Modeling Metabolism

Full course description

The ever-increasing availability of large-scale omics and other types of data for human subjects in health and disease calls for appropriate computational means of extracting insights out of the massive amounts of data. Individualized computational models furthermore hold out the prospect of contributing to the expansion of personalized medicine.

In this course, students will get acquainted with a metabolic modeling framework known as constraint-based modeling. This framework allows for the efficient study of genome-scale metabolic networks, i.e. networks encompassing all known metabolic reactions in a given cell type. By studying possible steady state reaction flux distributions of the network under different conditions, this framework studies metabolic functional states beyond the network’s topology only; while at the same time bypassing the need for detailed kinetic information, which is not available for entire human cellular networks. In this way, constraint-based modeling also allows for the incorporation of omics data, thus allowing to analyze big data in the context of established metabolic networks.

The course will familiarize students with constraint-based metabolic modeling and its applications, with a particular focus on biomedical applications. To this end, the course introduces the fundamentals of constraint-based modeling; discusses the content of genome-scale metabolic models; presents different constraint-based modeling methods to analyze these metabolic models; and shows how omics data can be used to obtain metabolic models that describe particular cell types and disease conditions. Further topics include other ways of incorporating omics data, as well as the incorporation of constraint-based models into multi-scale models.