Just as it was possible to take a whole-organism (all of the genes) approach to human DNA sequencing, it is now possible to consider a whole-organism approach to determining the mechanisms that control the biochemistry in a cell. An understanding of these mechanisms is the basis of disease prevention and treatment. High-throughput whole-organism approaches include genomics; proteomics; functional genomics; and structural genomics for the study of genes, proteins, gene function, and three-dimensional protein structures, respectively. These approaches contrast with and complement the hypothesis-driven research tradition in biology of studying a single isolated phenomenon. The next generation of hypotheses will address an entire complex activity such as metabolism, which requires information about multiple protein-DNA interactions of the cell's regulatory mechanisms.
As shown in Fig. 4.1, an integrated approach should have the potential to go from genes (DNA) to function. This chapter presents approaches to collecting gene, protein, and regulatory data and the challenges of integrating these data into an information system model of the biology. We include in this chapter a brief description of how computer modeling and simulation might facilitate data interpretation and an understanding of complex biochemical pathways and mechanisms.
There are several levels of data abstraction in biology. The basic unit is the one-dimensional gene that is composed of the four building blocks A, C, G, and T. The next level of abstraction is a protein. Proteins are three-dimensional combinations of the 20 amino acids, with structure strongly correlated with function. Pathways are the next level, with many pathways often combining for a robust activity such as metabolism. Systems-level biology is the new frontier and involves the pursuit of mathematics, computer algorithms, and biological data to show that complex networks of organisms (even ecosystems!) may be amenable to modeling and measurement. These are new ways of viewing biological research. Biology is becoming an information-based science.