Skip to main content
eScholarship
Open Access Publications from the University of California

M13 Bacteriophage Based Protein Sensors /

Abstract

Despite significant progress in biotechnology and biosensing, early detection and disease diagnosis remains a critical issue for improving patient survival rates and well-being. Many of the typical detection schemes currently used possess issues such as low sensitivity and accuracy and are also time consuming to run and expensive. In addition, multiplexed detection remains difficult to achieve. Therefore, developing advanced approaches for reliable, simple, quantitative analysis of multiple markers in solution that also are highly sensitive are still in demand. In recent years, much of the research has primarily focused on improving two key components of biosensors: the bio-recognition agent (bio-receptor) and the transducer. Particular bio-receptors that have been used include antibodies, aptamers, molecular imprinted polymers, and small affinity peptides. In terms of transducing agents, nanomaterials have been considered as attractive candidates due to their inherent nanoscale size, durability and unique chemical and physical properties. The key focus of this thesis is the design of a protein detection and identification system that is based on chemically engineered M13 bacteriophage coupled with nanomaterials. The first chapter provides an introduction of biosensors and M13 bacteriophage in general, where the advantages of each are provided. In chapter 2, an efficient and enzyme-free sensor is demonstrated from modified M13 bacteriophage to generate highly sensitive colorimetric signals from gold nanocrystals. In chapter 3, DNA conjugated M13 were used to enable facile and rapid detection of antigens in solution that also provides modalities for identification. Lastly, high DNA loadings per phage was achieved via hydrozone chemistry and these were applied in conjunction with Raman active DNA-gold/ silver core/shell nanoparticles toward highly sensitive SERS sensing

Main Content
Current View