Modeling Plant Biosensors for Heavy Metal Detection with a Case Study of a Nickel Sensing Circuit

Abstract

Plant biosensors could help monitor heavy metal contamination by reporting the bioavailable fraction of a contaminant in living tissue. Nickel is a useful test case because plants need trace amounts of nickel, but higher exposure can damage growth and physiology. This manuscript presents a computational framework for a proposed plant nickel biosensor based on the bacterial nickel regulator NikR. The framework treats sensing as a dynamic process rather than a direct conversion from external nickel to fluorescence. The model separates nickel uptake, intracellular nickel availability, NikR activation, promoter occupancy, reporter transcription, translation, fluorophore maturation, reporter degradation, and optical background. Simulations show that sampling time, reporter maturation, promoter strength, and parameter uncertainty can strongly change the apparent dose response. We compare the mechanistic model with a simpler Hill approximation, identify influential parameters, and outline the measurements needed to test the circuit experimentally. The result is a clear modeling workflow for evaluating plant nickel sensing circuits before making claims about sensitivity, specificity, or environmental use.