Editorial: Mechanistic, machine learning and hybrid models of the “other” endocrine regulatory systems in health & disease

Abstract

Editorial on the Research TopicMechanistic, machine learning and hybrid models of the 'other' endocrine regulatory systems in health and diseaseWe made this our focus because mathematical modeling of endocrine systems is dominated by models of glucose regulation and dysregulation, the endocrine pancreas, liver, fat and their associated metabolic subsystems. We solicited works on modeling of thyroid, parathyroid, adrenal, gonadal, pituitary and hypothalamus, as well as cytokine regulation of immunomodulating cell-signaling molecules involved in autocrine, paracrine and endocrine signaling.We succeeded in attracting articles from four areas, seven articles utilizing physiologically-based mechanistic (MEC) and one machine learning (ML) modeling.MEC modeling is based primarily on mechanistic information (biochemical, biophysical interconnectivity and dynamical couplings), derived from first-principles, and numerical input-output data for quantification. Machine learning (ML) models are based primarily on input-output and features data, typically in much larger quantities, modeled in a different but complementary way, usually using high-level optimization and statistical modeling techniques. Mathematical optimization and simulation methods were major tools of several of the articles, attesting to the importance of engineering and computational methodologies for quantifying and predicting biological and clinical phenomena.Contributions of the 8 published papers are summarized below. We amplify their main theses, pointing out their novelty and pertinence of their methods and applications within the framework of our Topic.The paper by Hoermann et al. addresses endocrine regulation teleologically, using a mechanistic model of thyroid hormone (TH) regulation to demonstrate their thesis.