Systems biology is a multidisciplinary and not clearly defined field of research. From an abstract point of view it is about understanding and investigating biology from a systems perspective. That is to say, the focus is not on isolated parts or processes, but on their interaction by which a certain behavior is generated or a certain task is fulfilled.
In modern science and engineering, systems are often studied using mathematical models for example in order to aggregate, integrate, formalize and challenge distributed existing knowledge, systematically analyse system behavior, develop and test hypotheses, and plan next experimental steps. Of course, the idea of using mathematical models also to investigate biology is not new. However, the more widespread use has strongly developed in the last decade as well as the increasing recognition and appreciation that mathematical models are needed besides, e.g. experimental and graphical models that have traditionally been used in biology, in order to cope with the ever increasing amount of data and information that are generated in life sciences. Considering the molecular complexity that forms the basis of life, it is clear that system boundaries and level of detail of any kind of model are limited. While experiments are and will remain an essential part of biological (and systems biological) research, in line with many "sytems biologists", we consider mathematical models as the core discipline of systems biology distinguishing it from other research approaches. Consequently, in this manual we mean mathematical models, if we speak simply of models and indicate, if we mean another form of model.
The content of this manual naturally is selected and biased. While systems biology includes biological diversity, we will focus on organisms and topics of broader relevance in pharmaceutical research and development, i.e. systems pharmacology, even though large parts of this software platform can also be used to address questions way beyond. But if we consider biochemical reactions or networks, for example, we often do that in a whole-body context to address the interaction of an active substance with an organism - this is where the software platform has unique capabilities and strengths.
While the very early phases of drug development do not involve work on whole organisms (animals or humans), the late preclinical phase includes animal experiments, mainly in mammals such as mice, rats, dogs, or monkeys before entering the clinical phase where the focus is on trials on humans as outlined below. Different modeling approaches have been developed to support investigations on different scales [39]. As outlined above, we will focus on systems pharmacology, which can be viewed as a mechanistic approach to study pharmacodynamics and pharmacokinetics as illustrated below.
