Jun 19

Jun 19 AI Development and Applications by the Future Leaders of Synthetic Biology

by Yorgo El Moubayed, Alonso Flores, and Vinoo Selvarajah

AI is advancing science in a range of ways — identifying meaningful trends in large datasets, predicting outcomes based on data, and simulating complex scenarios. As we envision AI for the future and using it to do independent scientific inquiry, there’s a lot to consider. We have to be very careful about understanding the potential of [emerging technologies] possibly affecting society in many different ways … cost, access, equity, ethics, and privacy.
— Victor Dzau, US National Academy of Medicine President

Artificial Intelligence (AI) is all over the news, and it's making waves in synthetic biology too. At iGEM, teams are leveraging AI as they push the boundaries of synthetic biology to solve some of the world’s greatest challenges. However, there is a significant data gap on how our community leverages AI tools throughout various stages of synthetic biology projects.

This year, iGEM is conducting a study of how teams incorporate AI tools in their projects and diving into the history of AI applications in all past iGEM Projects! The aim is to shed light on the best practices, challenges, and the transformative potential of AI in synthetic biology. We encourage all 2024 iGEM team members to participate in this exciting AI x SynBio study.

AI Project Examples

To give you some background and perspective on how past iGEM teams have incorporated AI in their work, check out these projects:

Munich Bioinformatics 2023 (Germany) developed graph-based deep learning algorithms applied to polypharmacy - associated with a decline in the patient's health outcomes - hypergraphs and chemical language models to predict the effects of cross talk between multiple drugs. Nominated for Best Software & AI Project, Gold Medal, Overgrad Division.

Evry-Paris-Saclay 2023 (France) overcame the challenges of performing large numbers of experiments by developing a Large Language Model (LLM)-based voice-activated assistant to run experiments on specialized hardware with as little human intervention as possible, while providing an intuitive interface, including vocal commands assistance. Best Hardware, Nominated for Best Software, Gold Medal, Overgrad Division.

TokyoTech 2022 (Japan) developed multiple machine learning models in efforts to establish a Dengue Virus serotype epidemic prediction and infection detection system. Silver Medal.

AFCM-Egypt 2021 (Egypt) built a deep learning-assisted immunotherapy platform for Triple-Negative Breast Cancer (TNBC). Bronze Medal, Undergrad Division.

IISER-Pune-India 2020 (India) applied deep learning for image processing to identify patients with malaria based on images of their blood smears, ultimately addressing issues related to poor diagnostics. Winner iGEMers Prize, Gold Medal, Undergrad Division.

Patras 2020 (Greece) applied deep learning for image processing to develop a time-saving, and portable genotyping method (based on work from UCL 2013 (United Kingdom)) for detecting genome polymorphism related to statins' metabolism. Silver Medal, Overgrad Division.

St Andrews 2019 (United Kingdom) used machine learning for protein structure prediction for engineering antibody domains to increase their stability by introducing novel crosslinking technology adapted from bacteria, and employed a variety of computational methods aimed to either inform rational design of these crosslinks or predict mutants capable of forming crosslinks. Gold Medal, Undergrad Division.

Paris-Bettencourt 2018 (France) developed AMP Designer – an open-sourced, AI-based software for antimicrobial peptide design, and built AMP Forest – a machine learning regression model for a highly accurate fitness landscape of peptide killing efficiency in a protein fusion. Nominated for Best Software Tool, Gold Medal, Undergrad Division.

Lambert-GA 2018 (United States) developed “CALM” – the Cholera Artificial Learning Model, a system comprised of four extreme-gradient-boosting machine learning models that, working together, forecast the exact number of cholera cases. Best Integrated Human Practices, Best Presentation, Best Measurement, Best Hardware, Gold Medal, High School Division.

Amazonas_Brazil 2017 (Brazil) investigated what makes an iGEM team successful, in terms of prizes and medals, by applying machine learning to recognize patterns, such as economic indicators and investment in education, science and technology, and addressing inequality and social development. Silver Medal, Undergrad Division.

CCU Taiwan 2017 (Taiwan) designed a system to evaluate the risk of dental caries through saliva using correlated parameters , and applied machine learning to generate a predictive model that works with a device to alert users of a risk of dental caries. Nominated for Best Entrepreneurship, Silver Medal, Undergrad Division.

Toronto 2016 (Canada) engineered a cell-free synthetic based bioactive paper for the detection of gold by applying machine learning methods trained using image datasets of molecules susceptible to colorimetric changes, over various concentrations. Bronze Medal, Overgrad Division.

XMU-China 2011 (China) used an artificial neural networks to model and predict the cell density of bacterial populations. Advanced to Championship, Gold Medal, Undergrad Division.

Calling all 2024 iGEM team members!

Your voice and insights are needed to help guide iGEM in adopting AI and understanding what approaches our community favors when applying AI to synthetic biology. The deadline is June 30th for the first survey. It only takes 10 minutes, so please …

Take the AI x SynBio study survey today!