Classical biochemists, such as the German biochemist Leonor Michaelis and the Canadian physician Maud Menten who worked together on enzyme kinetics, concentrated their research on the dynamics of interactions. However, more recently the greater part of research in molecular biology has focused on static interactions, and less emphasis has been given to the effect of a molecule’s dose or the time required to trigger all interactions in a cascade.
A number of limitations are introduced when these dynamics in biological interactions are not accounted for. A false time window during an experiment, for example, can lead to missed activation events of important signalling components; equilibrium is not accounted for in signal transduction cascades that are assumed to be unidirectional; and experimental studies miss the full complexity of a regulatory network while focusing only on sub-modules of the network, such as individual feedback or activation loops.
“Biologists are used to interpolating their data without knowledge of the dynamic behaviours of the system between the points measured. Furthermore, the complexity of regulatory networks does not allow intuitive estimations on the dynamics of the entire network.”
Fred Schaper, Otto-von-Guericke University Magdeburg
Fred Schaper, from the Otto-von-Guericke University Magdeburg, Germany, combines laboratory experiments and mathematical modelling in a systems biology approach to investigate how signal transduction is induced by interleukin-6 type cytokines. As someone who believes in the need, challenge and benefit of taking a systemic and dynamic view on signalling pathways and networks, Schaper was well qualified to be Guest Editor, alongside Stephen Feller from the University of Oxford, UK, for the Systems Biology and Medicine review series published in Cell Communication and Signaling this summer.
Emphasising that good models are still models and not a copy of the real system, Schaper highlights a number of benefits to taking a systems biology approach. Among these is saving time and money through modelling a system’s behaviour to promote an optimal experimental design. Models can be used to reject or refine a hypothesis, and to help identify new connecting components of a regulatory design, but an experiment will always be needed to confirm a hypothesis.
The Systems Biology and Medicine review series provides a comprehensive overview of current models and known networks in signal transduction, including reviews on apoptosis, dopamine metabolism and probabilistic Boolean networks.
Highlights include Steffen Klamt and Regina Samaga from the Max Planck Institute for Dynamics of Complex Technical Systems, Germany discussing the usability of logical modelling, such as interaction graphs, logical networks and logic-based ordinary differential equations. This review will enjoy great popularity among scientists involved in systems theory, but also addresses biologists open to a structured systems view on biological networks. Thomas Sauter from the University of Luxembourg and colleagues extend this view for the more advanced reader by presenting biomedical applications of probabilistic Boolean networks.
The biological systems used as examples in these two reviews, EGF signalling and apoptosis, are discussed in more detail in the accompanying articles of Boris Kholodenko and colleagues and Inna Lavrick and Kolja Schleich, respectively. These articles clearly demonstrate the benefit of a systems view on complex biological processes.
With this review series on Systems Biology and Medicine the benefits of systems biology in biological research is clarified, with the aim of encouraging the development of a systems view on complex biological regulatory networks, in light of the reasoning that pure intuitive understanding is not sufficient, and may even be misleading.
The complete list of series articles: