Cytomics in the realm of systems biology

CONVERGENCE OF SYSTEMS BIOLOGY AND CYTOMICS The current focus of systems biology is on the reverse engineering of networks to model gene regulatory or protein–protein interactions and the extraction of basic principles of biological organization. The ability of in silico representations to predict of how a system being in a particular state may react and adjust to perturbations has made systems biology an attractive component for basic research, drug development, and predictive medicine. However, computational systems biology is less experienced in implementing spatiotemporal properties of cells and multicellular architectures, and attempts to integrate and interconnect various levels of biological organization, such as genes, proteins, cells, and tissues, are a rare occurrence. As opinions about the nature of Cytomics as a discipline arise, and activities and projects evolve, more specific definitions may become available. At this point it should be noted that even systems biology, which already has a track record of successful research activities, still experiences debates of what it is or should be. Cytomics is currently centered on high-throughput, multiparametric imaging in conjunction with machine vision to quantify cellular morphologies and properties like protein activities, with the underlying notion of a hypothesis-free approach. It aims to provide comprehensive, accurate, unbiased and systematic data, features that have been defined as the cornerstones for measurement technologies in systems biology (1). Applications for a systematic profiling of different cells, such as all cell types in the human body, have been suggested (2). Systematic screening projects will enable us to populate a rather sparse data area above the level of the proteome. Cytomics appreciates single cell properties, which is of great value since averaging can seriously limit systems biology approaches, such as network analyses. The example recently demonstrated applied reverse engineering in T-cells, using multiparameter flow cytometry and Baysian statistics (3). Targeting multiple protein activators and inhibitors allowed simultaneous monitoring of the phosphorylation status of many protein species. As each pathway in each cell is in a particular status of activation, signaling networks can be more accurately revealed by taking multiparameter snapshots of many cells when compared to procedures that rely on averaged information out of cell lysates.