Over the past year, to prevent and control tomato bacterial wilt caused by Ralstonia solanacearum, we have developed two sets of systematic components, namely the fatty acid induction system and the erucamide production sensing system. The fatty acid induction system responds to the glycolytic system, initiates fatty acid synthesis, and meanwhile activates the downstream erucamide production sensing system. Additionally, by documenting the difficulties we encountered in wet experiments, we hope to provide assistance to other teams intending to carry out projects related to plant diseases.Finally, we established a mathematical model for the three-protein co-expression system and developed a standardized "function-parameter-equation" workflow. This workflow can be repeatedly applied to other iGEM multi-protein synthetic biology projects, providing a reference model foundation for similar projects.

In the fatty acid induction system: Since the production of fatty acids requires the PtsG gene (BBa_25SJHD2N) to rapidly accumulate acetyl-CoA through glycolysis, and to induce the expression of FabH (BBa_25ROJHES) (thereby initiating fatty acid synthesis), we have conducted the expression (with BBa_25HKBP2Y and BBa_25YH73O6) and protein purification of these two genes, thus activating our fatty acid induction system.
In the erucamide production sensing system: The synthesis of erucamide depends on an amidation reaction, with the reaction formula as follows: Erucic acid + Glutamine → Erucamide + Glutamic acid. GlnA (BBa_25GZZ1FM) provides the amide group donor (glutamine) required for the reaction during erucamide production, acting as the direct nitrogen source for erucamide synthesis. Meanwhile, it maintains the intracellular glutamine pool and supports the activity of fatty acid amide hydrolases FAA1/FAA2. Therefore, we have performed the expression (with BBa_25X7OHDK) and protein purification of this gene, thereby activating our erucamide production sensing system.

1.1Basic Part

1.2 Composite Part

2.1Fatty acid-inducible system plasmid synthesis
We successfully introduced the FabH and PtsG genes into the pET-28a(+) plasmid, and this plasmid was successfully transformed into Escherichia coli BL21(DE3). Specific primers were used for PCR amplification of the plasmid extracted from E. coli, and the experimental results demonstrated successful synthesis of these two plasmids.
Subsequently, the Coomassie Brilliant Blue method was employed for electrophoretic analysis of protein bands in the liquid during protein purification, indicating that the proteins could be successfully purified with high purity, making them suitable for plant infection experiments.

2.2The erucamide-induced sensing system has been successfully established.
We successfully introduced the GlnA gene into the pET-28a(+) plasmid, and this plasmid was successfully transformed into Escherichia coli BL21(DE3). Specific primers were used for PCR amplification of the plasmid extracted from E. coli, and the experimental results demonstrated successful synthesis of this plasmid.
In the protein expression experiments, it was found that the protein formed inclusion bodies, making purification considerably challenging. Therefore, we decided to use the bacterial cultures of these three functional strains rather than the protein solution for infecting the target strain.

2.3Bacterial Solution Infection Experiment
In the blank control group (without Ralstonia solanacearum infection), the disease index remained 0 throughout the experiment, the erucamide content was maintained at a baseline level of 14-15 μg/g fresh weight, and the plant survival rate was 100%.
In both the positive control group and the negative control group (without effective disease-resistant intervention), the disease index exceeded 90, the erucamide content decreased to 3.3-4.5 μg/g fresh weight, the plant survival rate was less than 15%, and there were no significant differences in all indicators between the two groups.
In the experimental group, the disease index was significantly lower than that of the previous two control groups ( <30), the erucamide content was significantly increased (16-17 μg/g fresh weight), and the plant survival rate reached 75%..

2.3.1 The functional E. coli bacterial suspension can significantly promote erucamide secretion in tomatoes.
The erucamide content in this group is significantly higher than that in the Ralstonia solanacearum control group and the negative control E. coli bacterial suspension group (P <0.05), and it can activate the erucamide synthesis pathway in tomatoes.

2.3.2 Erucamide secretion is directly negatively correlated with tomato disease resistance. The functional E. coli bacterial suspension group has a low disease index ( <30) and a high plant survival rate (75%); moreover, the higher the erucamide content, the milder the disease. This verifies the hypothesis of "enhancing disease resistance by regulating erucamide secretion" .

2.3.3 The effect of the functional E. coli bacterial suspension is specific. There are no significant differences in indicators between the negative control E. coli bacterial suspension group and the Ralstonia solanacearum control group, which rules out interference from non-functional components and indicates that the disease-resistant effect comes from the functional gene expression products of the suspension.

3.1 Standardized Multi-Protein Modeling Framework
It uses enzyme kinetics (Michaelis-Menten equation) and metabolic control analysis to turn PtsG, FabH, GlnA functions into quantifiable math functions. With literature-based parameters (e.g., K1-K3, γ), it forms a "function-parameter-equation" standardized process, reusable for other iGEM multi-protein synthetic biology projects.

3.2 Engineered Yield-Cost Optimization
Via multi-objective equations (max Y, min Cost) and Lagrange multiplier method, under "protein concentration" and "effective disease-resistant yield" constraints, it calculates the optimal protein ratio (1.2:1.0:1.5) and yield (23.8 μg/g), meeting plant disease resistance needs (5-28 μg/g fresh weight) while cutting costs, avoiding blind trial-and-error in iGEM.