iGEM 2025 — Team ouc-haide

{% extends "layout.html" %}
{% block title %}Notebook{% endblock %}
{% block lead %}This serves as a chronological record of your team's progress throughout the season. It documents your daily activities, experiments, discussions, and decisions.{% endblock %}
{% block page\_content %}

# Notebook

## Notebook for 2024

## Notebook for 2025

{% endblock %}

{% extends "layout.html" %}
{% block title %}Engineering Success{% endblock %}
{% block lead %}Demonstrate engineering success in a technical aspect of your project by going through at least one iteration of the engineering design cycle.{% endblock %}
{% block page\_content %}

# Engineering

## Overview

Since its inception, our project has undergone continuous refinement across multiple domains, including wet lab work, modeling, hardware development, human practices, and education.  **Despite encountering challenges, we consistently adhered to the Design-Build-Test-Learn (DBTL) cycle, which ultimately led to our success.** The following sections detail our iterative development process in the wet lab, modeling, and hardware components.

## Wet Lab

### Cycle 1: Chemical Immobilization Attempt & Pivot

**Design**

Advances in synthetic biology have expanded the use of genetically modified microorganisms in medicine, particularly in living biomaterials. A prevalent strategy in engineered yeast systems involves chemically inducible suicide switches to prevent cellular escape. However, the required inducers are often not fully biocompatible and necessitate manual intervention, thus lacking autonomy. As our dressing contacts blood directly, preventing live cells from escaping the carrier into the bloodstream was critical. Initially, we explored cell surface modification to immobilize yeast cells stably within the hydrogel matrix.

**Build**

We employed sodium periodate oxidation to introduce aldehyde groups onto the yeast cell surface, aiming to enable stable covalent cross-linking with the hydroxyl groups present in the L-DOPA modified hydroxybutyl chitosan (L-HBC) hydrogel.

**Test**

After following the established protocol, we observed a significant loss of viability or complete cell death in the modified yeast. No colony formation was observed on YPD plates after a three-day incubation.

**Learn**

Consultation with our principal investigators, Professor Guanglei Liu and Professor Ya Liu, revealed that this oxidative modification is standard for Pichia pastoris but unsuitable for our chassis, Yarrowia lipolytica, due to its more fragile cell wall and lower tolerance to such chemical treatments. Consequently, we pivoted to exploring gentler cell encapsulation strategies.

### Cycle 2: Hydrogen Bond-Mediated Gentle Encapsulation

**Design**

Conventional cell encapsulation often employs harsh reagents that can significantly compromise cell viability. To address this, we devised a strategy leveraging hydrogen bonding between the surface-displayed antimicrobial peptide Pexiganan by constructing new plasmid based on **pINA1317(BBa\_258PCOD1)** and the L-DOPA modified hydroxybutyl chitosan (L-HBC) hydrogel matrix. This approach enabled the encapsulation of the engineered yeast within the biocompatible L-HBC hydrogel.

**Build**

To achieve microbial surface display, we cloned the Pexiganan sequence into the pINA1317-ylcwp110 plasmid **(BBa\_25VB2NC8)** and transformed it into *Yarrowia lipolytica* Po1h.

**Test**

Successful surface display of functional Pexiganan was confirmed by an Oxford cup assay, which demonstrated a clear zone of inhibition against target bacteria. We then conducted encapsulation experiments to quantitatively assess yeast leakage over time

![]( https://static.igem.wiki/teams/5541/project/engineering/totalpic/engerning-1.webp)

Img.1 Time-course evaluation of engineered yeast encapsulation and leakage within the L-HBC hydrogel. A time-course assay was conducted to quantify the leakage dynamics of engineered yeast encapsulated in the L-HBC hydrogel. (a) At 24 hours, no yeast colonies were observed, indicating excellent integrity of the encapsulation system initially. (b) At 48 hours, the emergence of 4 single colonies indicated the onset of minimal leakage. (c) By 72 hours, the colony count increased to 26, demonstrating a significant, time-dependent increase in leakage. These results confirm that our encapsulation strategy provides complete containment within the first 24 hours and effectively suppresses leakage up to 48 hours. Furthermore, the clear time-dependent leakage trend offers crucial experimental evidence and a defined time window for future optimization of the encapsulation efficacy.

**Learn**

Leakage assay results (Figure 1) confirmed that our encapsulation strategy, based on non-covalent interactions, effectively contained the yeast for 24-48 hours, with minimal leakage thereafter, demonstrating its reproducibility and initial efficacy. This approach garnered positive feedback from microbiology experts and interdisciplinary team members during human practices engagements, who suggested computational modeling could further elucidate the underlying interactions. Through this DBTL cycle, we significantly mitigated the biosafety risk of cell escape and established a gentle encapsulation method for living biomaterials, while also identifying a clear pathway for future optimization.

## Dry Lab

### Hardware

#### Cycle 1: More Reliable "Wound Recognition + Spatial Positioning"

**Design**

Problem Discovery:
In wound visual recognition and spatial positioning, the initial version using RGB threshold segmentation missed boundaries under dark skin/strong reflection.
In 3D spatial positioning, ToF/radar solutions were too costly, which is not conducive to low-cost adoption.

**Goal:**
Under low computing power and low-cost constraints, achieve robust 2D contour segmentation and 3D relative spatial position/scale estimation.

**Build**

1. Data & Model: Collected wound images of feet/hands with different skin colors, then trained FCN/YOLO/SAM models.

2. Geometric Calibration: Integrated camera intrinsic/extrinsic calibration; micro-step sizes (Bx, By) were precisely given by the robotic arm.

3. We attempted machine learning algorithms for semantic segmentation, testing YOLO, SAM, and FCN models for wound segmentation. Mean IoU (mIoU) was used as the evaluation metric.

4. Depth/Scale: Used micro-baseline disparity method: the camera on the robotic arm performed known small horizontal/vertical micro-shifts, disparity was calculated via pinhole imaging to obtain relative height difference ΔZ and pixel physical scale (m/px).

5. Edge Hardware: Low-distortion visible-light camera + ESP/STM32 control.

**Test**

Recognition Robustness: Compared RGB threshold vs. FCN/YOLO/SAM in dark skin, strong reflection, and fluid environments, using mIoU and boundary F1. Results showed FCN achieved the best accuracy and was chosen as the main recognition model for future applications.
Depth Accuracy: Verified ΔZ errors under multiple baselines using standard step blocks; used error propagation to derive recommended micro-baseline (to meet target precision δZ). Also tested dimensional accuracy.

**Learn**

**FCN** was the most stable in boundary and dark-skin samples. YOLO had low latency but coarse boundaries. **SAM** required interactive annotation, suitable for guided labeling of difficult cases.

**Future plan:** adopt FCN as the backbone recognizer, **YOLOv8-seg** for ROI proposals/light backup, and SAM for semi-automatic annotation of difficult samples.

Precision and inference delay vary with different baseline cameras; in real applications, multiple trials are needed to achieve an engineering trade-off balance.

#### Cycle 2: More Stable "Robotic Arm Control + Anti-Jitter"

**Design**

Problem Discovery: **ESP32** built-in **IK** and linear interpolation caused jitter and end-effector Z drift during multi-joint coordination, leading to occasional nozzle contact with the wound. Even after PID fine-tuning, residual error remained.

**Goal:** Improve end-effector trajectory precision and Z-axis stability, enabling repeatable and traceable drug application on low-cost robotic arms **(RoArm-M3-S, 5–6 DOF)**.

**Build**

1. Developed **parameterized IK** (using  **DH parameters** to generate numerical/analytical hybrid solutions), solved and time-parameterized on PC, then sent joint trajectories to the robot.

2. **Polar Strip Path**: Within one strip, primarily moved the base joint, while shoulder/elbow were locked or minimally adjusted, physically suppressing Z-axis disturbance.

3. **Trajectory Shaping**: Applied S-curve acceleration/deceleration and **joint-level synchronization** constraints; if necessary, added lead **compensation/feedforward** for key joints.

Test

Ran comparative tests between the old and new control algorithms.
Using both camera and raw serial output from the end effector, jitter was significantly reduced and Z-axis disturbance disappeared, ensuring safety and accuracy in drug application.

Learn

Conclusion: Moving **IK** and trajectory calculation to the **PC side** and adoptingb **polar strip paths** significantly reduced Z jitter. Transitioning from “multi-joint synchronous straight-line” to “few-joint dominant” control proved to be the most cost-effective stabilization method.

Next Step: Add **online error shaping** at the trajectory level (via end-force/torque or visual feedback loop), and **open API + parameterized IK generator** to facilitate secondary development and reproducibility.

### Model

#### Cycle 1: From Basic Retrieval to an Intelligent Information Assistant

Design

In the initial phase of the project, the core problem we faced was how to efficiently and accurately find candidate peptides that meet our wet lab requirements from a vast amount of unstructured antimicrobial peptide (AMP) literature and databases. Traditional keyword search or SQL query methods struggle to handle the complex semantics of natural language, leading to information omission or low retrieval efficiency.

To solve this problem, we initially designed a knowledge-based question-answering system based on **Retrieval-Augmented Generation (RAG)**. The core idea is to combine the powerful natural language understanding capabilities of a Large Language Model (LLM) with the precision of an external knowledge base. The system first understands the user's natural language query, then retrieves the most relevant text fragments (Chunks) from our constructed AMP literature database, and finally provides these fragments as context to the LLM to generate an answer that is both accurate and comprehensive. We hoped this approach would avoid the "hallucination" problem of general-purpose large models while addressing the limitations of traditional retrieval methods.

|  |
| --- |
| ![](https://static.igem.wiki/teams/5541/project/engineering/totalpic/engerning-2.webp) |
| Img.2 Diagram of the RAG Q&A System's Composition and Workflow |

Build

We used PyPDF to process the collected AMP literature, splitting it into text chunks. Next, we utilized the Sentence-Transformers model to convert the text chunks into vectors and stored them in a FAISS vector database for fast retrieval. For the backend, we chose DeepSeek-R1 as the core language model and built a web service using the Flask framework. The front end was implemented with HTML and JavaScript for the interactive interface. We successfully built a prototype of the RAG system capable of answering questions about a single uploaded document.

Test

We designed two sets of tests to evaluate the system's performance.

1. **Test Group One (Information Extraction Capability):** We provided the system with queries targeting a single document, such as "Find me antimicrobial peptides that have inhibitory effects on *Staphylococcus aureus*." The results showed that the system could accurately identify keywords and, in conjunction with the context, summarize effective information, including the mechanism of action (Figure.3).

2. **Test Group Two (Retrieval Accuracy):** We conducted a quantitative retrieval quality assessment in a database containing multiple documents. We designed a specific query ("What is the mechanism of action of nisin against *Staphylococcus aureus*?") and manually annotated the relevant "gold standard" text chunks beforehand.

The test results revealed a serious problem: for this specific query, the system's hit rate, Mean Reciprocal Rank (MRR), and **Normalized Discounted Cumulative Gain (nDCG)** were all 0. The retriever returned irrelevant text chunks that contained the keyword "nisin." The reason was the high frequency of the keyword in the literature and a deviation in semantic matching, which led the retriever astray.

Learn

This failure gave us a profound understanding that the fixed, linear **"Query -> Retrieve -> Generate"** workflow of a standard RAG system has fundamental flaws. When faced with complex scientific questions, it lacks the ability to self-correct and iteratively optimize. If the retrieval fails, the entire process fails with it, unlike a human researcher who can dynamically adjust their retrieval strategy.

Based on this lesson, we decided to upgrade the system to an **Agentic RAG** paradigm. The core of the new design is a cyclical **"Think-Act"** framework. After retrieval, the Agent first evaluates the quality of the results. If the results are poor, it can autonomously reformulate the query, adjust its strategy, and retrieve again, forming a dynamic optimization loop. We named this more powerful and robust agent the **AMP Research Agent**. It successfully solved the retrieval problems encountered in the first phase and found the key candidate antimicrobial peptide, Pexiganan, for our wet lab experiments.

|  |
| --- |
| ![](https://static.igem.wiki/teams/5541/project/engineering/totalpic/engerning-3.webp) |
| Img.3 Comparison of the mechanisms between a standard RAG system and an Agentic RAG system |

#### Cycle 2: From "Black-Box" Generation to Interpretable Sequence Priority Reranking

Design

As our research deepened, we anticipated the need to design new antimicrobial peptides from scratch to meet more stringent therapeutic requirements. However, traditional machine learning or deep learning generation models have two major pain points: "low hit rate" (often below 10%) and "lack of interpretability." The latter makes the model a "black box," creating a huge cognitive gap between wet and dry lab researchers and hindering collaborative optimization.

Our design philosophy was to **avoid "black-box" generation and shift to "white-box" ranking**. Instead of having the model directly create new sequences, we developed an agent capable of **intelligently reranking a set of candidate sequences**. The core idea is to leverage the powerful reasoning ability of an LLM to learn and summarize explainable **"design experiences"** in natural language by comparing a large number of known AMP sequences and their properties. Then, using these accumulated experiences, it evaluates and ranks the candidate sequences, recommending the most promising ones for wet lab validation, thereby significantly reducing validation costs. This method combines the stability of machine learning with the "insightful" convergence advantage of LLM reasoning.

|  |
| --- |
| ![](https://static.igem.wiki/teams/5541/project/engineering/totalpic/engerning-4.webp) |
| Img.4 Diagram showing the convergence effect of combining machine learning methods with experience-driven LLM reasoning methods |

Build

We built the **AMP Rerank Agent**, which consists of three core modules:

**Memory Module:** Inspired by the memory-agent concept, we dynamically store and update the experiences learned by the Agent in the form of a knowledge graph.

**Training Module:** We processed the grampa.csv dataset, calculating and adding physicochemical properties. We innovatively used **cosine similarity** to cluster sequences (Table 4), ensuring that the LLM could learn the most effective experiences from comparing highly similar sequences. Through careful prompt engineering (Figure.7), we guided the LLM to autonomously learn and accumulate 1948 design experiences.

**Reranking Module:** We designed a rigorous **"Sequence Analysis -> Experience Retrieval -> Final Ranking" workflow** (Img.5). The Agent first independently analyzes the physicochemical properties of each sequence, then retrieves relevant experiences from its memory, and finally provides a final ranking with explanations based on all the information.

|  |
| --- |
| ![](https://static.igem.wiki/teams/5541/project/engineering/totalpic/engerning-5.webp) |
| Img.5 Diagram of the Reranking Module's workflow |

Test

We selected 9 antimicrobial peptide sequences as a "gold standard" test set and had the AMP Rerank Agent and a baseline machine learning model (based on ProtBERT) predict the ranking of these sequences according to their MIC values. We used the **nDCG@9** metric to evaluate the quality of the ranking results.

The test results showed that the machine learning model achieved an nDCG@9 score of **0.916**, while our AMP Rerank Agent scored **0.871** (Tab.1).

![]( https://static.igem.wiki/teams/5541/project/engineering/totalpic/engerning-6.webp)

|  |
| --- |
| Tab.1. Table showing nDCG@9 results for the Machine Learning Method and the AMP Rerank Agent |

Learn

Although slightly lower in quantitative score than the pure machine learning model, this test validated the core value of our design. The greatest success of the AMP Rerank Agent lies in its  **unparalleled interpretability**. For example, it can provide clear decision rationales like "strong antimicrobial activity with good safety; stable amphipathic structure, easy to interact with membranes," completely breaking down the barrier of "black-box" models and bridging the cognitive gap between wet and dry labs.

We realized that the current performance is limited by the small amount of training data (only 134 groups). The future optimization path is very clear: through larger-scale training, we will allow the Agent's knowledge network to accumulate enough experience to eventually achieve a "knowledge phase transition," surpassing traditional models in both performance and interpretability.

#### Cycle 3: From Tedious Programming to Automated Wet Lab Data Insights

Design

During our wet lab experiments, we encountered common data analysis challenges. For example, rheometer data contained missing values due to instrument noise and physically impossible outliers (such as negative moduli). Handling this data required programming knowledge, which was not only time-consuming but also diverted researchers' valuable energy from scientific thinking.

To address this pain point, we designed an **automated Data Analysis Agent**. The goal of this Agent is to free scientists from tedious data processing and programming. Its core design is to be able to:

1. Understand high-level analysis requirements described by the user in natural language.

2. Autonomously break down the requirements into specific, executable data processing steps.

3. Automatically generate and execute Python code to perform data cleaning, statistical analysis, and model fitting.

Summarize the results into a structured analysis report.

Build

We used the LangGraph framework to build the workflow of the **Data Analysis Agent**. LangGraph's structure of nodes and edges is naturally suited for orchestrating a fixed workflow. The process was designed as: **Planner -> Code Generator -> Code Executor -> Report Generator**. When a user inputs a task, the Agent sequentially invokes these nodes, forming a complete automated pipeline from understanding the task to generating the final report. We integrated powerful Python data science libraries such as pandas, SciPy, and matplotlib, enabling it to handle complex analysis tasks.

|  |
| --- |
| ![](https://static.igem.wiki/teams/5541/project/engineering/totalpic/engerning-7.webp) |
| Img.6 Diagram of the Data Analysis Agent's workflow |

Test

We conducted two tests to verify the Agent's effectiveness:

1. **General Task Test:** We provided the Agent with data on the charge and MIC values of a set of antimicrobial peptides and asked it to analyze the correlation between them. The Agent successfully generated code, calculated the Pearson correlation coefficient, and output a complete report containing statistical analysis and visualizations.

2. **Real-World Task Test:** We gave the Agent a real challenge we faced in the wet lab—processing anomalous rheology data for the gelation temperature of L-HBC. We described the problem (negative modulus values) and asked the Agent to recommend a processing strategy. The Agent accurately identified the problem and suggested using Spline Interpolation, providing a reasonable explanation for choosing this method, as it best preserves the inherent patterns in data from continuous physical processes.

Learn

The test results demonstrated that the Data Analysis Agent can successfully translate high-level analysis requirements into specific, correct execution steps and code, greatly improving the efficiency and standardization of data processing. We learned that this type of Agent, which combines the planning capabilities of an LLM with the precise execution of code, is a powerful tool for solving repetitive technical tasks in scientific research. It not only solved our immediate data processing problems but also laid a solid technical foundation for our more ambitious future goals—for example, providing real-time online data analysis and decision support for our intelligent robotic arm. This significantly accelerates the conversion of raw data into scientific insights, allowing researchers to focus more on innovation and scientific discovery itself.

#### Cycle 4: Investigating the Microscopic Driving Forces of L-HBC's Thermo-sensitive Properties

Design

The starting point for this project's engineering design was to find a solid microscopic mechanism for the macroscopic thermo-sensitive phenomenon observed in L-HBC hydrogels in our wet experiments. The core question we faced was: How exactly does a change in temperature drive the material from a low-temperature liquid state to a body-temperature gel state at the molecular level? To address this, we proposed a core scientific hypothesis: this phase transition is the result of a dynamic competition between polymer-water interactions (enthalpy-driven) and polymer-polymer interactions (entropy-driven), dominated by temperature changes. To test this hypothesis, we chose molecular dynamics simulation as our core research tool because it provides an objective perspective on dynamic behavior at the atomic scale. At the outset of the design, we defined the approximations and simplifications in our system: a system composed of 6 L-HBC chains, each with a degree of polymerization of 100, would be considered a "minimal functional unit" capable of representing macroscopic behavior. Furthermore, the nanosecond time scale would be sufficient to capture the key molecular events necessary to validate our hypothesis, such as the rapid reorganization of the hydrogen bond network and the initial collapse trend of the chain conformation.

Build

The construction of the model was a multi-step, multi-tool collaborative precision process. We first used Chemdraw and Materials Studio to build a long L-HBC chain with the correct chemical structure and three-dimensional conformation. Then, using the Sobtop program, we automatically generated topology files based on the **GAFF general force field** and MMFF94 atomic charges for this complex, non-standard polymer system. This decision struck a key balance between ensuring internal force field consistency and computational efficiency. To obtain a relaxed initial conformation, we thoroughly pre-equilibrated a single chain.

|  |
| --- |
| ![](https://static.igem.wiki/teams/5541/project/engineering/totalpic/engerning-8.webp) |
| Img.7 Pymol visualization of a single L-HBC chain structure after pre-equilibration |

Finally, using Packmol software, we efficiently assembled 6 pre-equilibrated single chains into an initially uniform multi-chain aggregate. We solvated it and performed a pre-equilibration with position restraints. Ultimately, we used GROMACS software to run production simulations of the six-chain glycopolymers at 4°C and 37°C for 1 ns and 8 ns, respectively, as shown in the video (only the simulation at 37°C for 8 ns is shown):

Video.1 Dynamic Simulation Demonstration at 37 degrees Celsius for 8ns

Test

We placed the constructed systems at two different temperatures, 4°C (277.15 K) and 37°C (310.15 K), and ran production simulations for 8 nanoseconds each. We used the system's Root Mean Square Deviation (RMSD) and Radius of Gyration (Rg) as quantifiable success criteria to evaluate its conformational changes. The test results clearly showed that at 4°C, the system's RMSD reached a plateau after about 5 nanoseconds, and the Rg value remained stable. This demonstrated that the polymer chains formed a stable, extended conformation that fully interacted with water molecules. In stark contrast, at 37°C, the system's RMSD did not converge throughout the entire 8 nanoseconds, while the Rg showed a significant and continuous downward trend. This directly proved that the polymer chains were undergoing continuous dehydration and collapse.

Learn

The test results of this engineering cycle strongly validated our initial scientific hypothesis. We learned that the thermo-sensitive phase transition of L-HBC is indeed a dehydration and collapse process driven primarily by an increase in entropy initiated by the temperature change. This profound understanding from the atomic scale not only provides a solid theoretical explanation for the phenomena observed in the wet lab, but more importantly, it offers practical guidance for our project. The model's results clearly indicate that to ensure optimal efficiency and uniformity during the encapsulation of drugs or cells, all loading operations should be performed at low temperatures (e.g., 4°C), as the molecular network is most open and extended at this temperature, providing the best fluidity and loading space.

#### Cycle 5: Deconstructing the Affinity Mechanism between L-HBC and Biomolecules

Design

After successfully elucidating the material science foundation of L-HBC, we entered the second engineering cycle, aiming to solve a more advanced problem: What is the molecular basis for L-HBC's "gentle immobilization" of bioactive molecules when used as a cell encapsulation matrix? We proposed a new scientific hypothesis: this high affinity originates from the formation of a stable hydrogen bond network between L-HBC and peptide chains through an interfacial **desolvation** process, which is favorable in terms of both energy and entropy. In our design, we chose the antimicrobial peptide Pexiganan as a model for cell surface proteins. A key engineering decision was to continue using the GAFF force field to describe the entire composite system to ensure a physically consistent description of the polymer-peptide chain heterogeneous interface, thereby avoiding computational artifacts that could arise from mixing different force fields.

Build

The model construction in this phase reflected the continuity and reusability of our work. We first used AlphaFold3 to predict the three-dimensional structure of Pexiganan and again used Sobtop to generate a GAFF topology file compatible with our system. Subsequently, we took the equilibrated 37°C L-HBC six-chain glycopolymer from the first engineering cycle as the initial conformation and strategically placed 10 Pexiganan peptide chains around it using Packmol software to construct the final composite system. This approach not only efficiently reused existing results but also ensured that the starting point of the study was a well-relaxed and physically more reasonable polymer conformation.

|  |
| --- |
| ![](https://static.igem.wiki/teams/5541/project/engineering/totalpic/engerning-15.webp) |
| Img.8 Diagram of the system with the six-chain glycopolymer and 10 antimicrobial peptides |

Test

We conducted a 4-nanosecond production simulation on the constructed composite system. The core of the test was to precisely quantify the dynamics of the hydrogen bond network between L-HBC and Pexiganan. The analysis revealed key information on two levels: first, a macroscopically extremely stable hydrogen bond network of about 610 bonds was rapidly formed and maintained between them, which directly proves the existence of strong and extensive interactions.

|  |
| --- |
| ![](https://static.igem.wiki/teams/5541/project/engineering/totalpic/engerning-16.webp) |
| Img.9 Total number of hydrogen bonds formed between polysaccharide and peptide chains over time |

Second, by comparing the changes in the number of "solute-solute" hydrogen bonds versus "solute-water" hydrogen bonds, we observed a clear negative correlation: when polysaccharide-peptide hydrogen bonds were formed, the number of hydrogen bonds each formed with water molecules decreased significantly and simultaneously.

|  |
| --- |
| ![](https://static.igem.wiki/teams/5541/project/engineering/totalpic/engerning-17.webp) |
| Img.10 Competitive relationship between 'solute-solute' and 'solute-water' hydrogen bonds |

Learn

This test perfectly validated our desolvation hypothesis. We learned that the core thermodynamic driving force for the strong affinity between L-HBC and Pexiganan comes not only from the energy advantage of their direct interaction but also, to a large extent, from the favorable entropy increase resulting from the release of ordered water molecules from their surfaces. This profound molecular mechanism insight provides a solid theoretical foundation for our project's design of "encapsulating engineered bacteria in an L-HBC hydrogel and synergistically treating wounds with antimicrobial peptides." It clearly communicates the intrinsic mechanism of our solution at the atomic scale, proving that our chosen material system is not only macroscopically feasible but also possesses efficient and stable bio-affinity at the microscopic level.

#### Cycle 6: Spatiotemporal Pharmacodynamic Prediction of a "Living Therapy" Based on a Reaction-Diffusion Model

Design

This engineering cycle began with a clear clinical challenge: Can our designed engineered yeast-hydrogel system rapidly and effectively deliver the therapeutic factors IL-4 and VEGF in the complex pathological microenvironment of a chronic diabetic wounds? To avoid costly and time-consuming animal experiments upfront, we decided to first construct a high-fidelity computational model, a "digital twin" of the wound microenvironment. Our core design choice was to use **reaction-diffusion partial differential equations (PDEs)**, as they are the gold standard for describing the spatiotemporal dynamics of substances in biological tissues. To transform a complex biological problem into a mathematically solvable model, we systematically established a series of key assumptions. These included simplifying the three-dimensional wound into a one-dimensional depth profile to capture the main concentration gradients, adopting a continuum assumption to describe collective molecular behavior, and using the quasi-steady-state approximation (QSSA) to simplify molecular binding kinetics. This approach maintained the core physical meaning while ensuring the computational feasibility of the model.

Build

The model construction process involved precisely translating biological concepts into mathematical language. We first divided the wound into two regions: a hydrogel domain (Ωg) and a tissue domain (Ωt), and established mass balance equations for four key species (IL-4, VEGF, MMPs, and Albumin). In the hydrogel domain, we meticulously derived the effective PDE, considering yeast production, diffusion, MMP degradation, and the dual buffering effects of chitosan and albumin. In the tissue domain, we established a general transport equation describing diffusion and cellular consumption. To ensure the model reflected real-world physicochemical properties, we assigned values to dozens of parameters (including diffusion coefficients, reaction rate constants, partition coefficients, etc.) based on extensive literature review and biophysical principles (such as the Stokes-Einstein equation), forming a "baseline scenario." Finally, we implemented this complex system of coupled, non-linear PDEs numerically in Python using the **Finite Difference Method**. We employed a Forward-Time Central-Space (FTCS) scheme, converting the model into a set of discrete algebraic equations that could be solved by time-stepping, thus completing the construction from theory to executable code.

Test

We ran a 36-hour numerical simulation of the constructed model under the defined "baseline scenario" to test the pharmacodynamics of our "living therapy" system. We established a clear and quantifiable success criterion: the "time to target concentration at the interface," defined as the moment when the concentrations of IL-4 and VEGF first reach their therapeutic thresholds (1.5 nM and 1.0 nM, respectively) at a depth of 20 micrometers below the wound surface (x=0.02 mm). The test results were clearly presented through spatiotemporal concentration heatmaps and time-series curves at the monitoring point. The results showed that VEGF first reached its target concentration in about 0.16 hours (9.6 minutes), while IL-4 reached its target in about 0.32 hours (19.2 minutes).

|  |
| --- |
| ![](https://static.igem.wiki/teams/5541/project/engineering/totalpic/engerning-20.webp) |
| Img.11 Concentration change of IL-4 and VEGF at 0.02mm |

Learn

This simulation test provided us with profound insights and learning outcomes. First, it strongly demonstrated the great potential of our designed yeast-hydrogel system for **rapid onset of action**, capable of establishing an effective dual-factor therapeutic concentration locally at the wound site within half an hour. This provides strong theoretical support for the feasibility of the therapy. Second, we learned a seemingly counter-intuitive yet crucial mechanism: although the VEGF molecule is larger, its onset of action is faster than that of IL-4. This is because IL-4 is subject to a stronger buffering effect from the gel matrix, which significantly reduces its effective diffusion rate. This finding revealed that the **gel's buffering effect** is a key bottleneck parameter controlling the drug release kinetics. Finally, this validated model provides clear guidance for subsequent wet lab experiments: it not only gives precise recommendations for sampling time points in animal studies (should be early, e.g., within 0.5-2 hours) but also points the way for formulation optimization (e.g., by modifying the chemical properties of chitosan to reduce its buffering effect on IL-4). Through this engineering cycle, we successfully transformed a complex biomedical problem into a predictive, quantitative tool, achieving efficient, simulation-guided experimental research and development.

{% endblock %}

{% extends "layout.html" %}
{% block title %}Experiments{% endblock %}
{% block lead %}Describe the research, experiments, and protocols you used in your project. It is designed to provide sufficient information for other teams to replicate our work.{% endblock %}
{% block page\_content %}

# Experiment

## Genetic Engineering and Strain Construction

## Protein Expression and Function Validation

## Intelligent Response System Verification

## Construction and Evaluation of Delivery Systems

{% endblock %}

{% extends "layout.html" %}
{% block title %}Results{% endblock %}
{% block lead %}Present the results of your project, along with a detailed analysis and discussion of their significance. Also outline future plans and reflections on the impact of your project.{% endblock %}
{% block page\_content %}

# Results

We present the results according to the project's design logic, proceeding from genetic construction to functional validation and, finally, to product integration. This section is structured into three main parts: 1) the successful construction of engineered yeast strains, 2) the functional efficacy of the multi-layered therapy system, and 3) the performance of the smart encapsulation and delivery system.

## 1. Successful Construction of Engineered Yeast Strains

The synthesized plasmids, hosted in *E. coli*, were extracted using a commercial kit. As the vendor provided a quality report, re-sequencing was omitted. We then transformed the four target plasmids—carrying the Pexiganan surface display system, VEGF, IL-4, and IL-10 genes—into the *Yarrowia lipolytica*Po1h strain (Genotype: Ura-, ΔAEP, ΔAXP, Suc+). As the strain is auxotrophic for uracil, the plasmids carried the Ura3 gene, allowing positive transformants to be selected on YNB medium. Several viable single colonies were successfully obtained. Selected single colonies were cultured in YPD medium for expansion, followed by genomic DNA extraction from the transformants. PCR amplification was performed for each target gene, and the products were analyzed by agarose gel electrophoresis to confirm the size of the amplified genes.

Confirmation of successful genomic integration for all four target genes was obtained by agarose gel electrophoresis of PCR products from the *Yarrowia lipolytica* Po1h transformants. Img.1 shows distinct bands of the expected sizes for Pexiganan (483 bp), VEGF (673 bp), IL-4 (481 bp), and IL-10 (541 bp), validating the successful integration and presence of the target genes in our engineered strains.

.

![](https://static.igem.wiki/teams/5541/wetlab/results/resultnew/result-1.webp)

Img.1 Construction validation of the engineered *Yarrowia lipolytica* strains for diabetic wound therapy. Cloning verification of the key therapeutic genes in the recombinant *Yarrowia lipolytica* strains. Specific bands amplified by colony PCR from positive clones, with the electrophoresis results confirming the successful construction and transformation of the expression vectors into the yeast cells. (a) Gene encoding the antimicrobial peptide Pexiganan (483 bp) for surface display. (b) Gene encoding vascular endothelial growth factor (VEGF, 673 bp) under the control of a heat-inducible promoter. (c) Gene encoding interleukin-4 (IL-4, 481 bp) under the control of a glucose-inducible promoter. (d) Gene encoding interleukin-10 (IL-10, 541 bp). The leftmost lane in each panel is the DNA molecular weight marker. It is important to note that while the IL-10 gene was successfully cloned, the corresponding engineered strain failed to express the protein at a detectable level and was thus excluded from subsequent functional experiments.

## 2. Therapy System

### 2.1 Functional Surface Display of Antimicrobial Peptide Pexiganan

Since Pexiganan is designed to be firmly displayed on the cell wall and not secreted into the supernatant, we could not isolate it from the culture medium. The standard method for verifying surface display via plasmid is to fuse the target protein with GFP and observe fluorescence under a microscope. Due to time constraints, we opted to demonstrate Pexiganan's functionality as an indicator of successful surface display.

We evaluated the antibacterial capability using the Oxford cup method, which revealed potent antibacterial activity of the engineered yeast. As illustrated in Img.2, the culture of Pexiganan-displaying yeast generated a clear inhibition zone with a diameter of approximately 1.6 cm against both *E. coli* and *S. aureus*, confirming the successful surface display and functionality of the antimicrobial peptide.

![](https://static.igem.wiki/teams/5541/wetlab/results/resultnew/result-2.webp)

Img.2 Validation of the antibacterial activity of engineered yeast surface-displaying Pexiganan. Antibacterial assessment of the engineered yeast using the Oxford cup method on a single agar plate. (a) Zone of inhibition observed after adding 100 μL of *Yarrowia lipolytica* culture surface-displaying the antimicrobial peptide Pexiganan. The clear zone demonstrates the effective bactericidal activity of the engineered yeast. (b) Control area without an Oxford cup, showing the normal growth of *Escherichia coli* and *Staphylococcus aureus* in the absence of any intervention.

### 2.2 Secretion of Bioactive Anti-inflammatory Factor IL-4

#### 2.2.1 Bioactivity of IL-4

Given the structural complexity of interleukins, we prepared two factors as a precaution. Since our goal was to induce M2 macrophage polarization to alleviate inflammation, and both IL-4 and IL-10 can promote this polarization, successful expression of either one would suffice. Positive transformants verified by PCR were induced in PPB medium for 3 days to express the target proteins from the IL-4 plasmids, respectively. The expression products were precipitated using ice-cold acetone and initially detected by PAGE.

We set up three experimental groups: a group treated with IL-4 secreted by yeast, a positive control group with commercial IL-4, and a group treated with LPS. Equal volumes of reagents and media were added and co-cultured with RAW264.7 cells. The M1/M2 ratio was detected by flow cytometry after one day. The results demonstrated that the IL-4 secreted by the engineered yeast was biologically active (Img.3).

![](https://static.igem.wiki/teams/5541/project/proofofconceptnew/pofchange2.webp)

Img.3 Effect of IL-4 secreted by engineered yeast on macrophage polarization. Flow cytometric analysis of the M1/M2 phenotype ratio in RAW264.7 macrophages under different treatments.

Compared to the pro-inflammatory state induced by LPS (M1/M2 ratio > 3), both the addition of 20 ng/mL commercial IL-4 and 100 μg/mL crude protein (containing IL-4) secreted by the engineered yeast significantly promoted macrophage polarization towards the anti-inflammatory M2 phenotype, reducing the M1/M2 ratio to below 1, confirming the IL-4 in the crude protein holds full bioactivity.

#### 2.2.2 Selection of Glucose-inducible Promoter

To identify a promoter exhibiting strong responsiveness to glucose, three glycolysis-related genes were selected and analyzed by qRT-PCR. We first assessed the integrity of the extracted RNA by electrophoresis (Img.4), which revealed sharp ribosomal RNA bands. The results identified the PGK promoter as the most responsive, exhibiting the strongest induction of downstream gene expression under high glucose conditions (Img.5).

![](https://static.igem.wiki/teams/5541/wetlab/results/resultnew/result-4.webp)

Img.4 Yeast RNA samples for promoter screening.

Agarose gel electrophoresis of total RNA from yeast cultured under high-glucose and glycerol-replacement conditions. The RNA samples from both groups exhibited a characteristic electrophoretic profile dominated by distinct 18S and 5S rRNA bands. These samples were subsequently used as templates in qPCR analyses, which successfully identified a glucose-sensitive promoter, confirming their suitability for this transcript-level analysis.

![](https://static.igem.wiki/teams/5541/wetlab/results/resultnew/result-5.webp)

Img.5 Analysis of glycolysis-related gene expression in response to high glucose.

The relative expression levels of three glycolysis-related genes (PGK, TDH, HXK) in *Yarrowia lipolytica* cultured under high-glucose versus glycerol (control) conditions were analyzed by qRT-PCR. Data were normalized to the expression levels under glycerol culture (set as 1).

As shown, the PGK gene exhibited the most significant upregulation in response to high glucose, with its expression increasing more than twofold. In contrast, the expression of TDH and HXK showed markedly lower induction (both less than 1.4-fold). This result clearly demonstrates the high glucose sensitivity of the PGK promoter, leading to its selection for driving the expression of the anti-inflammatory cytokine IL-4.

#### 2.2.3 Validation of promoter PGK

To validate the functionality of the PGK promoter, we constructed a pPGK-GFP expression cassette. As shown in Img.6, the engineered yeast harboring this plasmid exhibited green fluorescence under microscopy, with significantly greater intensity than the wild-type control, demonstrating that the PGK promoter effectively drives recombinant gene expression.

![](https://static.igem.wiki/teams/5541/wetlab/results/resultnew/result-6.webp)

Img.6 Functional validation of glucose-induced GFP expression driven by the PGK promoter.

Fluorescence microscopy analysis of GFP expression in engineered yeast harboring the pPGK-GFP plasmid under high-glucose conditions. The top left panel shows a bright-field image of wild-type yeast, and the top right panel shows the corresponding fluorescence image, showing only minimal background signal. The bottom left panel shows a bright-field image of engineered transformants, and the bottom right panel shows their corresponding fluorescence image. Upon excitation at 492 nm, a strong green fluorescent signal is observed in the transformants, which is absent in the wild-type control. This result visually confirms that the PGK promoter is effectively activated by glucose and capable of driving robust heterologous gene expression.

### 2.3 Secretion of Pro-angiogenic VEGF with Demonstrated Bioactivity

#### 2.3.1 Bioactivity of VEGF

To directly assess its bioactivity, we measured the ability of yeast-secreted VEGF to promote cell proliferation. After synchronizing the cell cycle using CCK-8, we set up two groups: one treated with VEGF secreted by yeast and a blank control group. Cell proliferation was then quantified by measuring the OD450 using a microplate reader.

![](https://static.igem.wiki/teams/5541/wetlab/results/resultnew/result-7.webp)

Img.7 Cell proliferation-promoting effect of yeast-secreted products.

The impact of crude protein secreted by engineered yeast on cell proliferation was assessed using a CCK-8 assay. After 48 hours of culture, cell viability was quantified by measuring the OD450. The group supplemented with the crude protein (containing yeast-secreted VEGF) exhibited a significantly higher OD450 value than the blank control group (p < 0.01), indicating enhanced HUVEC proliferation. This quantitative result confirms that our engineered yeast secretes VEGF with significant mitogenic activity.

#### 2.3.2 Selection of Heat-inducible Promoter

Similar to the glucose-sensitive promoter, to identify a promoter with a strong response to heat stimulation, we selected three heat shock protein-related genes and analyzed their expression levels using qRT-PCR. The results showed that the HSP90 promoter was the most effective, with the highest upregulation of its downstream gene expression (Img.8).

![](https://static.igem.wiki/teams/5541/wetlab/results/resultnew/result-8.webp)

Img.8 Expression response of heat shock genes to different temperatures. The relative expression levels of HSP40, HSP70, and HSP90 genes in *Yarrowia lipolytica* at 37°C, 39°C, and 40°C were analyzed by qRT-PCR.

demonstrated the most potent induction at all tested temperatures, with a dramatically stronger response compared to HSP40 and HSP70. Notably, at 39°C, HSP90 expression exhibited an exceptional, orders-of-magnitude upsurge, exceeding 700-fold relative to the baseline. In stark contrast, the induction of both HSP40 and HSP70 was minimal (less than 5-fold). Consequently, the HSP90 promoter was selected to construct the infrared light-triggered pro-angiogenic genetic circuit.

#### 2.3.3 Validation of promoter HSP90

A functional analysis of the HSP90 promoter was conducted by constructing an pHSP90-GFP expression cassette. Following thermal induction at 39°C, transformants displayed a distinct and significantly elevated fluorescent signal relative to the negligible background of the wild-type control, conclusively establishing the efficacy of the HSP90 promoter under heat-shock conditions.

![](https://static.igem.wiki/teams/5541/wetlab/results/resultnew/result-9.webp)

Img.9 Functional validation of heat-induced GFP expression driven by the HSP90 promoter.

Fluorescence microscopy analysis of GFP expression in engineered yeast harboring the pHSP90-GFP plasmid following heat shock at 39°C. The top left panel shows a bright-field image of wild-type yeast, and the top right panel shows the corresponding fluorescence image, revealing only faint autofluorescence. The bottom left panel shows a bright-field image of engineered transformants, and the bottom right panel shows their corresponding fluorescence image. Upon excitation at 492 nm, a strong green fluorescent signal is observed in the heat-shocked transformants, which is absent in the wild-type control. This result visually confirms that the HSP90 promoter is effectively activated by thermal stress, establishing it as the core regulatory component for the programmed control of pro-angiogenic factor (VEGF) expression.

## 3. Encapsulation and Delivery System

### 3.1 Characterization of the Thermosensitive Properties of L-HBC

The thermosensitive properties of L-HBC were characterized using a rheometer. The temperature was gradually increased from 4°C to 50°C. The gelation temperature, defined by the crossover point of the loss (G'') and storage (G') moduli, was determined to be 20.3°C. Given the human body temperature of 37°C, this formulation can rapidly transition from a liquid to a gel state at the wound site. However, during the mixing process with yeast, it does not gel immediately, making it convenient for application.

![](https://static.igem.wiki/teams/5541/wetlab/results/resultnew/result-10.webp)

Img.10 Temperature-dependent rheological behavior of the L-HBC hydrogel. Dynamic rheological analysis of 3% (w/v) L-HBC hydrogel during a temperature ramp from 4°C to 50°C.

The graph shows the evolution of the storage modulus (G', representing elastic solid-like behavior) and loss modulus (G'', representing viscous liquid-like behavior) as a function of temperature. The crossover point of the two curves (i.e., the gelation point) is identified at 20.3°C. Below this temperature, G'' > G', indicating a liquid state; above it, G' > G'', confirming the formation of a solid gel. This result verifies the excellent thermos-sensitivity of L-HBC. Its gelation point, being significantly below body temperature (37°C), ensures a rapid sol-gel transition upon injection into wounds, allowing it to perfectly conform to irregular wound beds.

### 3.2 Encapsulation Efficiency of L-HBC with Pexiganan-Expressing Transformants

Encapsulation efficiency was evaluated by monitoring yeast leakage into PBS from the L-HBC hydrogel over 72 hours (Img.11). The system demonstrated excellent containment within the first 24 hours, with zero leakage observed. By 48 hours, minimal leakage was detected (4 colonies), and the number of leaked colonies increased to 26 by 72 hours. This time-dependent leakage profile provides an evidence-based guideline for dressing replacement, confirming a safe therapeutic window of at least 24-48 hours.

![](https://static.igem.wiki/teams/5541/wetlab/results/resultnew/result-11.webp)

Img.11 Time-course evaluation of engineered yeast encapsulation and leakage within the L-HBC hydrogel; (a) 24-hour leakage; (b) 48-hour leakage; (c) 72-hour leakage

These results demonstrate that our encapsulation strategy achieves complete containment within the first 24 hours and largely contains the yeast for up to 48 hours. Furthermore, the clear time-dependent leakage trend offers crucial experimental evidence and a defined time window for future optimization of the encapsulation efficacy.

{% endblock %}