Final Project - Ideas/Drafts

Colorimetric Cell-Free Biosensor for Heavy Metal Pollutant Detection

Illegal artisanal and small-scale gold mining locally known as galamsey has devastated river systems across Ghana, particularly the Pra, Ankobra, Offin, and Birim rivers, contaminating drinking water sources with toxic heavy metals including lead (Pb), arsenic (As), mercury (Hg), and cadmium (Cd). Existing water quality monitoring infrastructure is sparse, expensive, and inaccessible to the rural communities most affected. This project proposes the computational design and in silico validation of a multiplexed, colorimetric, cell-free biosensor capable of simultaneously detecting all four heavy metals in field-collected water samples, with results readable by the naked eye against a printed color reference card, requiring no electricity, laboratory equipment, or smartphone.

The central hypothesis is that four metal-responsive transcription factor systems (PbrR, ArsR, MerR, CadC), each coupled to a spectrally and chromatically distinct colorimetric reporter enzyme (LacZ, CrtI, BpsA, MelA), can be computationally designed, simulated, and optimized to function as a modular, freeze-dried cell-free diagnostic panel calibrated against WHO, US EPA, and Ghana EPA drinking water standards.

The four circuits are organized across two dual-circuit plasmids — GalaSense-A (PbrR-LacZ + ArsR-CrtI; Lead and Arsenic detection) and GalaSense-B (MerR-BpsA + CadC-MelA; Mercury and Cadmium detection) — each carrying two independent T7 promoter-operator units on a single backbone, balancing cost efficiency with circuit independence and colorimetric clarity.

Project Aims

Aim 1: Experimental (Computational) Aim:

The first aim of my final project is to design and computationally simulate individual cell-free biosensor circuits for detecting mercury, arsenic, lead, and cyanide by utilizing DNA construct design tools (Benchling) and gene expression modeling platforms (Asimov Kernel). This will involve constructing in silico genetic circuits incorporating metal-responsive transcription factors (MerR, ArsR, PbrR, CadC), each linked to distinct reporter outputs, and simulating their response to varying pollutant concentrations.

Aim 2: Developmental Aim:

The second aim is to refine and optimize the computational models by incorporating parameters such as promoter strength, transcription factor binding affinity, and reaction kinetics to improve prediction accuracy. This includes performing sensitivity analysis and exploring design variations to identify optimal biosensor configurations with high specificity and minimal background expression.

Aim 3 — Visionary Aim:

This project will become Africa’s first open-source, naked-eye biosensor network for community-led environmental surveillance a living, crowd-sourced map of heavy metal contamination across Ghana’s river systems, maintained not by governments or corporations, but by the communities whose water and livelihoods depend on it. In a future where synthetic biology tools are as accessible as pregnancy tests, the biosensor kits will sit in village health posts, school science labs, and the hands of environmental activists from Obuasi to Tarkwa, generating real-time contamination data that feeds into open digital dashboards, empowers legal action against illegal miners, and ultimately drives policy reform.

This project envisions a paradigm shift in environmental diagnostics: from expensive, centralized laboratory infrastructure to distributed, community-owned biosensor networks designed from the ground up for the ecosystems and people they serve.