Predicting how a system behaves under changing conditions is an essential component of science and engineering. The ability to make accurate predictions about the system indicates that it is well understood and provides the opportunity to simulate the response to conditions that would
be empirically difficult or impossible to test. In the life sciences, the term systems biology was introduced to articulate the notion that the molecular and phenotypic response of a cell or organism to perturbations is the result of interplay of a multitude of molecules. The ability
to predict the behavior of such complex molecular systems remains challenging and inevitably requires the involvement of different types of models and data that support them. In this article, we discuss a range of data-driven models that have proven particularly useful for predicting the behavior
of biological systems at different levels of complexity and the matching data generation methods that support them. We specifically focus on predictions based on protein or proteome data generated by mass spectrometry. We describe three case studies that represent frequently encountered situations
in systems biology.
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Document Type: Research Article
Department of Biology, Institute of Molecular Systems Biology, ETH Zurich, Otto-Stern-Weg 3, CH-8093 Zurich;, Email: [email protected]
Faculty of Science, University of Zurich, Zurich
Publication date: August 1, 2019
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International Journal for Chemistry and Official Membership Journal of the Swiss Chemical Society (SCS) and its Divisions
CHIMIA, a scientific journal for chemistry in the broadest sense, is published 10 times a year and covers the interests of a wide and diverse readership. Contributions from all fields of chemistry and related areas are considered for publication in the form of Review Articles and Notes. A characteristic feature of CHIMIA are the thematic issues, each devoted to an area of great current significance.
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