Contribution
Overview
In the wetlab, we developed several innovative BioBricks through a modular design approach and integrated them into an efficient and controllable "sense-response" system. It provides new tools, strategies and valuable resources for the future research of synthetic biology in the field of intestinal microecology treatment. In order to promote knowledge sharing and reproducibility, we have also developed a detailed experimental operation manual for our laboratory to help future NKU-China teams get started quickly.
In the dry experiment section, we systematically develop a complete modeling paradigm from parameter calibration, data-driven prediction to cross-scale integration.Transform the uncertainty of parameters into mechanism exploration, and achieve rapid prediction through data-driven approaches when the mechanism is difficult to clarify and objective conditions are limited. Finally, a cross-scale analysis chain from molecule to system is constructed. This transferable methodology marks an important leap from project application to systematic innovation of team modeling.
In addition, in order to help the establishment of the next NKU-China team of Nankai University, we provided experience sharing and guidance. Inherit the fire, live and die.
Parts
1. A Novel TMA-Responsive Biosensor
Our project has pioneered the design and construction of a novel trimethylamine (TMA) biosensor in yeast. The core of this system is an innovative signal reception unit, consisting of a fusion protein between the human TAAR5 receptor with a yeast membrane-localizing signal peptide and a modified version of the endogenous yeast Gpa1 protein.
To ensure efficient signal transduction, we precisely engineered the C-terminus of Gpa1 by replacing its native five amino acids with the C-terminal sequence of human Gα₁. This modification significantly enhanced the compatibility between the chimeric receptor and the downstream yeast signaling pathway. The sensor effectively converts disease-relevant TMA concentrations in the gut into activation of downstream gene expression.
We have submitted the following three basic parts to the registry:
| name | code |
|---|---|
| TAAR5 receptor (with N-terminal yeast membrane localization signal) | BBa_251M1Q8U |
| Gpa1/Gα fusion protein | BBa_25EJSV4B |
| pFUS1 promoter | BBa_25XWUN58 |
This innovation not only provides a new biological tool for TMA detection but also demonstrates the feasibility of using G protein-coupled receptors (GPCRs) in yeast for sensing specific signal molecules. It paves the way for developing GPCR-based biosensors in Saccharomyces cerevisiae.
Furthermore, this system can be repurposed as a biological switch: by using exogenous TMA as an input and placing a gene of interest under the control of the pFUS1 promoter, then the user-defined functions can be implemented.
2. A ROS and TMA Dual-Sensing “AND Gate” Switch
To enhance the diagnostic precision of our engineered yeast in the complex gut environment, we innovatively designed and constructed an “AND Gate” logic circuit responsive to both ROS and TMA. The key design feature is the placement of the TAAR5 receptor—a critical component of the TMA sensor—under the control of the ROS-responsive promoter pTRR1. This ensures that TMA receptor synthesis occurs only under high ROS levels (typically associated with inflammation and cellular stress), thereby “unlocking” TMA sensing capability. This design elegantly implements the logical condition “(ROS present) AND (TMA present)”, ensuring that the synthesis of therapeutic butyrate is triggered only when both early biomarkers of Alzheimer’s disease are simultaneously detected. This significantly improves system specificity and prevents false positives due to fluctuations in a single signal, offering a new design paradigm for smarter and more precise live biotherapeutics.
In addition to the previously described parts, we have submitted:
| Name | Code |
|---|---|
| pTRR1 promoter | BBa_25RNTN9I |
Our experiments confirm that the pTRR1 promoter can effectively respond to free radical stimulation, making it suitable as a versatile reactive oxygen species (ROS)-responsive element in S. cerevisiae. It provides a valuable reference for future designs of ROS-sensing biological parts.
3. Intelligent Butyrate Synthesis with a Negative Feedback Loop
During the middle stage of the project, we introduced a precise negative feedback regulatory circuit to prevent potential butyrate overproduction. The system consists of genetic components including Lrp protein, pPcha promoter, TetR protein, and the TETR promoter. When butyrate produced by the engineered yeast reaches a certain concentration, it binds to Lrp, activating the TetR system. TetR protein then suppresses the TETR promoter, halting expression of a rate-limiting enzyme in butyrate synthesis (BCoAT), thereby achieving “auto-shutdown” of production.
It should be emphasized that the Lrp/pPcha system is derived from BMU-2023, and their design provided us with inspiration.
This is a complex element containing multiple genes. We have decomposed it into 4 basic components and have uploaded them. The components include:
| name | code |
|---|---|
| Lrp protein | BBa_25MIZ9C7 |
| pPcha promoter | BBa_252GI6E4 |
| TetR protein | BBa_25OZXWPX |
| pTETR promoter | BBa_25T2KDPN |
We hope that this intelligent regulation mechanism can prevent the excessive accumulation of butyrate from causing metabolic burdens and potential toxicity to the yeast itself, and can ensure the long-term stability of the engineered yeast in the intestinal environment and the stability of the therapeutic effect.
This design provides an excellent example for the future construction of "intelligent cell factories" that can self-regulate the concentration of products. All that is needed is to replace the Lrp/pPcha system with the corresponding system that can sensitively recognize other substances, and place TETR ahead of the key synthetic enzymes, so as to achieve intelligent response and negative feedback pathways for this substance.
4. Controllable Suicide System with High Biosafety (A Controllable Suicide System for Enhanced Biosafety)
Biological safety is the core issue for all engineering biological applications. Therefore, in our project, we have integrated a tightly controlled suicide system. This system consists of a regulatory protein that integrates rtTA3, VP16 and NLS (nuclear localization sequence), and a pro-apoptotic protein Bax controlled by the TETR promoter. Finally, this system is protected by two insulator sequences, which can prevent the accidental activation by the yeast's own transcriptional activation domain and the incorrect inhibition by the silencing mechanism, thus preventing the expression from leaking or being suppressed.
Only in the presence of specific inducers (such as tetracycline or doxycycline) will the regulatory protein activate the expression of Bax, thereby initiating the cell apoptosis program and efficiently eliminating the engineered yeast from the body. This design provides a reliable safety guarantee for our in vivo biological therapy, ensuring that it can be rapidly removed from the intestinal environment by human intervention when the treatment is over or in case of unexpected situations (such as immune rejection or IBD).
This is a complex element containing multiple genes. We have decomposed it into 4 basic components and have uploaded them. The components include:
| name | code |
|---|---|
| rtTA3 protein (containing VP16 sequence + yeast NLS) | BBa_25DVG514 |
| TETR promoter | BBa_25T2KDPN |
| Bax protein | BBa_257KIJW9 |
| Insulator | BBa_25TX0FU3 |
5. Composite biological component:
TMA signal detection and reporting module
This hybrid bioelement was used as our TMA signal detection and reporting system to identify trimethylamine (TMA), a pathological marker associated with Alzheimer's disease. To express the human TAAR5 receptor on the yeast cell surface, we fused a 267bp yeast membrane protein sorting signal sequence encoding 89 amino acids to the N-terminal region of TAAR5. When TMA binds to the TAAR5 receptor in the gut environment, the activated receptor initiates intracellular signaling through the chimeric G protein GPA1/Gα1, triggering the endogenous MAPK signaling cascade in yeast, which ultimately allows the Ste12 transcription factor to bind to the pFUS1 promoter and drive EGFP reporter gene expression. Through codon optimization and cross-species protein engineering, the system successfully responds to the pathological signals of Alzheimer's disease-related TMA in yeast and achieves precise pathological responsive treatment.
Special attention is paid to the fact that other promoters of this composite element have been carefully selected by us and verified by many experiments to perform well.
This is a composite element composed of multiple genetic elements, which we have uploaded:
| name | code |
|---|---|
| TMA detection and reporting complex system in yeast | BBa_25L5UP5N |
ROS signal detection and reporter module
This composite biological element serves as our oxidative stress response system, used to detect intracellular reactive oxygen species (ROS) levels and convert them into visual fluorescence signals. pTRR1 is a promoter derived from yeast itself, and its core functional element is a specific oxidative stress response element. As an output signal, the expression level of EGFP directly reflects the activation intensity of the pTRR1 promoter, thereby achieving real-time monitoring and visualization of intracellular ROS levels.
This system achieves a sensitive response to ROS by utilizing the endogenous oxidative stress response pathway in yeast, providing a reliable molecular tool for pathological microenvironment detection and the conditional activation of precise therapeutic genes.
Special attention is paid to the fact that other promoters of this composite element have been carefully selected by us and verified by many experiments to perform well.
This is a composite element composed of multiple genetic elements, which we have uploaded:
| name | code |
|---|---|
| ROS detection and reporting complex systems in yeast | BBa_25VXKMA5 |
6. Systematic Engineering of Multi-Module Integration and Future Prospects
The outstanding contribution of our project lies in the successful integration of four major functions - the signal sensing module (TMA/ROS sensor), the product synthesis module (butyrate synthesis pathway), the intelligent regulation module (negative feedback loop), and the biological safety module (suicide system) - into a single engineered yeast strain. This highly modular design demonstrates the great potential of achieving complex life activity regulation within a single chassis organism, enhancing the stability and functional diversity of the system, and providing valuable design ideas and practical experience for the future team.
We hope that our work can inspire the future iGEM teams, and we hope that future projects can:
Optimize sensors: Through directed evolution or replacing other GPCR receptors, modify our sensor platform to enable it to respond to a wider range of intestinal signaling molecules (such as other metabolites, inflammatory factors, etc.).
Expand output functions: Connect our sensing system with different effect modules (such as synthesizing other drug molecules, secreting antimicrobial peptides, etc.) to develop "customized" engineered yeast strain for different intestinal diseases.
Construct more complex logical circuits: Integrate multiple input signals to build logical circuits such as "AND gates" or "OR gates", enabling the engineered yeast strain to make more precise judgments and responses based on complex intestinal environment signals.
Standardized experimental operation manual and resource sharing
In addition to innovative biological components and system designs, our team is also committed to contributing a valuable asset to NKU's future iGEM team - a detailed and standardized "NKU-China Experimental Standard Operating Manual". This manual aims to provide a clear and reproducible operation process for future teams engaged in related research (especially those using yeast or mammalian cells as the chassis), thereby lowering the threshold for project initiation and increasing the success rate of experiments.
Comprehensive and systematic content coverage: The manual content starts from the laboratory layout and safety guidelines, detailed user guides for core instruments, to the key experimental operation procedures in the project (due to space limitations, only some experiments are listed).
Attention to detail and reproducibility: We not only document the experimental procedures but also include a large number of key details and precautions to ensure the success of the experiments. For instance, we have clarified the precise speed Settings of the shaker at different culture stages, the significance and specific methods of sample balancing before centrifugation, as well as the strict division between contaminated and non-contaminated areas during gel electrophoresis imaging.
Promoting a culture of safety and norms: The manual emphasizes the importance of biosafety and sets strict regulations on aseptic operations and the disposal of waste containing live bacteria, which helps to promote a responsible research culture in the iGEM community.
Display part of the experimental manual
We believe that this manual, which embodies the experience and wisdom of our team, will become a valuable public resource, helping future IGemers walk further and more steadily on the shoulders of their predecessors.
We have submitted all the key components mentioned above to the iGEMPartsRegistry and made our experimental manual public. We welcome everyone to explore and use it!
Contribution of Dry Lab
1. Modeling paradigm based on parameter calibration
In metabolic modeling, literature studies often focus more on mechanisms rather than precise parameters. As a result, model groups often encounter the following predicaments: the form of the kinetic equation is easy to determine (the mechanism is clear), but specific parameters are difficult to obtain - a small number of parameters come from accurate direct measurements, while more are extrapolated from other species or indirectly estimated, with low credibility. Based on this common predicament, we have developed a modeling paradigm based on parameter calibration. The calibration framework we developed is divided into two layers: The first layer is baseline calibration: We use high-confidence parameters as fixed values and combine reliable target data to reverse-calculate the reasonable values of uncertain parameters, thereby establishing a baseline model with reliable parameters. The second layer is mechanism exploration: On the calibrated baseline model, we can study the changes in disease states through the model. Our key innovation lies in the fact that we set parameter range constraints based on biological knowledge - which physiological processes should remain relatively stable in diseases and which may develop lesions. Different constraint Settings correspond to different hypotheses of pathological mechanisms.
Under our modeling paradigm, by adjusting the combination of constraints based on mechanism research, the "parameter fitting problem" can be transformed into a "mechanism exploration tool". The core of the entire framework is: to embody biological prior knowledge through mathematical constraints, enabling the model not only to fit the data but also to reveal the mechanism. In the future, iGEM teams engaged in metabolic modeling, pharmacokinetics or disease modeling can apply this modeling paradigm to greatly enhance the credibility of parameters, systematically integrate biological prior knowledge, and explore verifiable mechanism hypotheses from the model.
2. Data-driven modeling based on the BBB model as a template
In drug design and research on neurological diseases, predicting whether small molecules can pass through the blood-brain barrier (BBB) is a key issue. The traditional kinetic modeling methods require detailed pharmacokinetic parameters and complex mechanism models, which are not practical for the rapid screening of a large number of candidate molecules. Facing such prediction tasks with complex mechanisms and difficult-to-obtain parameters, we have developed a data-driven research paradigm: using existing experimental data, we establish a prediction model between molecular structure and properties through ensemble learning. Taking the prediction of blood-brain barrier permeability (logBB value) as an example, we developed a model that integrates systematic data preprocessing and a robust ensemble learning framework. Users only need to input the SMILES string to quickly obtain the prediction result of the plasma-cerebrospinal fluid distribution coefficient, significantly reducing the reliance on mechanism knowledge and complex parameters. We have also developed a user-friendly prediction interface that can be directly integrated into the wet experiment screening process.
This data-driven paradigm has broad transferability: in the future, iGEM teams engaged in drug design, delivery systems, or those needing to rapidly screen a large number of candidate molecules, when confronted with biological processes that are difficult to elucidate in terms of mechanism or obtain parameters for (such as membrane permeability, drug metabolic stability, tissue distribution, etc.), can all adopt a similar framework - collecting existing data, extracting molecular features, and establishing an ensemble learning model to quickly obtain reliable predictive capabilities in the absence of a complete understanding of the mechanism, and accelerating the design-test cycle.
3. Cross-scale integration modeling paradigm from a system perspective
In complex synthetic biology projects, multiple biological scales are involved, ranging from molecular mechanisms to therapeutic effects. For biosafety reasons, animal experiments are strictly restricted. In vitro models such as organoids are costly and difficult to simulate multi-organ interactions. In this case, establishing a cross-scale integrated computational model becomes a key tool for understanding the complete treatment process. We have developed a methodology to form a systematic prediction framework from mechanism to efficacy by clarifying the connection relationships among models of different scales.
The core of cross-scale integration lies in identifying three connection methods between scales:
Confirmatory support: The upstream scale provides feasibility basis for downstream design (such as omics verification of the clinical relevance of targets, molecular docking verification of the rationality of receptor recognition)
Parameter transfer: The quantitative output of the previous scale directly becomes the input of the next scale (for example, the product flux predicted by the metabolic model is transferred to the pharmacokinetic model, and the logBB value predicted by machine learning is embedded in the distribution model)
Threshold constraints: The results at a certain scale define the boundary conditions at other scales (such as the binding energy of molecular docking constraining the parameters of kinetic models, and the concentration verification of the activation feasibility of induction modules through the reverse deduction of pharmacokinetic models)
By systematically designing these connections, models of various scales jointly answer the questions of "whether the design is feasible and whether the effect can meet the standards", rather than modeling them in isolation.
In the future, iGEM teams engaged in the design of complex therapeutic systems, especially those constrained by experimental conditions, can apply this methodology: identify the key biological scales of the project, clarify the connection relationships (verification/transfer/constraint) between each scale, and establish corresponding model interfaces, thereby achieving a systematic understanding and prediction of the entire process under limited experimental conditions.
We have uploaded the specific model to Model page.
Passing on the Torch: Our Commitment to the Future of NKU-China
As part of our strong commitment to fostering a vibrant and sustainable synthetic biology community, the NKU-China 2025 team has put a lot of effort into nurturing the next generation of Igemers in our school. Recognizing the importance of knowledge transfer and the persistence of team memory, we implemented a multifaceted plan to support the formation and preparation of the NKU-China 2026 team.
A comprehensive on-campus outreach campaign was launched to reignite interest in iGEM and synthetic biology. This included designing and distributing eye-catching posters and sharing our entry journey through the school's official social media channels, which successfully reached a wide audience across different faculties.
Our original poster is presented above, introducing synthetic biology and our project, respectively
To reinforce this interest, we set up a dedicated discussion group on Lark. The group became a hub for answering questions, sharing resources, and interacting with potential applicants, attracting more than 100 enthusiastic students from diverse academic backgrounds.
In addition, we planned and implemented a series of training activities, culminating in a flagship campus information session. In this event, we introduced the core concepts and spirit of iGEM, demystied the fascinating field of synthetic biology, and honestly shared our first-hand experience from initial project conception to wiki creation. By highlighting the practical lessons we have learned, we aim to provide knowledge and inspiration to our successors so that they can embark on their own iGEM journey with confidence.