A new mathematical model incorporates custom details to simulate the metabolic effects of exercise. Featured in Computational Biology PLOS by Maria Concetta Palumbo of the National Research Council of Italy, Rome, and colleagues, the model can be adapted to different individual characteristics, such as age and weight, as well as different types and intensities of exercise .
Physical activity can help prevent or treat metabolic diseases, and a better understanding of the molecular effects of exercise could aid clinical efforts to combat the disease. The system-level effects of exercise are difficult to monitor in people, so scientists have developed mathematical models to simulate them. However, previously developed models do not take into account key details, such as type of exercise and personal characteristics.
To address this challenge, Palumbo and his colleagues extended an existing model to make it more personalized. The existing model used the known properties of different organs and tissues to simulate the effects of exercise on the metabolic dynamics of glucose, hormones and related substances in these tissues. However, the model only addressed one type of exercise (cycling) at a fixed intensity level for one type of person (a 70 kg man with no cycling training).
Without changing the biological basis of the old model, the researchers mathematically extended it to incorporate a better and more personalized definition of physical exercise. The resulting new model accounts for a subject’s gender, age, body weight, fitness level, exercise duration, and exercise intensity, as measured in the context of personal fitness level.
The researchers validated their model by showing that it accurately simulated the results of previous real-world studies in which blood samples were used to monitor metabolic effects in people with different individual characteristics who performed different forms of exercise.
“Modeling the influence of physical exercise on the control of glucose homeostasis is of paramount importance in understanding how physical activity prevents disease and improves health outcomes, and therefore in the development of online health monitoring devices for personalized medicine,” said Palumbo.
Next, the research team plans to further extend their model to incorporate key parameters related to an individual’s lifestyle, such as nutrition.
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