ABSTRACT
Biomarkers are mostly correlated to a complex biomedical process such as disease propagation or drug action, which is affected by a multitude of biological mechanisms. Systems biology, focusing on the integrated approach to describe the behavior of complex biological systems, in contrast to the reductionist approach focusing on the system’s entities, may be the way toward more specific biomarkers. However, utilizing systems biology in order to develop biomarkers that are both sensitive and specific requires the identification and validation of quantitative models for the system behavior representing the complex interaction of its relevant entities. However, the identification of the relevant entities and their interactions suffers from the complexity of the multi-scale architecture of biological systems and requires the integration of processes on the molecular, cellular and multicellular up to the macroscopic scale such that quantitative models of full-scale biology are far beyond reality today. Still, the increasing availability of biomedical data repositories in combination with machine learning induces increasing interest in academia and industry in methods to tackle the biological complexity. Because a generic systems biology approach is not yet available, we will discuss possible routes toward systematic biomarker identification using systems biology for specific examples.
