Systems biology: a way to make complex problems more understandable

Despite major advances in biomedical research, exceptional difficulties remain arising from the complexity of various diseases and their variability. Development of many disorders and their therapeutic responses are associated with disturbances in cell death pathways. Although for 440 years ourknowledgeaboutcelldeathwasrestrictedtoapoptosisand pathological necrosis, nowadays based on Recommendations of the Nomenclature Committee of Cell Death, there are 410 different cell death modalities recognized. 1 Cell death was classified according to its morphological appearance, enzymological criteria, functional aspects or immunological characteristics. 2 Intensive work in the field, for example, by the beginning of 2014 4400000 publications in PubMed are related to this area of research, led to understanding the biochemical features of various modes of cell death and some of molecular mechanisms of their activation/development/ execution. To get the insight into the complexity of the cell death networks, the upcoming field of systems biology has been successfully employed over the past decade. Systems biology is an interdisciplinary field of research that focuses on complex interactions within biological structures using a holistic perspective approach to biological and biomedical investigations. Systems biology combines theoretical and computational approaches with quantitative experimental data. On the theoretical side, a wide spectrum of mathematical formalisms is used. Their choice is based on the question to be answered by the modeling, available experimental data sets and the intricacy of the signaling network under consideration. Boolean models are effectively used to characterize large cell death signaling networks. In Boolean modeling, protein activities are presented by nodes that can be either off or on, and no knowledge is required for the quantitative characteristics of the individual reactions. In contrast, ordinary differential equations (ODEs) describe temporal dynamics of signaling networks and require the knowledgeof kinetic parameters of thesystem as well as a set of temporally solved experimental data. ODE-based modeling is one of the most common approachesused in the analysis of the cell death networks. ODEs might not be sufficient for modeling spatiotemporal processes within the cell, for example, translocations within different compartments that involve spatiotemporal gradients. 3 In this case modeling is conducted using partial differential equations (PDEs). Modeling cell-to-cell variations arising from single-cell measurements requires stochastic simulations. In addition, Petri nets, agent-basedmodels(ABMs)andBayesianmodelshavebeen employed for the analysis of the cell death networks. The combination of various mathematical tools allowed to quantitatively describe the major cell death processes and to identify biologically relevant systems’ properties that will be highlighted in this issue. Computational models require the exact knowledge about the numbers and interaction constants of the molecules in the pathway that allows making unique quantitative assessments upon molecular mechanisms of the complex signaling network regulation. These vigorous quantifications require state-of-the-art experimental methodology that includes quantitative biochemistry, cell biology and mass spectrometry techniques. The classical western blot and immunoprecipitation approaches were recently developed in the systems biology studies to generate time-resolved population data on the semiquantitative and quantitative levels. Single-cell analysis has enabled valuable insights into cell death using a number of special tools, including FRET-based and localization-based caspase activity probes. Finally, progress in mass spectrometry field is coming up with AQUA- and SILAC-based technologies, and development toward singlecell mass spectrometry analysis provides yet another major technological advance essential for the quantitative data generation. During the past decade, the powerful methodology of systems biology combining high-level mathematics with the state-of-the-art quantitative experimental work helped us to understand many aspects of cell’s decision to live or to die, in particular, death receptor- and mitochondria-mediated cell death pathways were elucidated and understood on a systems level with the unprecedented level of detail. 4‐6