Mathematical Modeling of the Human Energy Metabolism Based on the Selfish Brain Theory

Conference paper
Part of the Advances in Experimental Medicine and Biology book series (AEMB, volume 736)


Deregulations in the human energy metabolism may cause diseases such as obesity and type 2 diabetes mellitus. The origins of these pathologies are fairly unknown. The key role of the brain is the regulation of the complex whole body energy metabolism. The Selfish Brain Theory identifies the priority of brain energy supply in the competition for available energy resources within the organism. Here, we review mathematical models of the human energy metabolism supporting central aspects of the Selfish Brain Theory. First, we present a dynamical system modeling the whole body energy metabolism. This model takes into account the two central control mechanisms of the brain, i.e., allocation and appetite. Moreover, we present mathematical models of regulatory subsystems. We examine a neuronal model which specifies potential elements of the brain to sense and regulate cerebral energy content. We investigate a model of the HPA system regulating the allocation of energy within the organism. Finally, we present a robust modeling approach of appetite regulation. All models account for a systemic understanding of the human energy metabolism and thus do shed light onto defects causing metabolic diseases.


KATP Channel Corticotropin Release Hormone Allocation Mechanism Appetite Regulation Huxley Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors thank Kerstin M. Oltmanns for her invaluable physiological expertise in the development of the presented models. We also thank the reviewer for his/her thorough review and highly appreciate the comments and suggestions, which significantly contributed to improving the quality of the publication. This work was supported by the Graduate School for Computing in Medicine and Life Sciences funded by the German Research Foundation [DFG GSC 235/1].


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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Department of MathematicsTexas State UniversitySan MarcosUSA
  2. 2.Graduate School for Computing in Medicine and Life Sciences, Institute of Mathematics and Image ComputingUniversity of LübeckLübeckGermany

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