Systems Biology – Old Concepts, New Science, New Challenges

Life is a relationship among molecules and not a property of any molecule. On the timescale of history of science, Systems Biology is so new that a precise consensus defi nition for the term is yet to be developed. However, the common-sense concept behind systems biology that a living organism is more than a mechanical sum of its parts has been noted, discussed, and analyzed for centuries by philosophers and scientists alike, with Kant, Goethe, Schrödinger, and Delbruck being just a few examples. It is now well understood that life at any level – from cell functioning to human behavior – is defi ned by the dynam-ical interactions between its components, not by the properties of these components in isolation. However, only recently fundamental advances in molecular biology, computing, and mathematical modeling, have allowed us to attempt a coordinated effort to studying and understanding this phenomenon. Systems biology is the cross-disciplinary methodology behind the effort to understand the dynamics of life, aiming at determining how the individual components of a living system interact in space and time to form functional networks. The challenges are huge: in biological systems at all levels of organization from sub-cellular to the cell, tissue, organ, and human behavior , control and functional mechanisms are emergent properties of networks, not of their separate components. These biological networks exhibit self-organization with relatively independently functioning subsystems that have network structures of their own, often comprised of modular units found in multiple locations. A number of questions of equal importance arise in this context. To name a few (refl ecting the author's personal bias and interest): What network interactions lead to a specifi c system function? Knowing a system's function , can a network of subcomponents be identifi ed to exhibit the same behavior? In what way are self-organizing biological networks between organizational levels and between organisms similar? In what way are they different? The tools of modern biology and classical neuroscience are insuffi cient in seeking answers. Their insights into available data from molecular biology, genomic, pro-teomic, metabolomic, neuroendocrine, and behavioral research need to be merged with mathematical models, computational tools, and engineering systems analysis to ensure better understanding of evolution, physiology and human behavior. In this effort, tight links between network modeling and experimentation and their consistent iterative interaction are critical to understanding the network structure. In other words, the network structure and interactions that are inherent …