Systems Biology: Its Practice and Challenges

Systems biology is a comprehensive quantitative analysis of the manner in which all the components of a biological system interact functionally over time. Such an analysis is executed by an interdisciplinary team of investigators that is also capable of developing required technologies and computational tools. In this model, biology dictates what new technology and computational tools should be developed, and, once developed, these tools open new frontiers in biology for exploration. Thus, biology drives technology and computation, and, in turn, technology and computation revolutionize biology. Systems approaches have long been taken, particularly in immunology, physiology, development, and neurobiology. However, technology development during the 1980s permitted the concepts generated by many years of reductionist inquiry to be analyzed in the context of the entire system. Automated DNA sequencers enabled the sequencing of genomes and the definition of polymorphisms among individuals; microarray analysis permitted global transcriptional profiling, and advances in mass spectrometry led to large-scale proteomic and metabolomic analysis. The mountains of data generated by these high-throughput platforms led to the rapid growth of computational biology and bioinformatics. Thus, knowledge of the complete sequences of genomes, together with technology that permit the monitoring of information flow leading to specific cellular functions, set the stage for systems biology. There are three basic concepts that are crucial to understanding complex biological systems: they are emergence, robustness, and modularity. Emergence. Complex systems display properties, often called “emergent properties,” that are not demonstrated by their individual parts and cannot be predicted even with full understanding of the parts alone. For example, understanding the properties of hydrogen and oxygen does not allow us to predict the properties of water. Life is an example of an emergent property. It is not inherent in DNA, RNA, proteins, carbohydrates, or lipids but is a consequence of their actions and interactions. A comprehensive understanding of such emergent properties requires systems-level perspectives and cannot be gleaned from simple reductionist approaches. Robustness. Biological systems maintain phenotypic stability in the face of diverse perturbations imposed by the environment, stochastic events, and genetic variation. Robustness often arises through positive and negative feedback loops and other forms of control that constrain a gene’s output. This feedback insulates the