Contribution stands as a core value of the iGEM competition, emphasizing collaboration, innovation, and the ethical foundation of research. As iGEM Thessaloniki 2025, we wanted to share the resources, tools, and knowledge we gained throughout our iGEM journey, so that future iGEM teams can build upon our work and enhance it.

On this page, we present the contributions we developed throughout our journey. These range from experimental resources and datasets to educational tools and accessibility initiatives. By making them openly available, we aim to support future teams in advancing their work more efficiently, while promoting inclusivity and responsible research practices.

Click on each button to review our Contribution to the iGEM Community!

"When everyone is included, everyone wins.", said Jesse Jackson. From the very beginning of building our Team Wiki, we set the goal of designing a platform that is accessible to everyone, regardless of visual, cognitive, or learning differences. To achieve this, we brainstormed ways to implement accessibility features directly into our Wiki and created the Wiki Accessibility Button.

The Wiki Accessibility Button provides visitors with a range of customizable options to improve readability and navigation:

• Increase Font Size: Enhances text size for users with low vision.
• Hide Images: Reduces visual overstimulation, particularly beneficial for individuals with epilepsy or ADHD.
• Adjust Paragraph Spacing: Increases spacing between paragraphs, improving readability for visually impaired users.
• Adjust Line Spacing: Adds extra space between lines, making text easier to follow for users with low vision or dyslexia.
• Dyslexia-Friendly Font: Switches text to a font designed to improve legibility for people with dyslexia.
• Text Alignment Options: Allows users to choose left-aligned or justified text for better readability depending on preference.
• Highlight Links: Clearly marks hyperlinks, improving navigation for users with visual impairments or cognitive challenges.
• Pointer Customization: Provides alternative cursor styles for users who find standard pointers hard to track.

We created this feature not just for our own Wiki, but as a contribution to the entire iGEM community. To ensure that every future team can easily integrate it, we have documented and uploaded the full code for the Wiki Accessibility Button (HTML/JavaScript and CSS) on our GitLab Repository. By sharing this resource, we hope to make iGEM Wikis more inclusive, accessible, and user-friendly, so that everyone can engage with the amazing science being presented.

During the development of our project, we designed and optimized a series of detailed protocols for our experiments that guided our wet-lab work. These protocols cover the full workflow of our experimental pipeline, from siRNA transfection into MEC-1 cells to RNA extraction, qRT-PCR analysis, and cell viability assays. More specifically, the protocols we contribute are:

• Cell Thawing and Reculture
• siRNA Transfection
• INTERFERin Titration
• RNA Extraction
• Electrophoresis of RNA from MEC-1 cells
• qPCR for mRNA of BCL-2 Expression
• Treatment of GSK126 Inhibitor in MEC-1 cells
• Fluorescence Detection with a Multi-Mode Microplate Reader

We recognized early on that reproducibility is a common challenge in synthetic biology and molecular biology research, as protocols often vary slightly between labs or are not thoroughly documented. To address this, we carefully standardized, annotated, and validated each step of our procedures, recording not only the methods but also critical notes, troubleshooting tips, and optimized conditions that we discovered along the way, all shared in our Lab Notebook.

By sharing these protocols, we aim to provide future iGEM teams with a reliable starting point for similar RNA-based experiments and contribute to the iGEM community by ensuring that our work can be reproduced, tested, and expanded. Our protocols are available on our Experiments Page.

Building a high-quality and reliable dataset was both a difficult and necessary step for our project modeling, laying the foundation for the development of our machine learning models. Multiple public siRNA sequence datasets exist, however when these datasets are compared to each other, some issues arise:

• Inconsistent efficacy reporting: Silencing efficiencies are experimentally measured using different assays, protocols, and cell lines, and are often reported on different scales.
• Heterogeneous sequence formats: siRNAs vary in length (commonly 19 nt or 21 nt) and in orientation (5’-3’ or 3’-5’), making direct integration challenging.
• Data inconsistencies: Several datasets contain minor errors that undermine reproducibility.

Recognizing these challenges, we took the initiative to create siRBench: the first harmonized training dataset that integrates and standardizes siRNA data from diverse sources. By curating, cleaning, and aligning thousands of sequences into a single dataset, siRBench provides the iGEM community with a reliable foundation for training models in order to predict siRNA silencing efficacy.

This dataset not only underpins the machine learning models we developed but also serves as an open contribution to future iGEMers. The siRBench dataset is available at our GitLab Repository, ensuring that others can build upon, validate, and expand this work. We also have extensively documented the pipeline we followed while creating our harmonized dataset on our Software Page.

Along with our harmonized training dataset, we offer to the iGEM community our siRNA silencing efficacy prediction models as an additional contribution. We designed and developed two models in order to receive predictions about the silencing efficacy of the candidate siRNAs we found by conducting literature review and the candidate siRNAs we designed ourselves.

More specifically, we proposed two different model architectures: the LightGBM-Based Model (Light Gradient Boosting Machine) and the TabPFN Regressor (a transformer-based model pre-trained on millions of synthetic tasks). Each of these models offer multiple advantages for our task, and both of them gave us important predictions regarding the expected siRNA silencing efficiencies.

Both models are well-documented at our GitLab Repository, while an extensive documentation of their building-process can be found at our Software Page.

Through the year, we organized and conducted multiple educational activities and thus, we created countless materials for their implementation. We designed all these materials meticulously, ensuring that they can be easily deployed for productive educational endeavors. We have documented all the materials we created, which is a significant contribution for future iGEM teams who wish to pursue this path and build upon our work. Our Education Pack for Future iGEM Teams contains:

• A Coloring Book for drawing experiment equipment and microorganisms, ideal for primary school students
• A Career Orientation Presentation, that showcases university majors related to the field of synthetic biology
• A Bioethics Debate Booklet, for conducting a debate related to ethical dilemmas in the field of synthetic biology
• A Fairytale, called “The Silent Sisters: Serena & Sienna Saving the city of Verosia”, that describes the siRNA action mechanism through a memorable story addressed to little children
• Memory Cards depicting lab tools and microorganisms

More information regarding our educational activities can be found on our Education Page.