Bioinformatics pipelines: The Smoke Detector in the Fight Against Pandemics

This project was a runner-up for the "Simple Explainer" prize on our Pandemics (Sept 2024) course. The text below is an excerpt from the final project.

The Potential of Bioinformatics pipelines as a Resource for Biosafety and Biodefense

Bioinformatics pipelines: The Smoke Detector in the Fight Against Pandemics
In a world where urban fires ravaged cities, one key innovation changed the course of history: smoke detectors. These tools did not extinguish flames or replace firefighters, but they provided something essential: an early warning that allowed action before the fire spiraled out of control. They were not the complete solution, but they became an indispensable piece in the prevention system.

In this report, bioinformatics is addressed as an essential part of monitoring, detecting, and containing pathogens. We will explore its capabilities and limitations, as well as how it can be integrated into a global surveillance system. So far, this system has only focused on outbreak detection and addressing emerging public health needs. However, it lacks specific solutions to identify synthesized or artificially modified pathogens.

This work aims to fill this gap by explaining how a bioinformatics pipeline works. First, it details how artificial intelligence could be integrated to design advanced machine learning algorithms capable of identifying suspicious genetic patterns and design markers. Secondly, it proposes the creation of an expanded database incorporating known synthetic organisms, enabling more precise and effective detection. Additionally, the establishment of an international consortium is suggested to develop data-sharing standards and promote global collaboration.

This possibility has the potential to transform bioinformatics surveillance, enabling earlier and more precise identification of synthetic biological threats. For example, implementing these systems could quickly detect artificial genetic modifications in pathogens such as SARS-CoV-2, facilitating faster responses to emerging outbreaks. Moreover, a strengthened system could prevent catastrophic scenarios by identifying suspicious genetic patterns even before they clinically manifest.

Full project

View the full project here