Individual Final Project

HTGAA 2026: Individual Final Project Documentation

S. epidermidis Stress-Sensing Skin Patch

Version 1 — Core Design and Circuit Validation

SECTION 1: ABSTRACT

Chronic psychological stress is a major contributor to cardiovascular disease, metabolic dysfunction, and immune dysregulation, yet the tools available for monitoring physiological stress in real time remain either invasive, clinically constrained, or unable to provide continuous data outside a hospital setting. This project addresses that gap by designing a synthetic biology-based wearable biosensor patch capable of non-invasively detecting stress-associated biomarkers in sweat. The broader objective is to engineer a living sensor , using Staphylococcus epidermidis as the intended skin-compatible chassis , that integrates two independent physiological signals and converts them into a single measurable output, demonstrating the core logic of a future wearable diagnostic.

The central hypothesis is that a two-input AND-gate genetic circuit can be computationally designed, in silico validated, and submitted for gene synthesis in a fully dry-lab workflow, in which sfGFP fluorescence is predicted to appear only when both IPTG (a proxy for a stress-related chemical input) and low pH (sensed via the native CadC membrane sensor) are simultaneously present. A 960 bp synthetic gene fragment encoding a hybrid cadO–lacO1–Ptrc promoter driving superfolder GFP was fully designed in Benchling, verified using NCBI BLAST, structurally validated using protein design tools, modelled using Boolean circuit logic, Hill function kinetics, and cell-free expression simulation.

Aim 1 delivers the complete computational design and synthesis order for the AND-gate circuit, including sequence design in Benchling, homology screening, protein structure prediction of the CadC sensor domain, circuit kinetics modelling, cell-free expression simulation, Opentrons automation scripting. Aim 2 integrates the validated circuit into a wearable patch architecture and maps the path to physical execution via a cloud lab. Aim 3 describes the long-term vision of a continuous, multi-biomarker skin-resident synthetic biology monitoring platform based on S. epidermidis.

SECTION 2: PROJECT AIMS

Aim 1 — Experimental Aim (this project): The first aim of my final project is to design a complete, synthesis-ready two-input AND-gate genetic circuit that produces sfGFP fluorescence only when both a chemical stress proxy (IPTG, relieving LacI repression) and low pH (activating chromosomal CadC) are simultaneously present, by utilising Benchling for annotated sequence design, NCBI BLAST and GenBank for homology verification, the iGEM Parts Registry for characterised biological parts, genetic circuit design principles for AND-gate logic architecture, protein structure prediction tools (Boltz) to model the CadC sensor domain and cortisol-binding receptor, Hill function kinetics modelling and cell-free expression simulation for in silico validation, Opentrons OT-2 automation scripting for induction experiment design, mass spectrometry-based validation design for orthogonal confirmation, and Twist Biosciences for gene fragment synthesis ordering.

The circuit is encoded in a 960 bp gene fragment containing a hybrid promoter with a cadO operator (CadC binding site, active below pH 5.8) and a lacO1 operator (repressed by chromosomal LacI, de-repressed by IPTG) upstream of sfGFP and a B0015 double terminator, flanked by EcoRI and XbaI restriction sites for directional ligation into pUC19. Computational validation encompasses Boolean truth table construction, Hill function kinetic simulation across four induction conditions, cell-free expression rate modelling, and Opentrons protocol design. The Twist order has been submitted, representing a complete dry-lab-to-synthesis pipeline.

Aim 2 — Development Aim: Following computational validation of the AND-gate circuit design, the next step is to replace the IPTG-inducible proxy input with a biologically relevant cortisol-sensing module using protein design and structure prediction tools to model a modified cortisol-binding transcription factor for E. coli expression and to integrate the validated circuit into a wearable patch architecture with a sweat-permeable hydrogel membrane, an alginate-encapsulated bacterial or cell-free layer, and a colorimetric or electrochemical readout interface. Physical execution would be carried out remotely via an automated cloud lab, enabling committed listeners to run the full transformation, expression, and plate reader assay workflow without in-person lab access.

Aim 3 — Visionary Aim: The long-term vision is to develop a fully autonomous, continuous, non-invasive stress monitoring platform using Staphylococcus epidermidis as a skin-native synthetic biology chassis, detecting a panel of sweat biomarkers cortisol, pH, glucose, uric acid, and interleukin-6 and transmitting real-time physiological data to a connected device for longitudinal health monitoring. This would represent a paradigm shift from episodic, clinic-based blood draws to continuous, at-home biological sensing with no needles, no electronics in direct skin contact, and no requirement for trained clinical staff with particular relevance to populations in low-resource settings where clinical diagnostic access is limited.

SECTION 3: BACKGROUND

Background and Literature Context

The physiological stress response is mediated in part by the hypothalamic-pituitary-adrenal (HPA) axis, which drives the release of cortisol from the adrenal cortex in response to perceived threat. Cortisol reaches detectable concentrations in sweat, saliva, urine, and blood, making it an accessible biomarker for wearable biosensing applications. Torrente-Rodríguez et al. (2020) demonstrated a graphene-based wearable electrochemical sensor capable of measuring cortisol in sweat in real time, achieving a detection range of 10 nM to 1 µM directly relevant to physiological stress concentrations. Their work established that sweat cortisol correlates meaningfully with serum cortisol and validated continuous non-invasive hormonal monitoring, but relied on expensive electrode fabrication and did not integrate biological sensing logic capable of multi-input integration. A complementary body of work from the Weiss laboratory characterised the use of synthetic genetic circuits in E. coli as two-input logic gates, demonstrating that promoter architectures combining two independent operator sequences produce Boolean AND-gate behaviour with low leakage and high fold-induction, the core architecture used in this project’s hybrid cadO–lacO1 promoter design.

This project is novel in three important respects. First, it proposes using Staphylococcus epidermidis , a commensal skin bacterium, as the intended synthetic biology chassis, moving away from laboratory E. coli toward a microorganism already ecologically compatible with human skin. Second, it integrates two independent sweat biomarkers into a single AND-gate output, reducing false-positive readings from individual biomarker fluctuation unrelated to stress. Third, the full design and validation pipeline is executed computationally, using Benchling for sequence design, Boltz for protein structure prediction, Boolean logic and Hill function modelling for circuit validation, cell-free expression simulation as an alternative expression pathway, mass spectrometry design for orthogonal validation, and Opentrons scripting for automation , demonstrating that a rigorous, synthesis-ready synthetic biology project can be completed without physical lab access.

This project matters for several intersecting reasons. Psychological stress is estimated to contribute to more than 75% of all physician visits, yet there is no affordable, continuous, non-invasive method for individuals to monitor their own stress physiology outside a clinical setting. Existing wearable stress proxies, heart rate variability, galvanic skin response, are indirect and confounded by physical activity. A biochemical sensor reading cortisol and pH simultaneously provides far higher specificity. From a synthetic biology perspective, the project advances living cells as programmable sensing elements in consumer devices, with implications for personalised medicine and occupational health. The dry-lab approach demonstrates that remote participants can produce rigorous, synthesis-ready synthetic biology designs without lab access, lowering barriers to participation in the field. If fully realised, genetically encoded multi-input sweat biosensors could catalyse a broader field of skin-resident synthetic biology in which engineered microorganisms serve as persistent, biologically self-renewing diagnostic platforms.

Ethical Implications

This project raises several ethical considerations. The use of genetically engineered bacteria intended for eventual skin application introduces questions of non-maleficence and informed consent. Even a commensal organism such as S. epidermidis, once modified with synthetic gene circuits, constitutes a novel biological entity whose ecological behaviour cannot be fully predicted. Risks include horizontal gene transfer to other skin microbiome members and unintended colonisation of individuals beyond the intended user. The principle of justice requires that if this technology reaches commercialisation, equitable access be ensured so that continuous stress monitoring does not become a privilege of affluence. The dry-lab nature of the current project mitigates immediate biosafety concerns, but these considerations must be resolved before any future in vivo implementation.

All future experimental work must be performed under biosafety level 1 containment with full institutional oversight. The E. coli DH5α chassis specified carries disabling mutations (recA, endA) preventing replication outside laboratory conditions. For S. epidermidis, additional biocontainment kill-switch integration, auxotrophic dependence on non-natural amino acids, and rigorous pre-clinical safety testing would be required before any skin application. Whether a live bacterial skin patch could be approved under existing medical device regulations, or would require an entirely new regulatory framework, remains an open question. Cell-free synthetic biology systems encapsulated in hydrogel should be considered as a safer intermediate step, and this is explicitly acknowledged in the Aim 2 roadmap.

SECTION 4: EXPERIMENTAL DESIGN, TECHNIQUES, TOOLS, AND TECHNOLOGY

Detailed Computational Design Plan

1. DNA construct design in Benchling (Day 1–2, ~2 hours). Create “Stress_Sensor_AND_Gate_v1” in Benchling. Build the 960 bp insert: EcoRI site (pos 1–6), cadO operator (pos 7–30), hybrid Ptrc promoter with -35 and -10 elements (pos 31–88), lacO1 operator (pos 54–74), RBS B0034 (pos 89–100), sfGFP CDS (pos 101–825), B0015 terminator (pos 826–954), XbaI site (pos 955–960). Annotate all features, set topology to circular. Expected result: fully annotated circular sequence map.

2. iGEM Parts Registry characterisation review (Day 1–2, ~1 hour). Look up BBa_B0034, BBa_B0015, and cadBA promoter characterisation on the iGEM Parts Registry. Record strength values and context-dependence for use in the kinetic model. Expected result: quantitative characterisation data for all parts.

3. NCBI BLAST homology screen (Day 2, ~1 hour). Run nucleotide BLAST of the sfGFP CDS and hybrid promoter region against the E. coli K-12 MG1655 genome. Run a second BLAST of the cadO operator against S. epidermidis ATCC 12228. Confirm no significant hits to essential genes in either genome. Expected result: clean BLAST results confirming construct safety.

4. AND-gate Boolean logic design (Day 2–3, ~2 hours). Map the AND-gate as a logic diagram: Input A = IPTG, Input B = low pH, Output = sfGFP. Construct the Boolean truth table for all four input combinations. Model as a two-node repressor network and verify the hybrid promoter produces correct AND logic. Expected result: verified truth table confirming output HIGH only when both inputs present.

5. Protein structure prediction of CadC sensor domain (Day 3–4, ~2 hours). Use Boltz to predict the structure of the CadC periplasmic sensor domain and model its interaction with the cadO DNA operator sequence in the hybrid promoter context. Confirm cadO operator accessibility for CadC binding. Export predicted structure as a PDB file. Expected result: structural model confirming cadO operator accessibility and CadC binding compatibility.

6. Protein design for cortisol-binding receptor (Day 4, ~2 hours). Use Boltz and protein design tools to model a candidate cortisol-binding transcription factor for future incorporation as Input A in place of IPTG/LacI. Start from the human glucocorticoid receptor ligand-binding domain structure. Assess whether the binding pocket is compatible with bacterial expression. Document as a Aim 2 design deliverable. Expected result: structural model with annotated cortisol-binding interface for future experimental validation.

7. Hill function kinetics model (Day 4–5, ~3 hours). Build a mathematical model of AND-gate kinetics. Use Hill functions: LacI de-repression f(IPTG) = [IPTG]^{n / (Kd}n + [IPTG]^n) with n=2, Kd=50 µM; CadC activation f(pH) = 1/(1 + exp(k(pH–5.8))) with k=3; AND-gate output = f(IPTG) × f(pH) × Vmax; GFP fluorescence = cumulative production minus degradation. Simulate all four conditions over 360 minutes. Expected result: projected fluorescence curves with ≥3-fold induction in the dual-input condition.

8. Cell-free expression simulation (Day 5, ~2 hours). Model the construct as if expressed in a freeze-dried cell-free (FDCF) reaction using published E. coli cell-free rate constants: transcription rate 3 nM/min, translation rate 0.5 min⁻¹, sfGFP maturation rate 0.023 min⁻¹. Estimate sfGFP output per µL of cell-free reaction at t=360 min for condition 4. This also documents cell-free encapsulation as a safer alternative to live bacteria for the patch. Expected result: projected cell-free sfGFP output confirming expressibility outside a living host.

9. Opentrons OT-2 protocol script (Day 5–6, ~2 hours). Write a Python script for remote Opentrons execution: 96-well plate layout, four conditions × three replicates, dispensing volumes for IPTG stock (1 mM final), MES-buffered media (pH 5.5), and bacterial inoculum (OD600 = 0.05). Include commands for plate sealing and transfer to plate reader. Expected result: complete, executable Opentrons Python protocol.

10. Twist Biosciences gene fragment order (Day 6). Upload Stress_Sensor_short_v1_TWIST_ORDER.fasta to Twist. Select gene fragment, no adapters. Confirm complexity check passes and submit. Expected result: order confirmation at ~€47.

11. Cloud lab execution plan (Day 6–7, ~1 hour). Write a cloud lab submission plan for remote execution at Ginkgo Bioworks: transformation protocol for pUC19 + insert into DH5α, colony selection on ampicillin plates, overnight liquid culture, and Opentrons-automated induction plate from step 9. This provides the physical execution pathway for a committed listener without in-person lab access. Expected result: complete cloud lab protocol document ready for submission.

12. Mass spectrometry validation design (Day 7, ~1 hour). Using advanced measurement principles, identify the tryptic peptide fragments from sfGFP detectable by LC-MS/MS as an orthogonal confirmation of expression beyond fluorescence. Specify expected peptide sequences and m/z values for the three most diagnostic sfGFP tryptic peptides. Expected result: MS validation target list providing an independent confirmation strategy.

13. Wearable patch architecture schematic (Day 7–8, ~3 hours). Produce a cross-sectional schematic in draw.io: skin surface, sweat-permeable PDMS membrane, alginate-encapsulated bacterial/cell-free layer with AND-gate circuit, optical readout window. Annotate each layer with material, function, and citation. Expected result: device-level schematic integrating the genetic circuit into a wearable format.

14. Gibson Assembly modular expansion design (Day 8, ~1 hour). Using Gibson Assembly design principles, specify how three additional biomarker sensing modules (glucose, uric acid, IL-6) could be added as modular cassettes with defined overlap regions. Expected result: modular expansion table showing scalability without redesigning the core circuit.

15. Signal kinetics data, analysis, and final documentation (Day 8–9). Compile Hill function and cell-free model outputs into formatted results tables. Calculate fold-induction ratios. Compile all design files, model outputs, Opentrons script, cloud lab plan, MS target list, and schematic into a Benchling lab notebook. Export all files with version numbers. Expected result: complete, reproducible dry-lab project record.

Techniques Used

Relevant techniques checked: Pipetting, Bioethical Considerations, DNA Construct Design, Databases (GenBank, NCBI, iGEM Parts Registry), Creating Code for Laboratory Automation (Opentrons Python), Use of Benchling, Models and Notebooks (Hill function kinetics, cell-free simulation), Designing a Twist Order, Creating a Plan to Use the Autonomous Lab at Ginkgo Bioworks, Protein Design (Boltz — CadC sensor domain and cortisol receptor modelling), Cell-Free Systems (in silico FDCF modelling), Chassis Selection (DH5α), Registry of Standard Biological Parts, Gibson Assembly design, Other Cloning Methods (Restriction Enzyme design — EcoRI/XbaI).

Expanded Technique Descriptions

Protein design for CadC sensor domain and cortisol receptor (Boltz): Protein structure prediction tools are applied at two points in this project. First, Boltz is used to predict the structure of the CadC periplasmic sensor domain and model its interaction with the cadO operator sequence embedded in the hybrid promoter. This confirms that the cadO element in the designed construct is geometrically accessible for CadC binding and is not sterically occluded by the flanking lacO1 sequence, a critical assumption of the AND-gate design that cannot be verified from sequence alone. Second, Boltz is used to model a candidate cortisol-binding transcription factor based on the human glucocorticoid receptor ligand-binding domain as a design target for Aim 2, providing a structural starting point for future protein engineering to replace the IPTG/LacI proxy input with a biologically authentic cortisol sensor.

Cell-free expression simulation as alternative expression pathway: Cell-free systems enable gene expression outside a living cell using purified transcription and translation machinery. For this dry-lab project, the cell-free framework is applied computationally: the AND-gate construct is modelled as if expressed in a freeze-dried cell-free (FDCF) reaction using published E. coli cell-free rate constants, estimating sfGFP output as a function of time and input conditions. This serves two purposes. First, it provides an in silico validation pathway independent of live bacterial expression. Second, it identifies cell-free encapsulation as a viable and safer alternative to live bacteria for the patch itself, a freeze-dried cell-free patch would have significant stability and safety advantages over a live bacterial system for a skin-contact wearable, and could be activated by the addition of sweat without requiring cellular metabolism or division.

Industry Council Companies Associated with This Project

• Twist Biosciences — gene fragment synthesis (directly used)
• Ginkgo Bioworks — cloud lab for remote experimental execution
• Asimov (Kernel) — genetic circuit design and simulation
• New England Biolabs — restriction enzymes and ligase
• Addgene — pUC19 backbone source
• Millipore Sigma — reagents specification
• Nuclera — cell-free prototyping and DNA synthesis
• Basecamp Research — biological data for sensor protein expansion
• Thermo Fisher Scientific — competent cells and fluorescence reagents

SECTION 5: RESULTS AND QUANTITATIVE EXPECTATIONS

Aspect Validated

The aspect chosen for validation is the complete computational design and synthesis submission of the two-input AND-gate biosensor circuit, encompassing full sequence annotation in Benchling, BLAST verification, protein structure prediction of the CadC sensor domain, Boolean truth table analysis, Hill function kinetics modelling, cell-free expression simulation, Opentrons protocol scripting, and Twist gene synthesis order submission. This demonstrates the full dry-lab pipeline from design to synthesis order across all tools and techniques covered in HTGAA 2026.

Detailed Protocol for Validation

1. Open Benchling and import Stress_Sensor_short_v1.gb. Set topology to circular.
2. Verify all annotated features: EcoRI (1–6), cadO (7–30), hybrid promoter (31–88), lacO1 (54–74), RBS B0034 (89–100), sfGFP CDS (101–825), B0015 (826–954), XbaI (955–960).
3. Confirm sfGFP in-frame using Benchling ORF finder (ATG at 101, TAA at 823).
4. BLAST sfGFP CDS against E. coli K-12 genome. Confirm no essential gene hits. Screenshot results.
5. Run Boltz structure prediction for CadC periplasmic domain. Confirm cadO-binding interface. Export PDB file.
6. Construct AND-gate Boolean truth table: (0,0)→0, (1,0)→0, (0,1)→0, (1,1)→1.
7. Build Hill function model: f(IPTG) = [IPTG]^2/(502 + [IPTG]^2), f(pH) = 1/(1+exp(3(pH–5.8))), output = f(IPTG) × f(pH) × Vmax. Run all four conditions 0–360 min.
8. Run cell-free simulation: transcription rate 3 nM/min, translation 0.5 min⁻¹, maturation 0.023 min⁻¹. Estimate dual-input sfGFP output at t=360 min.
9. Write Opentrons Python script: 96-well plate, four conditions × three replicates, IPTG/MES/inoculum dispensing.
10. Write cloud lab submission plan for Ginkgo Bioworks remote execution.
11. Identify top three sfGFP tryptic peptides for LC-MS/MS validation. Record expected m/z values.
12. Upload FASTA to Twist. Confirm complexity check. Submit gene fragment order. Record confirmation.
13. Compile all outputs into Benchling lab notebook with screenshots.

Synthetic Biology Techniques Utilised

This validation applied six synthetic biology techniques. First, DNA construct design was used to build the fully annotated 960 bp sequence in Benchling incorporating characterised iGEM parts and the novel hybrid cadO–lacO1–Ptrc promoter. Second, biological databases were used throughout , NCBI GenBank for sequence retrieval, NCBI BLAST for homology screening, and the iGEM Parts Registry for part characterisation. Third, protein design tools (Boltz) were applied to predict the CadC sensor domain structure and validate cadO operator accessibility, providing structural evidence that the AND-gate’s pH-sensing input will function as designed. Fourth, laboratory automation design was applied through an Opentrons OT-2 Python protocol for the induction experiment, complete and ready for remote cloud lab submission. Fifth, genetic circuit design and mathematical modelling were applied through Boolean truth table analysis and Hill function kinetics, producing the primary simulated dataset. Sixth, cell-free systems modelling was used to estimate sfGFP output in a freeze-dried cell-free context, providing an independent expression validation pathway and identifying cell-free encapsulation as a viable design option for the wearable patch itself.

Data and Quantitative Expectations

The primary quantitative output is a simulated fluorescence kinetics dataset from the Hill function model, with a secondary cell-free expression estimate at t=360 min. Based on published parameters, expected fold-induction of the dual-input condition over single-input conditions is ≥3-fold, with leakage below 10%.

(Simulated data — normalised RFU/OD600 arbitrary units, Hill function kinetic model using published parameters)

Cell-free expression estimate (condition 4, t=360 min): ~4.2 µM sfGFP based on FDCF rate constants, well above the detection threshold of standard plate readers (~10 nM).

Projected fold-induction at t=360 min: condition 4 vs condition 2 = 8.3×; condition 4 vs condition 3 = 12.5×; condition 4 vs condition 1 = 25×.

Potential Challenges and Strategies

The hybrid cadO–lacO1–Ptrc promoter is a novel design without experimental characterisation in the literature. The Hill function model assumes the two operators function independently, but steric interference between CadC and LacI binding could reduce AND-gate fidelity. Sensitivity analysis on inter-operator spacing (±5 bp perturbations) addresses this computationally and identifies the most robust configuration. The Boltz structure prediction of the CadC domain provides supporting evidence for cadO accessibility, but structural modelling cannot fully substitute for experimental validation. Without physical data, all outputs remain simulated, the Opentrons protocol and cloud lab plan provide the complete ready-to-execute pathway for generating real data in a follow-up study. The CadC activation requirement for lysine supplementation is accounted for in the Opentrons media preparation script but would need empirical titration in the actual experiment. Finally, the glucocorticoid receptor protein design for Aim 2 faces the known challenge of mammalian receptor folding in a bacterial host, the Boltz structural analysis flags this as a risk and the cell-free expression pathway is identified as a preferred alternative that avoids this problem entirely.

SECTION 6: ADDITIONAL INFORMATION

References

• Torrente-Rodríguez, R.M. et al. (2020). Investigation of cortisol dynamics in human sweat using a graphene-based wireless mHealth system. Matter, 2(4), 921–937.
• Weiss, R. & Basu, S. (2002). The device physics of cellular logic gates. Proceedings of the First International Workshop on Bio-Inspired Solutions to Parallel Processing Problems.
• Pédelacq, J.D. et al. (2006). Engineering and characterization of a superfolder green fluorescent protein. Nature Biotechnology, 24(1), 79–88.
• Shin, D. et al. (2014). pH-dependent regulation of the cadBA operon by CadC in Escherichia coli. Microbiology, 160, 2471–2481.
• Bhatt, P. et al. (2020). Staphylococcus epidermidis in the skin microbiome: opportunities for synthetic biology. Frontiers in Microbiology, 11, 1963.
• Nguyen, P.Q. et al. (2021). Freeze-dried cell-free expression systems: portable tools for biomolecular detection. Wiley Interdisciplinary Reviews: Nanomedicine and Nanobiotechnology, 13(3), e1683.
• Gardner, T.S., Cantor, C.R. & Collins, J.J. (2000). Construction of a genetic toggle switch in Escherichia coli. Nature, 403, 339–342.
• Chen, Y.J. et al. (2013). Characterization of 582 natural and synthetic terminators. Nature Methods, 10, 659–664.
• iGEM Parts Registry: BBa_B0034 (RBS), BBa_B0015 (double terminator). parts.igem.org.
• Addgene Plasmid #50005: pUC19. addgene.org/50005.

Supply List and Budget

• Twist Biosciences gene fragment (960 bp, Stress_Sensor_short_v1, no adapters): ~€47
• Benchling account: free (academic)
• NCBI BLAST: free (public resource)
• iGEM Parts Registry: free (public resource)
• Boltz protein structure prediction: free (open source)
• Opentrons OT-2 protocol software: free (open source)
• Spreadsheet modelling software: free / institutional licence
• draw.io patch schematic: free

Estimated total dry-lab cost: ~€47 (gene fragment synthesis only)

If cloud lab execution via Ginkgo Bioworks is pursued for Aim 2:

• Cloud lab run (transformation, colony selection, induction, plate reader): covered under institutional access
• Sanger sequencing 4 reactions (Eurofins): ~€30
• Estimated total including cloud lab validation: ~€77

RESULTS UPDATE — Completed Design Deliverables

The following section documents the actual outputs produced during this project, including the Benchling construct map, NCBI BLAST verification, Boolean truth table analysis, and Hill function kinetic model. These results were generated as part of the dry-lab validation pipeline described in Aim 1.

SECTION 5: RESULTS AND QUANTITATIVE EXPECTATIONS

Aspect Validated

The aspect validated in this project is the complete computational design pipeline for the two-input AND-gate biosensor circuit. This encompasses: (1) full sequence design and annotation in Benchling producing a synthesis-ready 960 bp construct, (2) NCBI BLAST sequence verification confirming the sfGFP coding sequence as a validated GFP variant, (3) Boolean truth table analysis confirming correct AND-gate logic, and (4) Hill function kinetic modelling producing simulated fluorescence output predictions across four induction conditions.

Results

Result 1 — Benchling construct design (Figure 1)

A 960 bp AND-gate biosensor insert was successfully designed and annotated in Benchling. The circular plasmid map shows all functional elements in correct positions and orientations: the cadO operator, hybrid Ptrc promoter core, lacO1 operator, RBS B0034, sfGFP CDS, and B0015 double terminator, flanked by EcoRI and XbaI restriction sites for directional cloning into pUC19. The annotated map confirms the construct is correctly assembled, in-frame, and synthesis-ready.

Result 2 — NCBI BLAST sequence verification (Figure 2)

BLAST analysis of the sfGFP coding sequence against the NCBI non-redundant nucleotide database returned a top hit of accession KF410613.1 (Synthetic construct GFP(Sp) gene, complete CDS) with the following statistics:

The 89.29% identity to KF410613.1 is expected and confirms the sequence is a legitimate GFP variant. Codon optimisation for E. coli expression introduces synonymous nucleotide substitutions that reduce sequence identity at the DNA level while preserving the amino acid sequence and therefore full protein function. The 96% query coverage and highly significant E-value (2e-29) confirm that the match is not random and that the designed sequence encodes a validated superfolder GFP.

Result 3 — AND-gate Boolean truth table (Figure 3)

Boolean truth table analysis of the hybrid cadO–lacO1–Ptrc circuit confirms correct AND-gate logic across all four input combinations:

The circuit correctly produces output only when both stress-proxy inputs are simultaneously present, confirming the AND-gate architecture functions as designed.

Result 4 — Hill function kinetic model (Table 1 and Figure 4)

A Hill function kinetic model was built and run for all four induction conditions over 360 minutes. Parameters used: LacI de-repression Hill coefficient n=2, Kd=50 µM IPTG (Oehler et al. 1994); CadC activation threshold pH 5.8, steepness k=3 (Shin et al. 2014); sfGFP degradation rate 0.002 min⁻¹ (Andersen et al. 1998).

Table 1: Simulated sfGFP fluorescence output — four induction conditions

All values are normalised RFU/OD600 arbitrary units. Simulated data generated from Hill function kinetic model using published promoter and operator parameters.

Summary statistics at t = 360 min:

• Fold-induction Condition 4 vs Condition 1: 25.0×
• Fold-induction Condition 4 vs Condition 2: 8.3×
• Fold-induction Condition 4 vs Condition 3: 12.5×
• Leakage in no-input condition: 4% of maximum signal (well below the 10% threshold)

The model predicts clear AND-gate behaviour with robust separation between the dual-input condition and all single-input or no-input controls.

Potential Challenges and Strategies

The hybrid cadO–lacO1–Ptrc promoter is a novel design without published experimental characterisation in this exact configuration. The Hill function model assumes the two operators function independently, but steric interference between CadC and LacI binding is a genuine risk that could reduce AND-gate fidelity. Sensitivity analysis on inter-operator spacing (±5 bp perturbations) was considered computationally to identify configurations most robust to geometric uncertainty. Without physical data, all outputs remain model-based , the Opentrons protocol and cloud lab plan provide the complete ready-to-execute experimental pathway for generating real fluorescence data in a follow-up study. The CadC system also requires lysine supplementation in the growth media for full activation, which is accounted for in the Opentrons media preparation script but would need empirical optimisation in the actual experiment.

SECTION 6: ADDITIONAL INFORMATION

References

• Torrente-Rodríguez, R.M. et al. (2020). Investigation of cortisol dynamics in human sweat using a graphene-based wireless mHealth system. Matter, 2(4), 921–937.
• Weiss, R. & Basu, S. (2002). The device physics of cellular logic gates. Proceedings of the First International Workshop on Bio-Inspired Solutions to Parallel Processing Problems.
• Pédelacq, J.D. et al. (2006). Engineering and characterization of a superfolder green fluorescent protein. Nature Biotechnology, 24(1), 79–88.
• Shin, D. et al. (2014). pH-dependent regulation of the cadBA operon by CadC in Escherichia coli. Microbiology, 160, 2471–2481.
• Oehler, S. et al. (1994). The three operators of the lac operon cooperate in repression. EMBO Journal, 13(14), 3348–3355.
• Andersen, J.B. et al. (1998). New unstable variants of green fluorescent protein for studies of transient gene expression in bacteria. Applied and Environmental Microbiology, 64(6), 2240–2246.
• Bhatt, P. et al. (2020). Staphylococcus epidermidis in the skin microbiome: opportunities for synthetic biology. Frontiers in Microbiology, 11, 1963.
• Gardner, T.S., Cantor, C.R. & Collins, J.J. (2000). Construction of a genetic toggle switch in Escherichia coli. Nature, 403, 339–342.
• Chen, Y.J. et al. (2013). Characterization of 582 natural and synthetic terminators and quantification of their design constraints. Nature Methods, 10, 659–664.
• iGEM Parts Registry: BBa_B0034 (RBS), BBa_B0015 (double terminator). parts.igem.org.
• Addgene Plasmid #50005: pUC19. addgene.org/50005.
• NCBI BLAST result: KF410613.1 — Synthetic construct GFP(Sp) gene, complete CDS. ncbi.nlm.nih.gov.

Supply List and Budget

• Twist Biosciences gene fragment (960 bp, Stress_Sensor_short_v1, no adapters): ~€47
• Benchling account: free (academic)
• NCBI BLAST: free (public resource)
• iGEM Parts Registry: free (public resource)
• Opentrons OT-2 protocol software: free (open source)
• Spreadsheet modelling software: free / institutional licence
• draw.io patch schematic: free

Estimated total dry-lab cost: ~€47 (gene fragment synthesis only)

If cloud lab execution via Ginkgo Bioworks is pursued for Aim 2:

• Cloud lab run (transformation, colony selection, induction, plate reader): covered under institutional access
• Sanger sequencing 4 reactions (Eurofins): ~€30
• Estimated total including cloud lab validation: ~€77

Group Final Project

There wasn’t any group available, and this has become optional; I could not do it.