title: Mathematical modeling of complex biological systems: from parts lists to understanding systems behavior.
abstract: To understand complex biological systems such as cells, tissues, or even the human body, it is not sufficient to identify and characterize the individual molecules in the system. It also is necessary to obtain a thorough understanding of the interaction between molecules and pathways. This is even truer for understanding complex diseases such as cancer, Alzheimer's disease, or alcoholism. With recent technological advances enabling researchers to monitor complex cellular processes on the molecular level, the focus is shifting toward interpreting the data generated by these so-called "-omics" technologies. Mathematical models allow researchers to investigate how complex regulatory processes are connected and how disruptions of these processes may contribute to the development of disease. In addition, computational models help investigators to systematically analyze systems perturbations, develop hypotheses to guide the design of new experimental tests, and ultimately assess the suitability of specific molecules as novel therapeutic targets. Numerous mathematical methods have been developed to address different categories of biological processes, such as metabolic processes or signaling and regulatory pathways. Today, modeling approaches are essential for biologists, enabling them to analyze complex physiological processes, as well as for the pharmaceutical industry, as a means for supporting drug discovery and development programs.
authors.affiliation: Genedata AG, Basel, Switzerland.
authors.forename: Hans Peter
authors.initials: HP
authors.lastname: Fischer
mesh_terms: D000818:Animals; D032882:Comprehension; D006801:Humans; D008954:Models, Biological; D008962:Models, Theoretical; D015398:Signal Transduction / Q000502:physiology; D049490:Systems Biology / Q000379:methods* / Q000639:trends