Advancing low-cost synthetic biosensor arrays for rapid multiplex biomarker detection - the complexity and heterogeneity of diseases such as sepsis and allergy arise from diverse pathophysiological…

The National Institute of General Medical Sciences awarded Michigan Technological University $383,801 on May 1, 2026, under the Biomedical Research and Research Training program (CFDA 93.859) to develop cost-effective, scalable biosensing platforms for rapid multiplex biomarker detection in sepsis, allergy, and other complex diseases.

The research focuses on molecularly imprinted polymers (MIPs)—synthetic receptors that mimic antibody recognition with superior stability and lower production costs than conventional immunosensors. Over five years through February 28, 2031, the principal investigator's lab will establish an adaptable imprinting platform for rapid synthesis of MIP-based biosensors tailored to amino acids, peptides, and proteins, integrating computational modeling, machine learning, and experimental validation to optimize imprinting efficiency in physiological environments. The project advances multiplexed biosensing by integrating novel transduction mechanisms including redox-integrated MIP sensors and extended-gate field-effect transistor (EG-FET)-based MIP sensors coupled with machine learning approaches.

The research builds on the lab's prior demonstration of MIP-based electrochemical sensors for small molecules and protein targets, including SARS-CoV-2 spike protein and stress biomarkers. Current diagnostic platforms lack the scalability, affordability, and multiplexing capacity for widespread clinical implementation; host response-based diagnostics driven by endotype classification offer disease stratification and personalized treatment through biomarker-based profiling. Work is performed in Houghton, Michigan.

Advancing low-cost synthetic biosensor arrays for rapid multiplex biomarker detection - the complexity and heterogeneity of diseases such as sepsis and allergy arise from diverse pathophysiological mechanisms and variable patient responses to treatment. Traditional diagnostic approaches relying on clinical phenotypes fail to capture the underlying molecular variability, leading to ineffective therapies and high healthcare costs. Host response-based diagnostics, driven by endotype classification, offer a promising solution by enabling disease stratification and personalized treatment through biomarker-based profiling. However, current biosensing platforms lack the scalability, affordability, and multiplexing capacity necessary for widespread clinical implementation. This research program aims to revolutionize biomarker-based diagnostics by developing a cost-effective, scalable, and multiplexed biosensing platform based on molecularly imprinted polymers (mips). Mips, synthetic receptors mimicking antibody recognition, offer superior stability and lower production costs compared to conventional immunosensors. The PI's lab has already demonstrated MIP-based electrochemical sensors for small molecules and protein targets, including sars-COV-2 spike protein and stress biomarkers. Building on this foundation, the next five years will focus on three key objectives: 1) establishing an adaptable imprinting platform for the rapid synthesis of MIP-based biosensors tailored to amino acids, peptides, and proteins, inspired by nature's antibody generation system. This approach will integrate computational modeling, machine learning, and experimental validation to predict and optimize imprinting efficiency in physiological environments. 2) advancing multiplexed biosensing technology by integrating novel transduction mechanisms with scalability, such as redox- integrated MIP sensors and extended-gate field-effect transistors (EG-fets)-based MIP sensors, coupled with machine learning-driven signal deconvolution for multiplex biomarker detection. 3) developing a seamless sample-to-answer platform incorporating electrospun nanofiber-based whole blood separation and passive fluidic delivery to facilitate biomarker detection directly from complex biological samples without extensive preprocessing. Our research will initially focus on two key applications: (1) an amino acid biosensor array targeting branched-chain and aromatic amino acids, which are biomarkers for metabolic and neurological disorders; and (2) a multiplexed biosensor for sepsis biomarkers, integrating cytokines, acute-phase proteins, and metabolic indicators for early diagnosis and patient stratification. By integrating expertise in materials science, computational chemistry, electronics, and machine learning, our program will establish a next-generation biosensing framework that is cost-effective, scalable, and adaptable to diverse disease applications. The proposed work will bridge fundamental molecular recognition with real-world clinical translation, accelerating biomarker-based diagnostics, improving patient outcomes, and advancing precision medicine.