iGEM Toronto 2016

Project Overview: The 2016 iGEM Toronto team developed a synthetic biological sensor for detecting gold using GolS, a member of the MerR family of transcriptional regulators. My primary role was leading the computational team, where I focused on developing a colorimetric analysis app and modeling the GolS protein structure to enhance its gold-binding specificity.

Objectives

1. Develop a mobile application capable of precise colorimetric analysis to measure gold concentrations in samples from our wet lab’s paper-based biosensor.
2. Model the protein structure of GolS, a gold-binding protein, and compare it with other metal-binding proteins to identify mutations that enhance gold affinity.

Features

1. Colorimetric Analysis App:

   • Developed an iOS app using Apache Cordova for real-time colorimetric image analysis.
   • Enabled quantification of gold concentration via smartphone photos with advanced colorimetric algorithms.

2. Protein Structure Prediction:

   • Modeled GolS structure and its mutants using Rosetta and PyRosetta for enhanced gold-binding specificity.
   • Predicted gold-binding capabilities of GolS variants to guide experimental validation.

3. Linear Regression Analysis:

   • Employed Scikit-learn to perform linear regression analysis for quantifying colorimetric changes.
   • Implemented a machine learning algorithm to correlate color intensity with gold concentration.

Tech Stack

• Frontend: Apache Cordova
• Backend: Scikit-learn
• Database: Cordova-sqlite-Storage
• Computational Tools: Rosetta, PyRosetta

Outcome

The iGEM Toronto 2016 project successfully created a gold-specific biosensor and an accompanying colorimetric analysis app, providing a portable, user-friendly tool for accurate gold detection. The integration of advanced computational models and machine learning techniques ensured high specificity and reliability in real-world applications. Our efforts were recognized at the iGEM Jamboree 2016, where we achieved a bronze medal.