Biosensors are becoming increasingly important bioanalytical tools in the pharmaceutical, biotechnology, food, and other consumer-oriented industries. Although well developed in Europe, this technology has only recently begun to generate interest in the United States and is developing slowly. Much research is now being directed toward the development of biosensors that are versatile, economical, and simple to use.
There is a critical need to provide a better understanding of the mode of operation of biosensors with the goal being to improve its stability, specificity, response time, regenerability, and robustness. Diffusional limitations are invariably present in biosensors because of their construction and principle of operation. A better knowledge of the kinetics involved in the binding and dissociation assays of the biosensors will provide valuable physical insights into the nature of the biomolecular interactions sensed by the biosensors. In addition to these kinetics, knowledge regarding the nature of the sensor surface is an important consideration in the design. However, this aspect is sadly overlooked in many texts and publications dealing with biosensors. The main aim of this book is to address the kinetics involved in analyte-receptor binding using a novel mathematical approach called fractals. We will attempt to model the binding and dissociation of an analyte and a receptor using examples obtained from literature using fractal analysis. In doing so, we wish to delineate the role of the biosensor surface and diffusional limitations on the binding and dissociation reactions involved.
In the introductory chapter, we have given a background for the need for biosensors and the different types of immunoassays. Traditional kinetics are described under the influence of diffusion on antigen-antibody binding xi kinetics in biosensors in Chapter 2. Lateral interactions are included in Chapter 3.
In our opinion, Chapter 4 is one of the most important chapters in the book as there we first introduce the concept of fractals, fractal kinetics, and fractal dimensions. We also give a background of the factors that contribute toward heterogeneity on a biosensor surface and how it can be explained using fractal kinetics. There are a host of other parameters—such as analyte/ligand concentration, regeneration conditions, etc.—that affect biosensor performance characteristics. In Chapter 5, we try to explain the influence of these parameters on the surface and consequently on the fractal dimension values.
Havlin (1989) developed an equation for relating the rate of complex formation on the surface to the existing fractal dimension in electrochemical reactions. We have extended this idea to relate the binding rate coefficient and fractal dimension for an analyte-receptor reaction on a biosensor surface. A detailed explanation of Havlin's equation and how it can be made amenable to suit our needs can be found in Chapter 6. Just as the association between the analyte and the receptor is important, the reverse (dissociation) is equally important, perhaps more so from the viewpoint of reusability of the biosensor. Recognizing its importance, we have treated the dissociation separately in Chapter 7, where we present equations that we feel can adequately describe and model the dissociation kinetics involved. We have extended Havlin's ideas and applied them successfully, with slight modifications and reasonable justifications to model the dissociation kinetics. We feel that the analysis of binding and dissociation kinetics is our contribution in the application of fractal modeling techniques to model analyte-receptor systems.
There is a very slight shift in focus in Chapter 8 as we go back to the traditional kinetic models described in Chapters 3 and 4 to describe the problem of nonspecific binding in biosensors and how design considerations may have to be altered to account for this phenomenon. We also analyze this problem using fractals in Chapter 9.
In Chapter 10, we analyze examples from literature wherein DNA hybridization reactions have been studied using biosensors. In Chapter 11, we look at cell analyte-receptor examples, and in Chapter 12 we present examples of biomolecular interactions analyzed using the surface plasmon resonance (SPR) biosensor. The SPR biosensor is finding increasing application as an analytical technique in industrial and research laboratories. We have developed expressions for relating the fractal dimensions and binding rate coefficients, fractal dimensions/binding rate coefficients and analyte concentration, and so on.
We conclude with what in our opinion is the highlight of this book: a chapter on the biosensor market economics. What makes this chapter special is the effort that has gone into compiling it from hard-to-obtain industry and market sales figures over the last several years. Although some of the projection figures may be outdated, the chapter does give the reader a feel for the costs involved, and the realistic returns on the investment involved, and the potential for growth and improvement. Just to emphasize the point and to make it easier to understand, we have presented a 5-year economic analysis of a leading biosensor company, BIACORE AB.
We have targeted this book for graduate students, senior undergraduate students, and researchers in academia and industry. The book should be particularly interesting for researchers in the fields of biophysics, biochemical engineering, biotechnology, immunology, and applied mathematics. It can also serve as a handy reference for people directly involved in the design and manufacture of biosensors. We hope that this book will foster better interactions, facilitate a better appreciation of all perspectives, and help in advancing biosensor design and technology.
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