Four Constructs — HTGAA-Week-6-GeneticCircuits
All four genetic circuit constructs were designed and simulated in Asimov Kernel under the repository HTGAA-Week-6-GeneticCircuits — [Nicolás-Escobar]. Parts were taken from the Kernel library. The notebook export and simulator output are documented below.
⬇ Download Asimov Kernel Notebook (CSV)
After completing all three simulations (Toggle Switch, NOT Gate, AND Gate), a recurring pattern emerges: immediate convergence toward constant transcription levels rather than dynamic switching behavior. This reflects a structural limitation of the Kernel's deterministic ODE solver: default E. coli kinetic parameters place all three constructs in a regime of parameter-induced stability, where RNA levels reach steady state before the logical transitions can be observed. The discrepancy between expected ON/OFF dynamics and simulated flat lines does not indicate design failure — it reveals that the simulator's default parameters do not capture the transient states that protease-mediated degradation and cell dilution would produce in a living cell. Each construct is analyzed individually below with this constraint explicitly documented.
Upon comparing simulation results with the reference Repressilator from the Kernel Bacterial Demos Repository, three critical factors prevent the emergence of limit-cycle oscillations in this construct:
1. Lack of targeted proteolysis (LVA tags). The primary discrepancy stems from the absence of SsrA-mediated degradation tags (LVA) in the available biological parts. In the original Elowitz Repressilator, these tags are essential to increase the protein decay rate (δ_p). Without active degradation, repressor proteins accumulate and "saturate" the system — mathematically, the system reaches a stable fixed point rather than entering a sustained oscillation, because protein levels never drop fast enough to allow the subsequent promoter to disinhibit.
2. Deterministic vs. stochastic modeling. The Kernel uses a deterministic ODE solver. In such models, if parameters (transcription rates, dissociation constants, Hill coefficients) are not perfectly balanced to trigger a Hopf Bifurcation, the system inevitably collapses into a stable equilibrium. In real biological systems, molecular noise (stochasticity) often "kicks" the system into an oscillatory state — a feature absent in this rigid simulation.
3. Tightness of transcriptional control. The reference model relies on highly non-linear cooperative binding (Hill coefficient n > 2). If promoters R0040, R0051, and R00100 exhibit leaky expression or insufficient cooperativity within the simulator's underlying math, the negative feedback loop becomes too weak to overcome the inherent stability of the system.
Conclusion: While the genetic architecture is topologically identical to the reference model (a 3-node ring oscillator), the simulation fails to oscillate due to parameter-induced stability. The high stability of the repressor proteins and the deterministic nature of the solver result in a damped response that settles at a fixed point, rather than the sustained limit-cycle observed in the demo repository.
The three-node cyclic repression topology of the Repressilator (LacI → TetR → cI → LacI) is mathematically analogous to the three-strain circular auxotrophic ring in Füzi Poiesis (A requires Leu from C → B requires Trp from A → C requires His from B → closes). Both exhibit the property that disrupting any single node propagates through the entire cycle. The critical difference: in the Repressilator, disruption collapses oscillation; in the auxotrophic ring, it triggers cascade extinction — confirmed by the Aim 1 ODE model (Re(λ_max) ≈ −0.215, interior fixed point n* ≈ 0.629). The repressilator's failure to oscillate under parameter-induced stability is also instructive for Füzi Poiesis: the auxotrophic ring's stability properties depend on the same Monod kinetic parameters that must be experimentally calibrated for Halomonas elongata in Aim 2.
The Toggle Switch simulation demonstrates genuine bistable behavior: in the presence of IPTG (Ligand 1 added at hour 0), LacI is inhibited and TetR is expressed — but here, IPTG activates pLacI's repression of TetR, causing the LacI-expressing module (Module 1: pTetR → LacI) to dominate. The steady-state RNA level stabilizes at ~0.32 relative units for BBa_C0012 (LacI transcript), while the second module remains repressed.
Unlike the Repressilator, the flat lines here match the theoretical expectation. A genetic switch should maintain one stable state — cellular memory. The system is in a LacI-dominant state, maintaining ~0.32 RNA concentration while effectively repressing TetR expression. The RNAP flux bar chart confirms that only Module 1 has significant flux at the last time point, with Module 2 (pLacI → TetR) at near-zero — demonstrating correct toggle architecture. The system can be switched to the TetR-dominant state by a transient aTc pulse, which sequesters LacI and releases pTetR.
The Toggle Switch encodes the same bistable logic that underlies MazE/MazF antitoxin/toxin kill switches — considered in early iterations of Füzi Poiesis before auxotrophic coupling replaced active kill switches as the primary containment mechanism. A genetic toggle switch maintains containment in one of two states but can switch to the "escape" state under mutation pressure. The three-strain auxotrophic ring eliminates this failure mode: it has no "OFF" state to escape to — disrupting any node triggers cascade extinction (Ring collapses all 3; Pair loses only 1), as demonstrated by the Aim 1 cascade extinction simulation.
When IPTG is present (input = 1), pTac is activated and TetR is expressed, which should repress pTet and turn GFP (output) off. When IPTG is absent (input = 0), pTac is not active, TetR is not produced, and GFP is expressed. This is the correct NOT gate logic.
The simulation shows flat RNA concentration (~0.30) with no visible inversion response at IPTG addition (hour 20). Although the genetic architecture is topologically correct, the simulation demonstrates a signal propagation failure. The failure lies in the inducer's inability to generate a critical concentration of TetR that would shift the equilibrium of the second promoter. This result highlights the importance of force-tuning (RBS strengths and promoter strengths) in the design of bistable logic circuits — and is consistent with the biostability challenge observed across all three constructs.
The RNAP flux bar chart at the last time point shows GFP (BBa_E0040) as the dominant active element, confirming that the TetR-mediated repression of pTet was not strong enough to shut off GFP under the simulator's default kinetic parameters.
The AND Gate should produce GFP output only when both IPTG and aTc are simultaneously present. Each input activates the expression of a different repressor; only when both repressors are absent (no inducer present) is the output inhibited. The simulation ran with IPTG added at hour 20 and aTc at hour 35.
The resulting simulation shows immediate reporter saturation — two flat lines at ~0.50 (GFP, BBa_E0040) and ~0.32 (BM3R1_PRIMG transcript) from hour 0, with no visible steps or changes in slope at hours 20 or 35. This is consistent with the biostability challenge: the system is in a state of total saturation where the rate at which GFP mRNA is produced is so high that the effect of the repressors (PhiF and BM3R1) is mathematically negligible in the deterministic model.
The value of 0.5 is notable: GFP reached 0.50 while the other circuits limited at ~0.32. This is because the dual promoter architecture is intrinsically stronger (more polymerase binding sites), confirming that the genetic design does influence the system's maximum expression capacity even when temporal dynamics are obscured. The RNAP flux bar chart validates that the output node carries higher flux than intermediate repressor nodes — confirming signal integration even though the time kinetics are simplified by the deterministic solver.
This AND Gate construct is the Asimov Kernel demonstration of the Boolean logic implemented in Strain C of Füzi Poiesis. In the project, the two inputs are the AHL quorum signal (X₁, K_d = 10 nM, n = 2, Hill function) and soluble reactive phosphorus excess above 0.5 mg/L SRP (X₂). The Python AND-gate model confirms output below 5% of maximum in OFF states and above 90% in the (1,1) ON state — matching the design intent even where the Kernel's deterministic simulator cannot resolve the dynamic transitions. The saturation behavior observed here would be addressed in Aim 2 by force-tuning RBS strengths to place the circuit below the saturation threshold under Lake Budi's eutrophication conditions.
Six Questions on Assembly Methods
Phusion High-Fidelity PCR Master Mix is designed to amplify DNA fragments with superior precision for downstream applications including Gibson Assembly.
- Phusion DNA Polymerase — high-fidelity enzyme that synthesizes new DNA strands while minimizing nucleotide incorporation errors. Critical for site-directed mutagenesis and amplification of long inserts like the mcjABCD operon (~3.5 kb) for Füzi Poiesis.
- Deoxynucleotide triphosphates (dNTPs) — the four chemical building blocks (A, T, C, G) consumed during strand synthesis.
- Buffer and cofactors (MgCl₂) — maintains appropriate pH and provides magnesium ions required for polymerase stability and catalytic activity.
- 2× concentration format — researcher adds only primers, template DNA, and nuclease-free water to reach optimal working concentration in the final reaction volume.
- Melting temperature (Tm) — Ta is set 2–5°C below the lowest Tm of the primer pair.
- Primer length — central binding region of 18–22 bp for stable, specific hybridization.
- GC content — 40–60% GC optimal due to three hydrogen bonds per G-C pair.
- GC clamp — one or two G/C at the 3′ end promote specific binding; more than three in the last five nucleotides should be avoided.
- Primer symmetry — forward and reverse primers within 5°C Tm of each other for co-annealing.
- Secondary structures — hairpins or dimers with ΔG below −10 kcal/mol must be avoided by computational screening.
- Overhangs — for Gibson Assembly, 20–22 bp 5′ overhangs establish the homology regions and must be factored into the design.
PCR amplifies a specific region using thermal cycling (98°C denaturation / 53–57°C annealing / 72°C extension), producing new DNA de novo. Restriction digestion cuts at specific recognition sequences isothermally at 37°C without synthesizing new DNA.
| Dimension | PCR | Restriction Digest |
|---|---|---|
| Specificity basis | Primer design — any region with known flanks | Recognition site position — limited to existing sites |
| DNA modification | Add 5′ overhangs, introduce intentional mismatches | Leaves sequence intact; linearizes or fragments |
| Methylation | Products unmethylated (synthesized in vitro) | DpnI selectively digests methylated template |
| Temperature regime | Thermal cycling (98 / 53–57 / 72°C) | Isothermal (37°C) |
| New DNA synthesis | Yes — de novo amplification | No — fragmentation of existing DNA |
Prefer PCR when targeted mutagenesis is required, fragments need Gibson Assembly overhangs, or starting material is limiting. Prefer restriction digestion when methylated template must be removed after PCR (DpnI), or traditional cut-and-paste cloning is the strategy.
- Overlap regions (20–40 bp) — primers incorporate 20–22 bp 5′ overhangs; Benchling verifies correct orientation and overlap accuracy in silico before synthesis.
- Primer design criteria — binding region 18–22 bp, Tm 52–58°C, ΔTm ≤ 5°C, GC clamp at 3′ end, secondary structure ΔG > −10 kcal/mol.
- DpnI digestion — destroys methylated template plasmid after PCR, eliminating background transformants.
- Purification and quantification — silica column purification; post-cleanup concentration > 30 ng/μL (NanoDrop or Qubit).
- Electrophoresis verification — gel confirms band sizes match in silico predictions (pFP-A: ~3.5 kb mcjABCD; pFP-B: ~3 kb sqr-pdo; pFP-C: ~1.4 kb phoA_opt).
- Molar ratio — 2:1 insert-to-vector molar ratio in the isothermal reaction.
Plasmid DNA enters E. coli through temporary pores in the cell membrane created by one of two methods:
- Heat shock — rapid temperature shift from ice to 42°C for exactly 45 seconds forces the membrane to open transiently; plasmid DNA passively diffuses in.
- Electroporation — brief high-voltage electrical pulse (~1.8 kV for E. coli) achieves the same effect with higher efficiency; preferred for large plasmids or low-competence cells.
After shock, bacteria recover in SOC medium at 37°C for ~1 hour — allowing membrane repair and expression of acquired resistance genes before plating on selective media. For Füzi Poiesis Aim 2, transformation of Halomonas elongata will require electroporation protocol optimization as halotolerant bacteria have different membrane compositions than standard E. coli K-12.
Artificial gene synthesis constructs entire genes de novo without a pre-existing template. Short overlapping oligonucleotides (up to 150 bp) with ~20 bp complementary overlaps are designed computationally and annealed during thermal cycling, allowing polymerase to fill gaps and produce a complete double-stranded synthetic gene.
Step 1 · Synthesis — Generate short oligos with ~20 bp overlaps encoding each segment of the target sequence.
Step 2 · Annealing — Complementary regions pair, establishing correct assembly order.
Step 3 · Extension — DNA polymerase fills remaining single-stranded gaps 5′→3′.
Step 4 · Completion — Seamless double-stranded synthetic gene ligated into cloning vector and transformed into E. coli.
Modeling in Benchling: Target sequence imported; design tools partition DNA into overlapping oligos, calculate Tm values (52–58°C range), and screen for hairpins. Simulation in Asimov Kernel: Parts assembled on the digital canvas to verify circuit behavior before wet-lab execution — as demonstrated for Strain C's AND-gate PhoA circuit cross-validation against the Python Hill equation model.