Quantitative Analysis of Biochemical Data

This chapter presents a brief summary of the essentials of statistics that are particularly appropriate for handling biochemical data. This is followed by a section on the quantitative analysis of experimental results which deals chiefly with binding processes and enzyme kinetics. The chapter concludes with a brief discussion of methods of sequence analysis and databases, including a description of the FASTA and Needleman and Wunsch algorithms which form the basis of most of the sequence alignment methods currently in use. 

The results of biochemical investigations can only rarely be interpreted without some form of quantitative analysis of the experimental data. In this chapter, we describe methods that can be used for such analysis taking typical biochemical topics such as enzyme kinetics and the thermodynamics and kinetics of molecular interactions as our examples. The aim of the computer-based exercises in this chapter is to provide the reader with direct experience of methods of data analysis that, we hope, will enable them to apply these approaches to their own data. We also indude a short revision of the essentials of thermodynamics and kinetics relevant to the applications discussed. 

Mazer, N. A., and M. C. Carey. 1984. Mathematical model of bihary hpid secretion A quantitative analysis of physiological and biochemical data from man and other species. Journal of Lipid Research 25 932-953. 

The outcome of many biochemical experiments can be expressed as simple descriptive statements, such as the desired band or peak was observed . In others, such as studies of the rates of processes or the affinity of a ligand for its target, the results need to be given in quantitative form as numerical values of one or more parameters. These quantitative results are derived by mathematical analysis of the raw experimental data. As an example, Figure 8-4 illustrates the time course of an enzymatic reaction. The raw data are the dependence of product concentration (the dependent variable, conventionally shown on the y-axis) on the time (the independent variable, conventionally on the x-axis). Another example is the result of the fluorescence titration of a protein with DNA shown in Figure 7-14. In this case, the independent variable is the concentration ratio of protein/DNA and the dependent variable is the observed fluorescence intensity. 

An important extension of our large validation studies involves the use of data bases from field studies in the development of improved statistical methods for a variety of problems in quantitative applications of immunoassays. These problems include the preparation and analysis of calibration curves, treatment of "outliers" and values below detection limits, and the optimization of resource allocation in the analytical procedure. This last area is a difficult one because of the multiple level nested designs frequently used in large studies such as ours (22.). We have developed collaborations with David Rocke and Davis Bunch (statisticians and numerical analysts at Davis) in order to address these problems within the context of working assays. Hopefully we also can address the mathematical basis of using multiple immunoassays as biochemical "tasters" to approach multianalyte situations. 

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