Human cytome project, cytomics, and systems biology: the incentive for new horizons in cytometry.

SYSTEMS BIOLOGY Systems biology aims at the quantification of molecular elements and their interrelations in biological systems in response to perturbation to integrate the variety of information into mathematical network models and to correctly predict biological system response to stimuli (1–4). Although promising for single-cell systems such as bacteria, yeast, or cell cultures, difficulties arise for complex mammalian organisms with numerous genes, diversity of genotypes among individuals, and variable exposure histories to environmental influences. Additional complexity derives from the diversity of organs and specialized tissues, each consisting of multiple cell types of significant internal heterogeneity, e.g, of cell cycle, functional status, size, and molecule content. Cell cultures or diseased and genetically modified animals may in certain circumstances serve as model systems for human disease. However, significant concerns as to the ultimate validity of the conclusions from such model systems for the human in vivo situation, especially in disease, or in the search for new drug targets have been raised (5). Cell or animal model systems may ultimately not permit one to generate valid mathematical models of the human organism by systems biology, permitting, e.g., the accurate prediction of a particular patient’s reaction to stimuli such as microbial infection, interaction with toxic substances, or adverse drug reactions from mathematical network models. It is also questionable whether a preexisting mathematical model for an entire biological system such as the human organism represents a prerequisite for the understanding and mathematical modeling of specific molecular disease pathways in complex diseases such as cancer, allergies, rheumatoid arthritis, asthma, diabetes, or infections. MOLECULAR CELL PHENOTYPING BY CYTOMICS The analysis of molecular cell phenotypes by cytomics (6,7) represents a simplifying strategy for the dissection of molecular disease networks. Differential molecular screens of single cells from diseased versus reference persons in disease-associated cell systems provide discriminatory data patterns or biomarker patterns for predictive medicine by cytomics and for the detection of new drug targets (8,9) by using hypothesis-driven selection of screening parameters. Reference persons can be healthy individuals or stationary status or survivor patients. The differentially obtained discriminatory data patterns from cells represent important entry points for molecular reverse engineering by biomedical cell systems biology (7). The reasons for this are that diseases are caused by molecular perturbations in cells and cells represent the elementary construction and function units of cellular systems, organs, and organisms. Reverse engineering of biological complexity (10) using discriminatory data patterns from patients (8) has the potential to directly elaborate mathematically modeled molecular disease pathways without exact knowledge about the normal condition or the disease-causing mechanisms. Therefore, analysis of molecular differentials in disease circumvents a priori mathematical modeling of systematically perturbed biological systems. An important advantage for the molecular cell phenotype screening concept consists of the plethora of avail-