Bronze Medal
B1. Competition Deliverables
Presentation Video (Coming Soon)
Judging Form (Submitted)
Judging Session (to be assessed during the Grand Jamboree)
B2. Project Attributions
We have carried out detailed and clear responsibilities and recorded Attributions accurately.
B4. Contribution
We have made Contributions to iGEM in various aspects, from synthetic biology parts, software to human parctice.
Silver Medal
S1. Engineering Success
For engineering part, In order to achieve our function of constructing engineering bacteria with anti-tumor function, we constructed them through two rounds of engineering cycles, which are promoter testing and Integration of Promoters with Gate Circuits for Anti-tumor Protein Expression. We successfully completed the first cycle and the second cycle is undergoing.
S2. Human Practices
We have established close connections with the community through teaching support and communication with hospitals, which not only helps us improve our projects but also helps the development of the community. In addition, human practice has also helped us establish extensive connections with experts in various fields, enhancing the content of our project.
Golden Medal
In the realm of synthetic biology and genetic engineering, our project has not only yielded remarkable results but has also demonstrated a significant societal impact. Among the multifaceted aspects of our ZJUintl 2023-China, Cancer sniper, three particular achievements stand out as remarkable due to their inherent innovation, project relevance, broad application potential, and the substantial contributions they offer to both the iGEM community and the field of synthetic biology. These aspects encapsulate our aspiration to attain the "Golden Metal" recognition.
G1. Excellence in Synthetic Biology
Best New Composite Part: We are excited to present our groundbreaking composite part, which represents a significant leap forward in the field of synthetic biology and genetic engineering. Our primary achievement lies in the first-time design and construction of a three-input conditional control system BBa_K4776013 . This innovative system is engineered to accurately replicate the conditions found within the tumor microenvironment, opening new possibilities for precise and targeted therapeutic interventions.
Mimicking the Tumor Microenvironment Our composite part is a pioneering breakthrough as it can closely mimic the complex conditions prevalent in the tumor microenvironment. By incorporating high lactate, low oxygen, and low pH condition-inducible promoters, we have established a system that accurately emulates the unique milieu within tumors. This breakthrough provides a powerful platform for researchers to develop highly precise and effective targeted therapies, an advancement with profound implications in the field of cancer treatment.
Flexible Input Alterations One of the standout features of our composite part is its adaptability. The system's design allows for flexible input alterations by simply changing the promoters. This versatility empowers researchers to customize the system to respond to different triggers, greatly expanding its potential applications beyond the realm of oncology. This adaptability positions our composite part as a versatile tool that can be harnessed for a wide range of research endeavors.
Potential Applications The applications of our composite part extend far beyond the realm of anti-tumor therapy. Researchers across diverse domains can harness this technology to create tailored, condition-responsive genetic systems. This includes applications in metabolic engineering, environmental monitoring, and the development of responsive biosensors, among many others.
Our composite part not only reflects our dedication to advancing the boundaries of synthetic biology but also promises to revolutionize how we engineer genetic systems to respond to specific environmental cues. We firmly believe that our innovative three-input conditional control system embodies the spirit of "Best New Composite Part" by offering a transformative tool for the scientific community. Its unprecedented ability to accurately simulate the tumor microenvironment while providing a high degree of adaptability positions it as an invaluable asset in the field of genetic engineering.
Best Model: We constructed mathematical models for the expression of the anaerobic promoter pPePT, the pH-sensitive promoter pCadC, and the L-lactate biosensor pLldR in MATLAB's SimBiology software, following the content from reference literature. Subsequently, we fitted our parameters by collaborating with the wet lab experimental team, specifically through fluorescence protein experiments. The fitted mathematical models accurately represent real-world conditions. With the ODE model we built, we can simulate the expression capabilities of these promoters at the molecular level under different environmental conditions.
Additionally, we developed a mathematical model for the AND logic gate. By combining these two mathematical models, we created the final expression model that can simulate the expression levels of CDD-iRGD under varying environmental conditions. This is of great significance for our synthetic biology research and engineering applications, as it offers a more in-depth understanding and predictive approach to gene expression. It aids in optimizing the design and application of gene regulation systems.
G2. Specializations
Best Software Tool: The search for chassis bacteria is one of the most crucial aspects of bacterial-based cancer immunotherapy. An appropriate chassis bacterium can significantly enhance the efficiency and specificity of drug delivery, leading to improved treatment outcomes. However, bacteria of different species are entirely distinct entities, and simple biological methods are insufficient for selecting an ideal chassis bacterium.
Therefore, we have developed a machine learning program that leverages specific environmental bacterial expression data to construct environmental profiles. This allows us to screen all potential engineered or non-engineered bacteria. Consequently, we can select the environment that best suits our needs and conduct similarity scoring. The bacterium with the highest score is chosen as the optimal chassis bacterium.