About this Research Topic
Given the success of the previous collection, Mechanistic, Machine Learning and Hybrid Models of the ‘Other’ Endocrine Regulatory Systems in Health and Disease, we are pleased to announce the launch of Volume II.
A search of the literature on ‘math modelling of endocrine systems’ shows this topic to be dominated by models of glucose regulation and dysregulation, the endocrine pancreas, liver, fat, and their associated metabolic subsystems. Models and modelling of the other endocrine regulatory systems are our focus here, i.e. thyroid, parathyroid, adrenal, gonadal, pituitary, and hypothalamus neuroendocrine systems. Cytokine regulation models and modelling methods are also included. Cytokines (chemokines, interferons, interleukins, lymphokines, and tumor necrosis factors) are the small immunomodulating cell-signaling molecules involved in autocrine, paracrine, and endocrine signaling.
We especially encourage contributions that address the regulatory interactions among these systems and their hormonal signals.
Modelling methodologies of interest include three types: (1) physiologically-based mechanistic (MEC) (grey-box) modelling; machine learning models (ML) (black-box); and hybrid (HYB) modelling – where both MEC and ML are included in the modelling methodology. (2) MEC modelling is based primarily on structural (biological interconnectivity and dynamical couplings) information, derived from first-principles, as well as numerical input-output data – for structuring and quantification. ML models are based primarily on and utilize input-output data – data in typically much larger quantities, modelled in a different but complementary way, usually using high level statistical modelling techniques. (3) HYB models can be grouped into two broad categories: (i) models that use machine learning to calibrate (quantify) parameters of a MEC model; and (ii) modelling that incorporates the biological insights of a MEC model within and in conjunction with the training of an ML model. The integrated hybrid methodology for this second category is currently a relatively underdeveloped topic and contributions to this likely more fruitful methodology are strongly encouraged.
Finally, we are beginning to see COVID papers in the thyroid and adrenal (steroid) literature as well. Thus, integration of multidisciplinary approaches to modelling these pathologies are included in this solicitation – further justifying inclusion of cytokine regulation models, with a focus on how they interact and regulate endocrine systems in health and disease.
A search of the literature on ‘math modelling of endocrine systems’ shows this topic to be dominated by models of glucose regulation and dysregulation, the endocrine pancreas, liver, fat, and their associated metabolic subsystems. Models and modelling of the other endocrine regulatory systems are our focus here, i.e. thyroid, parathyroid, adrenal, gonadal, pituitary, and hypothalamus neuroendocrine systems. Cytokine regulation models and modelling methods are also included. Cytokines (chemokines, interferons, interleukins, lymphokines, and tumor necrosis factors) are the small immunomodulating cell-signaling molecules involved in autocrine, paracrine, and endocrine signaling.
We especially encourage contributions that address the regulatory interactions among these systems and their hormonal signals.
Modelling methodologies of interest include three types: (1) physiologically-based mechanistic (MEC) (grey-box) modelling; machine learning models (ML) (black-box); and hybrid (HYB) modelling – where both MEC and ML are included in the modelling methodology. (2) MEC modelling is based primarily on structural (biological interconnectivity and dynamical couplings) information, derived from first-principles, as well as numerical input-output data – for structuring and quantification. ML models are based primarily on and utilize input-output data – data in typically much larger quantities, modelled in a different but complementary way, usually using high level statistical modelling techniques. (3) HYB models can be grouped into two broad categories: (i) models that use machine learning to calibrate (quantify) parameters of a MEC model; and (ii) modelling that incorporates the biological insights of a MEC model within and in conjunction with the training of an ML model. The integrated hybrid methodology for this second category is currently a relatively underdeveloped topic and contributions to this likely more fruitful methodology are strongly encouraged.
Finally, we are beginning to see COVID papers in the thyroid and adrenal (steroid) literature as well. Thus, integration of multidisciplinary approaches to modelling these pathologies are included in this solicitation – further justifying inclusion of cytokine regulation models, with a focus on how they interact and regulate endocrine systems in health and disease.
Keywords: mathematical modelling, physiologically-based mechanistic modelling, machine learning models, hormone signaling, cytokine-endocrine signaling
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