Section 1 – Overview of the Design Workflow
Our engineering cycle begins with problem specification and constraint mapping. Stakeholders outline performance metrics (throughput, robustness, and cost), while regulatory constraints define the operating envelope. Systems-thinking diagrams are drafted to visualise each genetic module as a functional block, allowing rapid identification of potential bottlenecks or incompatibilities.
Once the functional architecture is agreed upon, we transition to computer-aided design. Benchling is used for DNA constructs, while MATLAB/SimBiology provides deterministic simulations of metabolite flux. This integrated environment lets us prototype design variants in silico long before we commit time and resources in the wet lab.
Early decisions are continuously revisited under the “design-build-test-learn” loop. Each loop concludes with a design review that feeds new data back into the requirements document, ensuring traceability from concept to prototype.
Section 2 – Kinetic Modelling & Parameter Estimation
Accurate kinetic parameters are the backbone of our pathway model. We compiled kcat and Km values from the BRENDA and SABIO-RK databases, prioritising measurements taken under conditions closest to our chassis temperature (30 °C) and pH (7.2). When empirical data were missing, we used comparative modelling via AlphaFold to infer active-site similarity and adopted parameters from homologous enzymes.
Parameter fitting employed a hybrid genetic algorithm followed by a Nelder–Mead local search. The objective function minimised the squared error between simulated metabolite curves and published time-course data. Convergence usually occurred within 250 generations, yielding parameter sets whose prediction intervals overlapped 90 % of experimental data points.
Sensitivity analysis ranked TAL and COMT turnover numbers as the dominant determinants of vanillin yield. This insight directed downstream protein-engineering efforts toward improving catalytic efficiency at those specific nodes.
Section 3 – Strain-Selection Decision Matrix
Choosing the optimal microbial chassis required balancing metabolic capacity, genetic accessibility, and safety. We compared E. coli BL21, E. coli Nissle, and Saccharomyces cerevisiae CEN.PK using a weighted-scoring matrix. Criteria included GRAS status, plasmid copy-number stability, and compatibility with our CRISPR toolkit.
Although S. cerevisiae offered superior tolerance to phenolic intermediates, E. coli Nissle scored highest overall thanks to its fast doubling time and proven track record in biosensor projects. A safety audit confirmed that the strain, originally isolated as a probiotic, poses minimal risk under standard BSL-1 conditions.
The decision matrix outcome was validated by bench experiments in which all three strains were transformed with a minimal TAL-only plasmid. Nissle cultures reached target OD600 25 % faster and produced 1.8-fold more p-coumaric acid than the yeast benchmark, supporting the computational recommendation.
Section 4 – In-Silico Toxicity Screening
Before introducing novel metabolites, we conducted a full in-silico toxicity screen using the VEGA-QSAR platform. Each pathway intermediate was evaluated for mutagenicity, aquatic toxicity, and endocrine disruption potential. All compounds scored below critical thresholds, except feruloyl-CoA, which triggered a mild aquatic-toxicity flag.
To mitigate potential environmental impact, downstream purification steps include an activated-carbon capture module capable of adsorbing residual ferulic acid derivatives before effluent release. The activated-carbon mass balance indicates a 98 % removal efficiency under nominal flow rates.
Section 5 – Future Automation & Scale-Up Roadmap
With bench-scale proof-of-concept established, we are designing an automated bioreactor workflow using a Raspberry Pi–controlled sensor suite. Load-cells track biomass, while inline HPLC (micro-fluidic chip) monitors vanillin concentration every 20 minutes. The control loop adjusts inducer feed and aeration rates to maintain optimal flux.
A techno-economic analysis estimates a production cost of US $180 kg-1 vanillin at 1 000 L scale, competitive with synthetic petro-based routes. Key cost drivers are glucose feed and downstream crystallisation. Exploring lignocellulosic hydrolysate as a carbon source could reduce feedstock costs by up to 40 %.
Lastly, we plan to interface our Pi controller with an OPC-UA gateway, facilitating seamless integration into industrial SCADA systems. This step ensures regulatory compliance and lays the groundwork for GMP-grade production.