Role of Brain Insulin Receptor in Control of Body Weight and Reproduction

The use of computational modeling and simulation has increased in many biological fields, but despite their potential these techniques are only marginally applied in nutritional sciences. Nevertheless, recent applications of modeling have been instrumental in answering important nutritional questions from the cellular up to the physiological levels. Capturing the complexity of today's important nutritional research questions poses a challenge for modeling to become truly integrative in the consideration and interpretation of experimental data at widely differing scales of space and time. In this review, we discuss a selection of available modeling approaches and applications relevant for nutrition. We then put these models into perspective by categorizing them according to their space and time domain. Through this categorization process, we identified a dearth of models that consider processes occurring between the microscopic and macroscopic scale. We propose a "middle-out" strategy to develop the required full-scale, multilevel computational models. Exhaustive and accurate phenotyping, the use of the virtual patient concept, and the development of biomarkers from "-omics" signatures are identified as key elements of a successful systems biology modeling approach in nutrition research - one that integrates physiological mechanisms and data at multiple space and time scales. © 2009 de Graaf et al.

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Nutritional systems biology modeling : From molecular mechanisms to physiology

An expanded view of energy homeostasis: neural integration of metabolic, cognitive, and emotional drives to eat.

Diabetes is a chronic and complex multifactorial disease caused by persistent hyperglycemia and for which underlying pathogenesis is still not completely understood. The mathematical modeling of glucose homeostasis, diabetic condition, and its associated complications is rapidly growing and provides new insights into the underlying mechanisms involved. Here, we discuss contributions to the diabetes modeling field over the past five decades, highlighting the areas where more focused research is required.

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The impact of mathematical modeling on the understanding of diabetes and related complications

Mathematical modeling of human energy regulation and body weight change has recently reached the level of sophistication required for accurate predictions. Mathematical models are beginning to provide a quantitative framework for integrating experimental data in humans and thereby help us better understand the dynamic imbalances of energy and macronutrients that give rise to changes in body weight and composition. This review provides an overview of the various approaches that have been used to model body weight dynamics and energy regulation in humans, highlights several insights that these models have provided, and suggests how mathematical models can serve as a guide for future experimental research.

http://demystifyingmedicine.od.nih.gov/DM14/2014-02-04/AnnuRevNutr-y2012v32p35.pdf

Modeling Metabolic Adaptations and Energy Regulation in Humans

Diabetes and obesity present a mounting global challenge. Clinicians are increasingly turning to mechanism-based mathematical models for a quantitative definition of physiological defects such as insulin resistance, glucose intolerance and elevated obesity set points, and for predictions of the likely outcomes of therapeutic interventions. However, a very large range of such models is available, making a judicious choice difficult. To better inform this choice, here we present the most important models published to date in a uniform format, discussing similarities and differences in terms of the decisions faced by modellers. We review models for glucostasis, based on the glucose–insulin feedback control loop, and consider extensions to long-term energy balance, dislipidaemia and obesity.

https://royalsocietypublishing.org/doi/pdf/10.1098/rsif.2008.0216

Mathematical models of energy homeostasis

Diabetes is a common endocrine disorder, primarily characterised by elevated plasma glucose levels. The disease affects all age groups worldwide. Most patients suffer from Type 2 diabetes, which is mainly due to insulin resistance. It is thought that changes in insulin signalling pathways underlie the development of insulin resistance. This article aims to review recent studies that have elucidated the role of individual proteins in these insulin signalling pathways. These studies have been undertaken using two strategies, one employing mice carrying a global null mutation of particular gene-encoding proteins by the homologous recombination method and another strategy using mice with tissue-specific insulin receptor and/or GLUT4 knockout by the Cre-loxP system. The various phenotypes of these knockout mice, and the light they shed on the etiology of insulin resistance, are discussed. By advancing our understanding of the complex molecular mechanisms underlying insulin resistance, these knock-out models may help us to develop more effective treatments for Type 2 diabetes.

Ratchada Pattaranit

Papers

Mathematical models of energy homeostasis

The development of insulin resistance in Type 2 diabetes : insights from knockout studies