Home
Team
Team Attributions
Project
Awards Contribution Description Engineering Implementation
Wet Lab
Experiments Notebook Results Safety
Dry Lab
Model Software
Human Practices
Education Human Practices

Contribution

We have introduced a suite of innovative tools and resources for iGEM teams, designed to enhance research, experimentation, and education in the field of synthetic biology. These tools include

  1. the GFP-sacB Negative Selection Marker, our new composite part to enable precise gene selection and expression tracking;
  2. STAR One-Plasmid System, efficiently generating single-stranded DNA (ssDNA) using a single-plasmid approach.
  3. the SIGNAL Workflow, streamlining data processing and RNA switch design;
  4. Large Language Models for accurate predictions in RNA switch design;
  5. Generative Models for RNA switch design optimization;
  6. the OTTER User Interface for user-friendly RNA switch design and collaboration;
  7. the Gene.io Educational Game for hands-on genetic circuit practice and learning;
  8. and a Children's Book for public engagement and outreach.

These resources would empower iGEM teams to advance their projects. Browse our wiki to learn more about each tool.

GFP-sacB Negative Selection Marker

With this, future iGEM teams can

  • employ this new composite part (BBa_K4850008 available on the iGEM Registry) for selection purposes, monitoring gene expression, or even as a biosensor for environmental and biological studies.
  • easily track the expression of genes in their constructs, leading to quicker troubleshooting and optimization.
  • precisely control and select for transformants carrying the desired genetic constructs, which is particularly valuable when working with complex genetic systems or multiplexing experiments.

STAR One-Plasmid System

With this, future iGEM teams can

  • save time and costs for future iGEM teams, allowing them to allocate resources more effectively to other critical aspects of their projects.
  • use this system to assemble genetic constructs, perform site-directed mutagenesis, and manipulate DNA fragments more efficiently, which is invaluable when working on complex synthetic biology projects.
  • iterate through their experiments more rapidly, fine-tuning their designs and achieving results quicker

SIGNAL Workflow

We have implemented the majority of this workflow during our iGEM season. With this, future iGEM teams can potentially

  • process a large volume of data in a relatively short period which significantly accelerates research and experimentation in the field of synthetic biology.
  • confidently distinguish between different RNA switch variants, even when they share similar characteristics.
  • make use of an expanded design space for RNA switches to explore diverse design possibilities with confidence.

Large Language Models

We have implemented and created the model architecture of this during our iGEM season. With this, future iGEM teams can potentially

  • rely on this language model to make accurate predictions, saving time and resources that might have been wasted on trial-and-error experiemnts.
  • utilise this architecture for a wide range of genetic engineering projects, from predicting protein-protein interactions to optimisng DNA sequences for gene expression.
  • reduce the number of iterations required to achieve desired results, thus saving precious resources and expediting the research and development process.

Generative Models

We have implemented the majority of this workflow during our iGEM season. With this, future iGEM teams can potentially

  • design and optimise RNA switches with better speed and precision.
  • be provided with a strong foundation to build upon and encourage confidence in the model's capabilities.
  • have access to mutation data derived from actual wetlab experiemnts, allowing for more accurate predictions and increased reliability in RNA switch design.

OTTER User Interface

With this, future iGEM teams can

  • input their own deep learning models into OTTER with greater ease with the user-friendly interface
  • share their designs, insights, and findings, fostering a collaborative environment where ideas and innovations can be freely exchanged.
  • harness the predictive power of these models without the need for extensive computational expertise.

Gene.io Educational Game

With this, future iGEM teams can

  • practice genetic circuit construction without the fear of making costly errors.
  • use Gene.io in classrooms, workshops, and public engagement events to introduce and educate a wider audience about the exciting possibilities within the field.
  • capture the interest and attention of learners, making complex concepts more accessible and enjoyable

Children's Book

With this, future iGEM teams can

  • use it as a tool for public engagement and outreach, furthering iGEM teams' goals of educating the public about synthetic biology.
  • can adapt and expand upon the children's book to cater to the specific educational needs of their members.
  • learn from the approach used in the children's book to create engaging and effective educational materials.