Digital twins; using personalisable in silico models to characterise glycaemic control
In this project we explore the potential to create personalised computational models of individuals’ glycaemic control.
Blood glucose levels in the body are maintained within a narrow range through a combination of complex and interconnected mechanisms including the secretion of insulin and the disposal of glucose to peripheral tissues. Deteriorations in these mechanisms may lead to impaired glucose homeostasis culminating in the development of prediabetes and eventually type 2 diabetes mellitus.
Glucose homeostasis when studied under perturbation (e.g. after a meal) exhibits highly dynamic behavior allowing insight into the condition of certain mechanisms. Computational models of the insulin-regulated glucose homeostasis allow the quantification of the dynamics arising in the perturbed state, presenting an opportunity for the early detection of deteriorations in the system.
Details
Project activities/type of research
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Metabolic modelling using dynamic models (in silico)
Programme
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Systems Biology
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Maastricht Science Programme
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Data Science and Artificial Intelligence
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Data Science for Decision Making
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Bachelor and Master level
Prior knowledge/skills required
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Basic molecular, human biology
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Knowledge of/interest in metabolic modeling
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Programming experience
Contact person/supervisor
Name: Balázs Erdõs, PhD candidate
Department: Maastricht Centre for Systems Biology