Mathematical Modeling of Endocrine Systems

Mathematical modeling is the process of formulating a set of equations to represent the structure and behavior of a system simultaneously. The aim is to build a model that responds similarly to the underlying biological system (observed data) to a given perturbation. Thus, these models offer insight into .

Mathematical modeling is the process of formulating a set of equations to represent the structure and behavior of a system simultaneously. The aim is to build a model that responds similarly to the underlying biological system (observed data) to a given perturbation. Thus, these models offer insight into mechanisms and signal transduction pathways, providing the bedrock for hypothesis-generating research. Furthermore, the parameters of the model may conveniently serve as biomarkers of specific biological mechanisms. On the population level, mathematical models offer a way to assess population dynamics and also quantify the effects of a treatment strategy on the overall risk of disease.

This Research Topic will focus on mathematical modeling of metabolism, obesity, and diabetes. We welcome original research and reviews focusing on modeling nutrients such as glucose, lactate, FFA, protein, and endocrine signals related to their homeostases such as gut incretin hormones (GLP-1, GIP), insulin, glucagon, somatostatin, and others in humans and animals. The models may pertain to healthy or diseased states or include pharmacological and epidemiological models. Moreover, the application of existing models in particular physiological states or pathologies will be considered.

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