔZ stable or ΔmCherry Just fed, bacteria rapidly after improving dropping consuming fresh LB, feeding event from SICK low from peak PosmY shutting off
OVERFED ΔpH < −0.8 ΔZ rising ΔmCherry low pH crash from excess (acidifying above baseline (< baseline) feeding; media too severely) (cell crowding) acidic for growth
With only one LED and one fluorescent protein, the timing cycle is straightforward:
No multiplexing required. The timing simplification also improves SNR — the OPT101 integrates for a full 100ms on each mCherry read rather than the briefer reads forced by v1’s 3-LED rotation.
lastFedTime for RECOVERING state logic.