This year, we have dedicated considerable effort to education, aiming to raise awareness about the iGEM competition, synthetic biology, and our project, ultimately encouraging greater participation and contribution to this field. Our educational initiatives have reached a diverse audience, ranging from children under 12 to the general public. We have also made a concerted effort to include students with special educational needs, fostering an inclusive environment where everyone can engage with and understand the principles of synthetic biology.

To enhance understanding, we organised various educational events that are thoughtfully designed and tailored to different age groups and backgrounds. For instance, our "Synbio Unleashed" program engaged high school students through interactive presentations and hands-on laboratory work, allowing them to explore the practical applications of synthetic biology. Meanwhile, our “Little Scientists” workshops for primary school students utilised enjoyable games and activities, such as building genetic circuits with LEGO, to introduce basic concepts in an engaging manner.

Our Youth Scientist Incubation Program (YSIP) exemplifies the mutual learning and dialogue we have established with our students regarding synthetic biology. The program originated from feedback received during our Synbio Unleashed workshop, where students expressed a strong desire to learn more about this exciting field.

This two-way interaction has been instrumental in shaping our educational initiatives. By actively listening to student comments and feedback, we have tailored our programs to better meet their needs and interests. For example, we designed in person and free hands-on activities that allow students to explore synthetic biology concepts in a practical context, fostering an environment where they can ask questions and engage in meaningful discussions.

Through YSIP, we have created a platform for students to not only learn but also contribute their ideas and perspectives on synthetic biology. This collaborative approach has enriched the learning experience for both our team and the students, as we share knowledge while also gaining insights from fresh perspectives. By establishing this dialogue with new communities, contacting schools and other iGEM teams for the final presentation and roadshow, fostering a ripple effect of empowerment. We are not only promoting awareness of synthetic biology but also empowering the next generation of scientists to take an active role in shaping the future of this field.

We believe that our long-lasting summer program, which allows ample time for students to truly incubate into future iGEMers or even scientists, sets us apart from other summer programs. We foster creativity by providing students with high flexibility in project ideation and encourage deep thinking and problem-based learning through group discussions. With this active learning approach, we believe that students can truly benefit from the experience, maximising the impact.

Moreover, we have actively sought feedback from the general public through events such as the Joint School Scientist Exhibition (JSSE), where we showcased our project and facilitated discussions about the societal impact of synthetic biology. This engagement not only helps us refine our project but also enhances broader understanding of scientific concepts among diverse audiences.

By prioritising education, we believe we have made a meaningful contribution to the fields of synthetic biology and iGEM. Our efforts not only enhance public understanding but also connect individuals from various backgrounds to engage actively with science. In the inclusivity workshop of Art meets science, we formed a long term collaboration with Caritas Lok Yi School. We tailored our teaching materials and made the complex knowledge of synthetic biology more understandable for students with moderate and severe intelligence disabilities. By using the 5 senses and art, we encourage self-expression and creativity, demonstrating that synthetic biology can be learnt from different perspectives. Their artworks are then displayed in the art gallery, to raise awareness and promote inclusivity, as well as to provoke the general public to think about synthetic biology from a diverse viewpoint.

With tailored content and one-on-one coaching, we ensure continuity of engagement throughout the iGEM journey, refining our approach based on feedback. We empathise empowerment, empower the people that we educate through their works, creating extensive impact. The unique blend of creativity and community impact distinguishes us from other teams. We are proud of our achievements and believe that our commitment to education makes us deserving of recognition as the best educational team in this year's iGEM competition.

Our team is deserving of the Best Integrated Human Practices award due to our extensive outreach efforts and the impactful feedback we received from diverse stakeholders, which significantly shaped the direction of our project. Throughout our journey, we engaged with various communities, including professors, experts in the field, and potential end-users. This engagement allowed us to gather invaluable insights that informed our project's design and implementation.

Throughout our project, we established a robust feedback loop that integrates suggestions into every phase of our development. We actively consulted with various experts, including professors who provided insights on our DHA yield model and the application of machine learning for parameter prediction; nutritionists who contributed valuable perspectives on the importance of DHA, ensuring that our project aligns with nutritional needs; and governmental departments specialising in food safety to ensure the safety of our project.

Additionally, we engaged with firms operating in similar fields to understand the current industrial challenges and pitfalls they have faced, gaining critical insights that informed our approach. This collaborative effort extended to potential end-users, as we sought to understand their primary concerns and preferences. By incorporating their feedback, we meticulously refined our project to address these factors, ensuring thoughtful and effective implementation.

By actively listening to the concerns and suggestions of those we reached out to, we were able to pivot our approach based on real-world needs rather than assumptions. For example, feedback from community discussions highlighted specific challenges faced by potential users, prompting us to adapt our solution for better accessibility and effectiveness.

Throughout our project, we dedicated considerable effort to modelling, developing three separate models that contributes significantly to our engineering cycles and in silico experimental predictions.

We worked on several approaches to computationally investigate the complex DHA production pathway, including utilization of existing genome scale metabolic models and building a simplified kinetic model from scratch. With extensive prior research effort in understanding the mechanism of PUFA synthase to the best of current knowledge, we proposed a possible branched pathway that leads to the production of the enzyme complex’s two major products. The proposed pathway was then implemented into the models, which were proven to be functional in predicting the DHA yield.

Genome scale metabolic models considers the complex metabolic network of Y. lipolytica, enabling us to predict the effect of introducing a brand-new gene cluster associated with foreign metabolic pathways. Flux balance analysis is performed to analyze the metabolic fluxes and optimize conditions for enhanced DHA production. Further analysis may also be performed on the model to identify upregulation/downregulation of expression of certain genes that may increase the flux of DHA, providing future optimization directions.

Our simplified DHA Yield Model is built with ordinary differential equations upon reasonable assumptions, and its parameters are predicted by comprehensive deep learning methods. While the genome scale metabolic model effectively predicts the behavior of the pathway within Y. lipolytica, the DHA yield model focuses more on the mathematical aspect of the enzymatic pathway itself. Our DHA yield model is proven to be functional and provides a base model with reasonably predicted initial parameters for future research work on our PUFA synthase. Future experimental data may be fitted to the model for validation and further tuning of parameters to increase the accuracy of the model.

Genome Scale Metabolic Model simulations not only provided insight into optimization of DHA production, but also potentially negative relationships between crucial reactions, such as the correlation between growth and DHA production. We observed that growth may be hindered when DHA production is maximized, and vice versa, hence the proposal of a stage control strategy, separating yeast growth and lipids production. To model this process, we constructed a batch reactor model based on ordinary differential equations optimize the growth and production of lipids in Y. lipolytica. Validated by experimental data, the model is proven able to predict correctly that utilizing glycerol as the carbon source would allow the culture to reach mid exponential phase earlier, suggesting that it would shorten the growth-production cycles, effectively increasing the efficiency of the overall DHA production process.

In summary, our comprehensive modeling efforts provided us significant insights into the mechanism and behaviour of DHA production pathways and the metabolism of Y. lipolytica. Our models would serve as a great framework and reference for future research. It holds great potential, and validation with experimental results that are to be generated in our future plan would improve its accuracy.