From Genomes to Systems

The availability of complete genome sequences for an increasing number of organisms has revolutionized biology. This has not only provided inventories of the ‘working parts’ (protein and RNA molecules) of organisms, but has also stimulated the development of technologies that allow the more or less comprehensive analysis of gene transcripts, proteins, and metabolites in a given cell type under a given set of conditions. It is the availability of these comprehensive data sets that has given birth to Systems Biology. However, Systems Biology is not so much concerned with inventories of parts but, rather, with how those parts interact to produce working units of biological organisation whose properties are much greater than the sum of their parts. Systems Biology aims at a comprehensive and integrative view of any unit of biological organisation through experiment and the use of computer models with both predictive and explanatory power. This approach is applicable to any level of biological organisation from an individual metabolic pathway or signal transduction cascade to a cell, tissue, organ, organism, population, or ecosystem. This breadth of applicability means that Systems Biology will come to permeate all branches of biology, just as molecular biology has done over the last fifty years. The complexity of biological systems is such that systems biologists need to represent them in formal models. These may be either logical models or mathematical models, but their size and complexity demands the use of computers to manipulate them. Metabolic Control Analysis (MCA) is a method modeling the relative contributions of individual effectors in a pathway to both the flux through the pathway and the concentrations of individual intermediates within it.  We have used competition analyses between the complete set of heterozygous yeast deletion mutants to reveal genes encoding proteins with high flux control coefficients. These genes may be exploited, in a top-down analysis, to build a coarse-grained model of the eukaryotic cell, as exemplified by yeast. In all, there is much to do to achieve the ultimate aim of building a comprehensive model of the eukaryotic cell that has both predictive and explanatory power. Although systems biologists often speak of discovering the ‘design rules’ of living organisms, it should be remembered that living things are the products of evolution and not design. This contributes to their complexity and suggests that it should be possible to simplify, at least, microorganisms. These simplified organisms represent “chassis” on which synthetic biologists can build artificial systems. The simplification needs to be model-driven and should, in itself, make a major contribution to our understanding of how genes interact to make a working organism.  The size and complexity of these problems demands the cooperation of human and robot scientists, with the robots not just automating the mechanical parts of the experimental process, but actually designing and interpreting experiments themselves.