A Mathematical Model of the Human Metabolic System and Metabolic Flexibility

Abstract

A note on versions:The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription. Abstract In healthy subjects some tissues in the human body display metabolic flexibility, by this we mean the ability for the tissue to switch its fuel source between predominantly carbohydrates in the post prandial state and predominantly fats in the fasted state. Many of the pathways involved with human metabolism are controlled by insulin, and insulin-resistant states such as obesity and type-2 diabetes are characterised by a loss or impairment of metabolic flexibility.In this paper we derive a system of 12 first-order coupled differential equations that describe the transport between and storage in different tissues of the human body. We find steady state solutions to these equations and use these results to nondimensionalise the model. We then solve the model numerically to simulate a healthy balanced meal and a high fat meal and we discuss and compare these results. Our numerical results show good agreement with experimental data where we have data available to us and the results show behaviour that agrees with intuition where we currently have no data with which to compare.

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“…Song and Thomas [13] developed a differential equation model describing the dynamics of stored energy in the form of fat mass, lean body mass, and ketone body mass during prolonged starvation. Pearson et al [14] derived a system of differential equations that describes the transport between and storage in different tissues of the human body and their results are in agreement with experimental data. Similarly, Song et al [15] analyzed a model of insulin delivery with an open-loop control and time delays in an insulin pump system, their results suggest that a smaller dose with higher delivery frequency has a better effect on continuous subcutaneous insulin injection administration.…”

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“…Pancreas is simplified as an insulin controller. In liver, many rate equations are derived from several previous works (4,8,10,11,25). I lump multiple associated enzyme reactions and simplify signal transduction pathways including glucose-controlled insulin secretion, phosphorylation of metabolic enzymes, and gene regulations by carbohydrate responsive element binding protein (ChREBP) and sterol-regulatory element binding protein 1c (SREBP-1c).…”

“…They used the compartment models that regarded particular metabolites as the representatives responsible for their associated functions such as glycolysis, gluconeogenesis, glycogenolysis, glycogenesis, and triglyceride (TG) synthesis/degradation. Compartment models simulated the glucagon/insulin-controlled glucose homeostasis by linking liver to other organ compartments (23)(24)(25), and suggested the mechanisms by which changes in a ratio of carbohydrate to lipid alter hepatic TG synthesis through insulin action (26) and generate different types of diabetes (27).…”

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