iGEM 2025 — Team missouri-miners

{% extends "layout.html" %}
{% include "heros/notebook-hero.html"%}
{% block page\_content %}
{% endblock %}

{% extends "layout.html" %}
{% include "heros/engineering-hero.html"%}
{% block page\_content %}

# Engineering Success

## Introduction

Mining and metal recovery are essential to modern industries, but conventional methods are energy-intensive, environmentally destructive, and generate toxic byproducts [1–3]. Metlock aims to develop a safe, efficient biomining system using engineered *E. coli* to selectively gather zinc from ores, industrial waste, and polluted environments/water sources. By synthetically regulating high-affinity zinc transport systems, Metlock demonstrates an iterative engineering approach to create a modular platform for sustainable metal recovery [4–6].

Metlock took on a challenging set of design goals: develop a library of synthetic genetic parts useful in biomining and remain modular for future contributions. Our final objective is for Metlock to expand to encompass several designer strains targeted for a variety of metals, accomadating both our current amount of talent and resources, this year we focused on the following design goal: design a fine-tunable system to maximize zinc influx while avoiding cytotoxicity. This will allow us to pilot workflows, develop our skills with the necessary molecular methods, and expand our dry lab skillset for our more advanced project goals. Following strain identification, this system could then be tuned for profitable yield in-lab and eventually deployed in simulated industrial settings.

## Design

To implement our goal of a tunable zinc uptake system, *E. coli*’s endogenous ZnuABC operon was redesigned in-silico to no longer be repressed by Zur and placed under the induction of the BBa\_K1960004 cassette – a part originally designed by Shijia Qi 2016 [7,4,6]. This allows for the tuning of expression of ZnuABC across an AHL gradient to tune expression as well as across copy number in clonal vectors [8–9,10]. The individual components of ZnuABC serve the following functions: ZnuA is the periplasmic solute-binding protein that captures Zn²⁺, ZnuB is the inner-membrane permease that translocates Zn²⁺, and ZnuC is the cytosolic ATPase that powers transport by hydrolyzing ATP [5].

![](https://static.igem.wiki/teams/5759/engineering/fig-9.webp)

Figure 1: LuxR producing and binding ability of BBa\_K1893004 functionally drives expression of the coding sequences present in the operon

Downstream of pLuxR, ZnuABC is expressed only in the presence of AHL, tying expression of this active metal transporting complex to the formation of AHL/LuxR complexes [8–9,10]. In this configuration, the titration of AHL into media containing bacteria transformed with this iteration of Metlock would theoretically lead to tunable expression of znuABC.

To validate the tunable zinc import system, we plan to utilize a colorimetric uptake assay using the Zn-specific dye Zincon (Z002), which forms a blue Zn–complex measurable at λ\_max = 620 nm (ε ≈ 23,000 M⁻¹cm⁻¹) under alkaline conditions (pH ~9) [11].

### RECOMMENDED ASSAY: Zincon-based Zn²⁺ uptake (supernatant-depletion readout)

**Goal:** Quantify Zn²⁺ import as a function of AHL induction of your Zur-independent iZnuABC cassette (LuxR/pLuxR control) using a colorimetric readout of residual extracellular Zn²⁺ with Zincon at λₘₐₓ ≈ 620 nm in pH 9 buffer [11,12,13,14].

**Rationale:** ZnuA/B/C is the high-affinity ABC Zn²⁺ importer (ZnuA: periplasmic binder; ZnuB: permease; ZnuC: ATPase), and Δznu mutants show impaired Zn acquisition phenotypes under Zn limitation which is ideal for controls [15,16,17,5].

### Strains:

1. iZnuABC (LuxR/pLuxR-driven, Zur-independent znuABC).
2. ΔznuB knockout (negative control for transport, this would be a strain of synthetic ZnuABC which does not contain ZnuB).
3. Empty-vector control.

   (pLuxR/LuxR cassette ref: BBa\_K1960004 [7,8].)

### Reagents & prep:

* ZnSO₄ stock (e.g., 10 mM).
* Metal-free water/buffers/media prepared by batch treatment with Chelex-100 and thorough rinsing [18–20].
* AHL gradient (e.g., 0, 1, 3, 10, 30, 100, 300 nM) to span induction range [7,8].
* Zincon reagents and pH 9 buffer for the spectrophotometric method (Standard Methods 3500-Zn B; λ = 620 nm; includes optional cyanide/cyclohexanone masking for interferences) [12,11,13].

  For a cyanide-free workflow in clean matrices, use a straightforward microplate adaptation at pH 9 with calibration and spike-recovery checks [11,14].

  Do not add EDTA to measurement tubes; it chelates Zn²⁺ and will depress the Zincon signal [21].

### Culture & induction:

Grow strains in Zn-depleted M9 (Chelex-treated) overnight; back-dilute 1:100 into fresh Zn-depleted M9 ± AHL; grow to OD₆₀₀ ≈ 0.3–0.4, then induce/express iZnuABC for ~90–120 min; wash and resuspend in Zn-free uptake buffer to OD₆₀₀ = 0.5 [7,8,18–20].

### Uptake reaction (time-course):

Mix cells with 25–50 µM ZnSO₄ in uptake buffer (e.g., 1.0 mL tubes or 200 µL wells at 30–37 °C).

Collect cell-free supernatant at 0, 2, 5, 10, 20, 30, 45, 60 min (quick pellet or filter). Keep on ice. *(No EDTA in these aliquots.)* [12,11,13,21]

### Zn²⁺ measurement (Zincon @ 620 nm):

* Path A: Standard Methods 3500-Zn B (robust): buffered pH 9; sodium ascorbate to suppress Mn interference; CN⁻ to complex metals; cyclohexanone to release Zn selectively; read 620 nm. Use only in labs approved for cyanide handling [12,13]
* Path B: Cyanide-free microplate (clean matrices): pH 9 buffer + Zincon; read 620 nm; verify matrix with spike-recovery ≥ 90% and note potential Cu interference [11,14].

### Controls:

ΔznuB knockout (low uptake), empty vector, heat-killed cells (adsorption), no-cell blank, Zn standards (5–100 µM; R² > 0.99), and spike-recovery samples; optional post-run EDTA “surface-strip” on pellets (not for Zincon readout) [12,11,13,16,17,5,21].

### Calculations & output:

Convert A620 to [Zn] via the standard curve; compute Uptake(t) = ([Zn]₀ − [Zn]ₜ)×V; normalize by OD₆₀₀ (or mg protein). Report initial rate (0–10 min slope; pmol Zn·OD₆₀₀⁻¹·min⁻¹) and plot uptake vs AHL to obtain induction curves (Hill fit optional) [11,12,13,14].

### Hypothesized results:

Induced iZnuABC shows AHL-dependent increases in uptake; ΔznuB remains near baseline; empty-vector minimal. This pattern reflects high-affinity ZnuABC transport under Zn limitation [5,15,16,17].

## Build

**Clonal Vector Ordering:** We ordered our parts using the clonal vectors offered by the Twist Bioscience iGEM Sponsorship, cloning our iZnuABC (LuxR/pLuxR-driven, Zur-independent znuABC) into the pUPD3 vector [15, 16].

**Transformations:** We transformed the plasmid by heat-shock into *E. coli* BL21, DH5α, and TOP10 strains as test chassis; our pUC19 positive-control transformation succeeded, but we observed no expression from the ordered clonal vector from its antibiotic resistance cassette - resulting in no viable cells to continue our experiments with [17–23].

## Test

**Objective:** Demonstrate that LuxR/pLuxR-induced, Zur-independent iZnuABC increases Zn²⁺ import, quantified by Zincon supernatant-depletion across an AHL gradient [11–14,5,15–17].

**Observed block:** pUC19 transformed successfully, but no colonies from the pUPD3::synthetic ZnuABC clonal vector on chloramphenicol plates (correct selection for pUPD3 is Chloramphenicol) [22].

### Most likely causes (with fixes):

1. **Transformation workflow sensitivity (outgrowth):** Chloramphenicol is bacteriostatic; giving ~60 min outgrowth in SOC at 37 °C with shaking materially improves survival/expression of the cat (chloramphenicol acetyltransferase) marker before plating [30,34].
2. **Antibiotic working range:** Plate a Chloramphenicol gradient (12.5 / 25 / 34 / 50 µg/mL) in parallel; 25 µg/mL is the standard recommendation for cat-based backbones like pUPD3 [22,31].
3. **Plasmid size/quality:** Large assemblies or salt-contaminated DNA reduce chemical-transformation efficiency; try electroporation (1–2 mm cuvette, ~1.8 kV, ~5 ms pulse) which boosts efficiency for bigger plasmids [35,34].
4. **Vector verification:** Run restriction digest/agarose on the clonal DNA and PCR across backbone and insert junctions after a small-scale no-antibiotic recovery (patch survivors to Chloramphenicol) to confirm integrity before concluding a build error [22,34].
5. **Strain idiosyncrasies:** Keep DH5α/TOP10 as cloning strains; BL21 is fine but not necessary at this step. If chemical competence is borderline, prepare fresh Hanahan/Inoue-style competent cells or use commercial high-efficiency cells [36–38].

### QC gates to unlock the assay

1. **Replica plating check:** Transform pUPD3::iZnuABC and plate across the Chloramphenicol gradient + a no-antibiotic plate; incubate 18–24 h. Positive hits should appear ≥12.5–25 µg/mL if the cat cassette is intact [22,31,30].
2. **Colony PCR + Sanger:** Verify (1) backbone and insert and (2) znuA, znuB, and znuC junctions in 2–4 colonies. Archive glycerol stocks [22].
3. **(Optional) Induction sanity-check:** Parallel strain carrying pLuxR-reporter to locate a clean AHL window before metal assays [8–10].

**Functional readout (after QC passes):** Perform the Zincon uptake assay as specified (Zn-depleted medium, AHL 0–300 nM, 25–50 µM ZnSO₄, supernatant @ 620 nm in pH 9 buffer; exclude EDTA from read tubes; include ΔznuB and empty-vector controls) and report initial uptake rate (0–10 min slope; pmol Zn·OD₆₀₀⁻¹·min⁻¹) and AHL dose–response [11–14,18–21].

**Expected outcome:** With verified constructs on Chloramphenicol and a confirmed induction window, iZnuABC shows AHL-dependent increases in uptake; ΔznuB remains near baseline; empty-vector minimal [5,15–17].

## Learn

**Why we modeled now:** While we troubleshoot the clonal build, we advanced learning by computationally characterizing our circuit, so the wet-lab assay starts with informed AHL set-points, promoter choices, and expected magnitudes. We used our Plot2Curve (P2C) workflows: the AHL induction model (LuxR/pLuxR Hill fit) and the Anderson-family promoter panel to turn sequences into predicted transcription rate (k\_tx) to mRNA to protein output curves, then translate those outputs into assay-ready expectations for Zn-uptake readouts [46, 44–45].

1. **sfGFP-first baseline (establish instrument & model scale)**

   1. **Reporter known to express:** We first run Anderson Family-sfGFP in P2C to fit a Hill curve, extracting EC50, Hill n, and dynamic range for our chassis and reader settings (sfGFP is a robust, well-folded reporter) [39, 46].
   2. **Unit calibration:** We map fluorescence units to molecules·cell⁻¹ (or to a consistent AU/OD₆₀₀ scale) so that downstream CDS predictions are comparable across promoters and AHL doses.
   3. **AHL window lock-in:** The fitted sfGFP curve defines the practical induction window (e.g., a sub-EC50 point, mid-slope point, and near-saturation point) that we will use in the Zincon assay design [46].
2. **Sequence-aware CDS modeling**

   1. **Input = CDS + 5′ UTR context:** For each gene (sfGFP and later ZnuA/ZnuB/ZnuC), P2C evaluates the RBS/SD match and spacing to estimate initiation rate and an effective translation throughput (proteins·mRNA⁻¹·s⁻¹), with ribosome-traffic caps [42–43, 47, 41, 49].
   2. **mRNA/protein dynamics:**  Using our standard bacterial parameters (mRNA half-life ~6 min; doubling time ~30–40 min), we compute m\_eq = k\_tx/mRNA\_decay and protein\_eq = β·m\_eq/α, then simulate time courses for each AHL point to capture approach-to-steady-state behavior [40, 48].
3. **Promoter benchmarking with the Anderson panel**

   * We pass the same CDSs through the Anderson family panel in P2C (e.g., J23100–J23118) to compute relative transcription rate (k\_tx) scaling. This provides a promoter ladder for future builds so we can step expression up/down without guesswork and place our AHL inducible systems in terms of an established baseline both in the lab and computationally [44–45].
4. **What we learned**

   * **Anderson Family Setpoints:** The Anderson family of promoters will be modeled using metrics set by Bionumbers to [41, 48].
   * **AHL set-points:** The sfGFP Hill fit gives three concrete induction levels (sub-EC50, mid-slope, near-max) to use as standard assay AHL points [46].
5. **Delierables we will generate alongside wet-lab**

   * **P2C “AHL” report:** fitted EC50, Hill n, dynamic range for sfGFP to support eventual ZnuABC activity predictions at the same AHL points [39, 46].
   * **P2C “Anderson” report:** predicted k\_tx ladder for sfGFP with a shortlist of promoter swaps [44–45].
   * **CSV exports:** per-condition k\_tx, m\_eq, and protein\_eq to use in the Learn for Design-2.

## References

[1] Jaiswal, M., & Srivastava, S. (2024). A review on sustainable approach of bioleaching of precious metals from electronic wastes. *Journal of Hazardous Materials Advances*, 14, 100435.

[2] Cozma, P., Simion, I.-M., Filote, C., Roșca, M., Hlihor, R. M., & Gavrilescu, M. (2024). Bio-recovery of metals through biomining within circularity-based solutions. *Processes*, 12(9), 1793.

[3] Mervine, E. M., Valenta, R. K., Paterson, J. S., Mudd, G. M., Werner, T. T., Nursamsi, I., & Sonter, L. J. (2025). Biomass carbon emissions from nickel mining have significant implications for climate action. *Nature Communications*, 16(1), 481.

[4] Patzer, S. I., & Hantke, K. (2000). The zinc-responsive regulator Zur and its control of the znuACB zinc uptake system in *Escherichia coli*. *Journal of Biological Chemistry*, 275(32), 24321–24332.

[5] Ammendola, S., Pasquali, P., Pistoia, C., Petrucci, P., Petrarca, P., Rotilio, G., & Battistoni, A. (2007). High-affinity Zn²⁺ uptake system ZnuABC is required for bacterial zinc homeostasis and virulence. *Infection and Immunity*, 75(12), 5867–5876.

[6] Kandari, D., Joshi, H., & Bhatnagar, R. (2021). Zur: Zinc-sensing transcriptional regulator in a diverse set of bacterial species. *Pathogens*, 10(2), 165.

[7] Qi, S. (2016). Part: BBa\_K1960004 (Low-level LuxR and lux pR cassette) [Web page]. *iGEM Parts Registry*.

[8] Part: BBa\_R0062 (pLuxR promoter) [Web page]. (n.d.). *iGEM Parts Registry*.

[9] Part: BBa\_C0062 (LuxR) [Web page]. (n.d.). *iGEM Parts Registry*.

[10] Shiner, E. K., Reddy, S., Timmons, C., Li, G., Williams, S. C., & Rumbaugh, K. P. (2004). Construction of a bacterial autoinducer detection system in mammalian cells. *Biological Procedures Online*, 6, 268–276.

[11] Dojindo. (n.d.). Zincon (Z002) product page.

[12] National Environmental Methods Index. (n.d.). Standard Methods 3500-Zn B: Zinc by Zincon Method (method summary).

[13] American Public Health Association. (1992). Standard Methods for the Examination of Water and Wastewater: 3500-Zn ZINC (Zincon method) [EPA-hosted text].

[14] Hach Company. (2007). Zincon Method for Zinc (Method 8009) [PDF].

[15] Patzer, S. I., & Hantke, K. (1998). The ZnuABC high-affinity zinc-uptake system and its regulator Zur in *Escherichia coli*. *Molecular Microbiology*, 28(6), 1199–1210.

[16] Yatsunyk, L. A., Easton, J. A., Kim, D., Sugar, F. J., Bennett, B., Breece, R. M., Hafeez, A. K., Gibney, B. R., & Rosenzweig, A. C. (2008). Structure and metal-binding properties of ZnuA, a periplasmic zinc transporter from *E. coli*. *Journal of Biological Inorganic Chemistry*, 13(2), 271–288.

[17] Gabbianelli, R., Scotti, R., Ammendola, S., Petrarca, P., Nicolini, L., & Battistoni, A. (2011). Role of ZnuABC and ZinT in *Escherichia coli* O157:H7 zinc acquisition and interaction with epithelial cells. *BMC Microbiology*, 11, 36.

[18] Bio-Rad Laboratories. (n.d.). Chelex® 100 and Chelex 20 chelating ion exchange resin (Technical Bulletin LIT200) [PDF].

[19] Bio-Rad Laboratories. (n.d.). Chelex® 100 Resin [Product page].

[20] Bio-Rad Laboratories. (n.d.). Sample preparation with Chelex® Resin (Bulletin 1825) [PDF].

[21] Nyborg, J. K., & Peersen, O. B. (2004). That zincing feeling: The effects of EDTA on zinc-binding proteins. *Biochemical Journal*, 381(2), e3–e4.

[22] Addgene. (n.d.). pUPD3 (GoldenBraid/GB2.0 domestication vector), Plasmid #118043 [Vector description and map].

[23] Cooke, M. B., Ballinari, P., Goyal, N., Shetty, R., Giannella, P., Chevreux, B., Smedley, M., & Glipsch, K. (2025). GoldenBraid2.0 *E. coli*: A comprehensive and characterized toolkit for enterics. *Synthetic Biology*, 10(1), ysaf015.

[24] New England Biolabs. (n.d.). Competent *E. coli*: High-efficiency transformation protocol.

[25] New England Biolabs. (n.d.). NEB® 5-alpha competent *E. coli* (high efficiency).

[26] Thermo Fisher Scientific. (n.d.). One Shot™ TOP10 chemically competent *E. coli*.

[27] Merck (Novagen). (n.d.). BL21(DE3) competent cells.

[28] New England Biolabs. (n.d.). pUC19 vector.

[29] Thermo Fisher Scientific. (n.d.). One Shot™ TOP10 chemically competent *E. coli*: Includes pUC19 control DNA [Product documentation].

[30] New England Biolabs. (n.d.). High-efficiency transformation protocol—Outgrowth recommendations.

[31] Barrick Lab. (n.d.). Antibiotic stock solutions and working concentrations.

[32] Addgene. (n.d.). pUPD3 (Plasmid #118043) details and selection (chloramphenicol).

[33] Sarrion-Perdigones, A., Vazquez-Vilar, M., Palací, J., Castelijns, B., Forment, J., Ziarsolo, P., Blanca, J., Granell, A., & Orzaez, D. (2013). GoldenBraid 2.0: A comprehensive DNA assembly framework for plant synthetic biology. *Plant Physiology*, 162(3), 1618–1631.

[34] New England Biolabs. (n.d.). Tips for successful transformations (troubleshooting guide).

[35] Dower, W. J., Miller, J. F., & Ragsdale, C. W. (1988). High efficiency transformation of *E. coli* by high voltage electroporation. *Nucleic Acids Research*, 16(13), 6127–6145.

[36] Hanahan, D. (1983). Studies on transformation of *Escherichia coli* with plasmids. *Journal of Molecular Biology*, 166(4), 557–580.

[37] American Society for Microbiology (ASM). (n.d.). Transformation of *E. coli* made competent by CaCl₂ [Protocol].

[38] Inoue, H., Nojima, H., & Okayama, H. (1990). High efficiency transformation of *Escherichia coli* with plasmids. *Gene*, 96(1), 23–38.

[39] Pédelacq, J.-D., Cabantous, S., Tran, T., Terwilliger, T. C., & Waldo, G. S. (2006). Engineering and characterization of a superfolder green fluorescent protein. *Nature Biotechnology*, 24(1), 79–88.

[40] Bernstein, J. A., Khodursky, A. B., Lin, P.-H., Lin-Chao, S., & Cohen, S. N. (2002). Global analysis of mRNA decay and abundance in *Escherichia coli* at single-gene resolution using two-color fluorescent DNA microarrays. *Proceedings of the National Academy of Sciences*, 99(15), 9697–9702.

[41] BioNumbers. (n.d.). Rate of translation by ribosome—*E. coli* (BNID 100059).

[42] Salis, H. M., Mirsky, E. A., & Voigt, C. A. (2009). Automated design of synthetic ribosome binding sites to control protein expression. *Nature Biotechnology*, 27(10), 946–950.

[43] Laursen, B. S., Sørensen, H. P., Mortensen, K. K., & Sperling-Petersen, H. U. (2005). Initiation of protein synthesis in bacteria. *Microbiology and Molecular Biology Reviews*, 69(1), 101–123.

[44] Kelly, J. R., Rubin, A. J., Davis, J. H., Ajo-Franklin, C. M., Cumbers, J., Czar, M. J., de Mora, K., Glieberman, A. L., Monie, D. D., & Endy, D. (2009). Measuring the activity of BioBrick promoters using an in vivo reference standard. *Journal of Biological Engineering*, 3, 4.

[45] iGEM Registry. (n.d.). Anderson promoter collection.

[46] Bintu, L., Buchler, N. E., Garcia, H. G., Gerland, U., Hwa, T., Kondev, J., & Phillips, R. (2005). Transcriptional regulation by the numbers: Models. *Current Opinion in Genetics & Development*, 15(2), 116–124.

[47] MacDonald, C. T., Gibbs, J. H., & Pipkin, A. C. (1968). Kinetics of biopolymerization on nucleic acid templates. *Biopolymers*, 6(1), 1–25.

[48] BioNumbers. (n.d.). Cell dry weight at doubling time of 30 min—*E. coli* (BNID 100009); Cell dry weight—*E. coli* (BNID 100008).

[49] Tomuro, K., Mito, M., Toh, H., Kawamoto, N., Miyake, T., Chow, S. Y. A., Doi, M., Ikeuchi, Y., Shichino, Y., & Iwasaki, S. (2024). Calibrated ribosome profiling assesses the dynamics of ribosomal flux on transcripts. *Nature Communications*, 15, 7061.

{% endblock %}


{% extends "layout.html" %}
{% include "heros/results-hero.html"%}
{% block page\_content %}

# Results

## Wet-Lab Results

### What we did (Build → Test)

* Initial transformations (7/30):
  We transformed synthetic ZnuABC (clonal vector) into TOP10 and DH5α alongside pUC19 controls using a
  standard heat-shock workflow (15 min ice → 42 °C 1 min → 5 min ice → SOC at 4× volume
  → 37 °C 45 min outgrowth).
  *Result:* pUC19 produced strong colonies; synthetic ZnuABC plates yielded few to no colonies (mostly failed).
* Retry (8/2):
  We repeated DH5α + iZnuABC with the same conditions.
  *Result:* no colonies.
* BL21 attempt (8/4):
  We tested BL21 + iZnuABC at two DNA inputs (1 µL and 10.5 µL at 4 ng/µL) with the same heat-shock and
  45 min outgrowth.
  *Result:* no colonies.

### How this aligns with our Engineering Success plan

* Our plan notes pUC19 success but no colonies for pUPD3::synthetic ZnuABC on chloramphenicol (Cam) plates,
  pinpointing Cam selection as the failure point.
* We flagged that bacteriostatic Cam is sensitive to outgrowth time and recommend ~60 min SOC with shaking
  (we ran 45 min), plus a Cam gradient (12.5/25/34/50 µg/mL) to test selection pressure.

### Interpretation

* The positive control verifies that our competent cells and workflow are sound; the systematic failure is
  specific to synthetic ZnuABC under Cam selection. The most likely causes are (a) Cam selection/outgrowth parameters and/or
  (b) vector integrity/quality, rather than a general transformation issue.
* Our 45 min outgrowth was likely insufficient for Cam (requires time for *cat* expression).
  Increasing to ~60 min with shaking is the evidence-based adjustment. Switching to BL21 did not rescue transformation,
  consistent with our guidance to stick with DH5α/TOP10 until the build is verified.

### What this means for Engineering Success

We completed a Build–Test–Learn cycle:

* Build: We ordered pUPD3::iZnuABC and performed transformations in multiple strains.
* Test: Controls worked; ZnuABC failed specifically under Cam selection.
* Learn: Extend Cam outgrowth, tune Cam concentration, verify plasmid integrity, and, if needed, electroporate.
  Then we can move to the Zincon uptake assay once we have confirmed colonies.

### Next steps to unblock the assay

1. Re-transform pUPD3::iZnuABC with SOC 60 min outgrowth (shaking); plate a
   Cam gradient (12.5/25/34/50 µg/mL) plus a no-antibiotic recovery plate.
2. Perform colony PCR across backbone/insert junctions on 2–4 colonies; archive glycerol stocks.
3. If efficiency remains low, electroporate high-quality DNA (validated by digest/gel), then repeat step 1.
4. With verified colonies, run the Zincon supernatant-depletion assay (AHL gradient; ZnSO₄ 25–50 µM; read 620 nm; no EDTA),
   including ΔznuB and empty-vector controls.

### Bottom line

Our controls succeeded, but the Cam-selected synthetic ZnuABC build did not. Our planned fixes include longer Cam outgrowth,
a Cam gradient, vector quality control, and potential electroporation. Once the build is stable, the Zincon assay will quantify
AHL-tunable Zn²⁺ uptake and complete our experimental validation.

*Source: Lab Notebook, iGEM - *Metlock* 2025*

---

## Dry-Lab Results

## Plot2Curve: Anderson Family Overview

Our iterative design process will be further developed by closely integrating computational
modeling with laboratory data acquisition. We will generate a series of predictive time-course
graphs from our model and systematically compare and contrast these outputs with experimental
results, specifically focusing on the kinetics of GFP variants due to their simplicity and
well-documented characterization. This baseline will allow us to accurately model various expression
rates. By iteratively incorporating new literature values and experimental data (e.g., promoter
strengths, RBS efficiencies, and fluorescent protein maturation rates) back into the model, we will
refine its parameters to achieve a strong relationship between graphical and numerical predictions.
This process will enable us to draw conclusions on the functional trade-offs and optimal use of
various promoters in genetic parts.

![](https://static.igem.wiki/teams/5759/ppfigs/overlaycomparison-8figs.webp)

**Figure 1:** This figure presents our dynamic overlay comparison of the gene expression kinetics
driven by 8 different BBa\_J23XX promoters over a 5000 second time course. The graph monitors the
accumulation of mRNA (solid lines, left y-axis) alongside the protein (dashed lines, right y-axis)
in molecules per cell. The data demonstrates the relative strengths of each promoter and matches
assumptions based off of literature regarding Anderson family promoters. J23100 exhibits the strongest
expression with a high steady state level for both mRNA (~10.5 molecules/cell) and protein (~12,000
molecules/cell) in the shortest time. Whereas J23113 exhibits the weakest and slowest expression of
both mRNA and protein. This data helps in developing synthetic circuits that require finely tuned
gene expression to help prevent leaky expression in downstream components or metabolic burden.

![](https://static.igem.wiki/teams/5759/ppfigs/proteincomparisonmrna.webp)

**Figure 2:** This figure provides a detailed side-by-side analysis of the J23100 and J23102
constitutive promoters. The Anderson J23100 promoter demonstrates a strong expression, reaching an
mRNA steady state of ~10.5 molecules/cell and a final protein level of ~12,000 molecules/cell. In
contrast, the J23102 promoter, a weaker variant, maxes out at a lower steady state of
~9 molecules/cell for mRNA and ~10,000 molecules/cell for protein. Both promoters quickly reach
steady state mRNA expression with a slower, sigmoidal accumulation of protein. These dynamics
match assumptions based off of literature regarding the dynamics of Anderson family promoters.

![](https://static.igem.wiki/teams/5759/ppfigs/proteinmaturationcomparison.webp)

**Figure 3:** This figure represents a kinetic model to predict fluorescent protein maturation
under the control of the strong Anderson promoters J23100 and J23102. The model separates total
protein into immature protein (recently translated, non-fluorescent) and mature protein (fully
folded and fluorescent). For both promoters, the immature protein pool rapidly reaches a pseudo-steady
state (~3500 molecules/cell for J23100; ~3000 for J23102), reflecting a balance between translation
and maturation rates. The mature protein pool accumulates continuously, illustrating that the observed
fluorescence lags behind actual transcription and translation. This simulated data follows what we
predict might be observed in a wet lab experiment.

![](https://static.igem.wiki/teams/5759/ppfigs/globalribosome.webp)

**Figure 4:** This figure shows how total mRNA levels and ribosome activity evolve over time
when the cell’s ribosomes are limited. Initially, the throttle factor (the fraction of free ribosomes
available for translation) starts high and drops rapidly — indicating that ribosomes quickly become
saturated by mRNA transcripts. As mRNA levels rise, ribosomes are increasingly burdened, causing a
global throttling effect where translation slows. Eventually, both the total mRNA and the throttle
factor reach steady-state; total mRNA near its equilibrium level, and global throttling near a low
value (~0.55–0.6), showing sustained ribosome competition. At KR=100, the system reaches a balance
where transcription continues but translation is limited by ribosome availability — a realistic
model for resource competition in cells.

![](https://static.igem.wiki/teams/5759/ppfigs/maturationlagvsbright.webp)

**Figure 5:** This scatter plot is a characterization of the fluorescent reporter's behavior for
various Anderson family promoter constructs. The y-axis displays the steady-state mature protein level
(a proxy for final brightness, in molecules/cell), while the x-axis quantifies the maturation lag (in
seconds), calculated as the difference between the time required to reach 50% of the maximum mature
protein (t50\_protein ) and the time required to reach 50% of the maximum mRNA (t50\_mRNA ). The data
provided from literature values will create a clear positive correlation between promoter strength
(brightness) and the maturation lag time. This figure highlights the importance of using kinetic
models to accurately correct protein maturation time when performing instantaneous measurements of
gene expression. The simulated model will eventually be compared to our measured values.

![](https://static.igem.wiki/teams/5759/ppfigs/brightnessandflour.webp)

**Figure 6:** Left: this panel confirms the ranking from the Maturation Lag vs. Brightness plot:
CustomStrong ≫ J23100 > J23102 > J23104, and so on. The CustomWeak and even weaker promoters
J23113–J23109 produce minimal brightness. Right: The bars look flat—and that can be misleading.
Time to reach 80% of each promoter’s own steady state is mostly set by the system’s built-in kinetics
(protein maturation and dilution by growth), not by how strong the promoter is, so strong promoters end brighter
but don’t reach “80%” much faster. Don’t read the flat bars as evidence of a translation or transcription bottleneck;
they reflect those shared time constants. For a speed comparison that does depend on promoter strength, use an absolute
threshold (e.g., “time to detectable fluorescence”), which will vary across promoters.

![](https://static.igem.wiki/teams/5759/ppfigs/detectiontimevsbrightness.webp)

**Figure 7:** This figure graphs the time (in seconds) required for minimum fluorescence
detection (at 1562 mature copies of sfGFP) versus the number of steady-state mature protein copies
(those that fluoresce) in the cell. In effect, a negative saturation trend is established between
the time required for recordable fluorescence data and the number of sfGFP proteins there are in the
cell. While promoters expressing high amounts of steady state proteins such as J23100 are detectable
much earlier than others, they, due to high amounts of protein production and folding, burden cell
machinery significantly more.

## Plot2Curve: AHL-Induction Simulation Overview

Our simulation framework was developed to explore the kinetics of quorum-sensing induction by
systematically modeling the effects of AHL (acyl-homoserine lactone) dosing on gene expression.
Using sfGFP and OD600 as a measurable output, we generate predictive time-course plots of inducer
dynamics, fluorescence production, and bacterial growth. These simulations allow us to test both
single-dose and scheduled-dose scenarios, providing a quantitative baseline for understanding EC50
values, Hill slopes, and the overall robustness of induction systems. By iteratively comparing these
computational predictions with experimental data, we refine the model to account for promoter
activity, RBS efficiency, and protein maturation rates. This process ensures that the simulation
not only captures general induction trends but also becomes a practical design tool for
evaluating circuit performance, trade-offs, and optimization strategies in quorum-sensing
applications.

![](https://static.igem.wiki/teams/5759/ppfigs/1.webp)

**Figure 1:**
This plot shows the single-well AHL potency simulation. The initial AHL concentration was set to
600,000 µM, and a scheduled dose of 1e10 µM was added when the optical density reached 1e-11. The
resulting curve demonstrates the sharp increase in AHL potency after dosing and its gradual decline
over time.

![](https://static.igem.wiki/teams/5759/ppfigs/2.webp)

**Figure 2:** This figure represents sfGFP fluorescence output as a function of time for the
same experimental parameters. Following the initial AHL spike, GFP expression rises sharply and then
plateaus at around 31 × 10^6 units. This plateau reflects the system’s steady-state response, where
the gene circuit has reached its maximal GFP output given the AHL induction conditions.

![](https://static.igem.wiki/teams/5759/ppfigs/3.webp)

**Figure 3:** This panel tracks simulated bacterial growth as OD600 for the same run
(600,000 µM initial AHL; a 1×10¹⁰ µM dose injected when OD600 reaches 1×10⁻¹¹). The curve exhibits:

- **Extended lag phase (t < ~600)** – low apparent OD as the population establishes.
- **Onset of exponential growth (~600–900)** – rapid, step-like increases as biomass accumulates.
- **Late-stage surge (> ~900)** – approaching the model’s carrying-capacity
  regime / non-linear growth limit by the end of the run.

Together with Figures 1–2, this shows how AHL induction coincides with growth dynamics: dosing
occurs at very low density, while sfGFP output rises as the culture enters exponential growth and
then plateaus.

![](https://static.igem.wiki/teams/5759/ppfigs/4.webp)

**Figure 4:** This figure shows the EC50 analysis for sfGFP as a function of the initial AHL
concentration, run over 100 time steps with no additional doses added. Each point represents the
final normalized GFP level for a given starting AHL value. A Hill curve is fitted to the data, shown
in black, with AHL concentrations displayed on a log scale (x-axis). The y-axis is normalized to
percentage of maximum GFP expression, illustrating the classic sigmoidal dose–response behavior
with a sharp transition around the EC50 point.

![](https://static.igem.wiki/teams/5759/ppfigs/5.webp)

**Figure 5:** This figure presents EC50 analysis where the response is measured against scheduled
AHL dosing events. The dosing follows the activation rule: [(5e-10, 1e6), (1e-9, 1e6), (5e-9, 1e6),
(1e-8, 1e6)], where the first value in each pair is the OD600 threshold that triggers the dose,
and the second value is the dose magnitude. The resulting data points are normalized GFP outputs,
fitted with a Hill curve (black line). The x-axis is logarithmic (dose trigger thresholds), and the
y-axis shows the percentage of maximum GFP, emphasizing the graded activation pattern driven by
scheduled dosing events.

![](https://static.igem.wiki/teams/5759/ppfigs/6.webp)

**Figure 6:** This example shows AHL potency in a single well over 600 simulation steps. The
trace reflects step-like increases when doses are triggered, followed by plateaus as AHL is consumed
or diluted. This mode of visualization provides per-well insight: the same panel can be used to
retrieve sfGFP and OD600 dynamics as alternative readouts for the exact same well.

{% endblock %}