Abstract
We present a description of a living organism that integrates multiple relationship networks, where each network represents a different facet of the target phenomena. The lifestyle illness and diabetes related phenomena are described using the proposed model. The relationship network model denotes a representation model where the focused phenomena or element is represented as a node, and nodes are connected if the represented phenomena or elements are somehow related. Each relationship network is a hypernetwork model, whose representation power is stronger than conventional models. Different viewpoints can be selected from a single model, thus the user can grasp the represented phenomena according to his needs.
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Christakis, N.A., Fowler, J.H.: The Spread of Obesity in a Large Social Network over 32 Years. N. Engl. J. Med. 357, 370–379 (2007)
Cone, R.D.: Anatomy and regulation of the central melanocortin system. Nat. Neurosci. 8, 571–578 (2005)
Jordan, S.D., Konner, A.C., Bruning, J.C.: Sensing the fuels: glucose and lipid signaling in the CNS controlling energy homeostasis. Cell. Mol. Life Sci. 67, 3255–3273 (2010)
Porte Jr., D., Baskin, D.G., Schwartz, M.W.: Insulin Signaling in the Central Nervous System: A Critical Role in Metabolic Homeostasis and Disease From C. elegans to Humans. Diabetes 54, 1264–1276 (2005)
Sainsbury, A., Cooney, G.J., Herzog, H.: Hypothalamic regulation of energy homeostasis. Best Pract. Res. Clin. Endocrinol. Metab. 16, 623–637 (2002)
Badmin, M.K., Flier, J.S.: The Gut and Energy Balance: Visceral Allies in the Obesity Wars. Science 307, 1909–1914 (2005)
Demuro, G., Obici, S.: Central Nervous System and Control of Endogenous Glucose Production. Curr. Diab. Rep. 6, 188–193 (2006)
Lam, T.K., Schwartz, G.J., Rossetti, L.: Hypothalamic sensing of fatty acids. Nat. Neurosci. 8, 579–584 (2005)
Karr, J.R., et al.: A Whole-Cell Computational Model Predicts Phenotype from Genotype. Cell 150, 389–401 (2012)
Okano, Nakayama, Ito, Maeshiro: Estimation of gene regulatory network related to worm embryo. In: Japan Molecular Biology Society 33rd Symposium, vol. 4P, p. 1255 (2011)
Maeshiro, T., Hemmi, H., Shimohara, K.: Ultra-fast Genome wide Simulation of Biological Signal Transduction Networks - Starpack. In: Frontiers of Computational Science, pp. 177–180. Springer (2007)
Schierenberg, E.: Embryological variation during nematode development. WormBook (2006)
Maeshiro, T., Maeshiro, M., Shimohara, K., Nakayama, S.-i.: Hypernetwork model to represent similarity details applied to musical instrument performance. In: Jacko, J.A. (ed.) Human-Computer Interaction, Part I, HCII 2009. LNCS, vol. 5610, pp. 866–873. Springer, Heidelberg (2009)
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Maeshiro, T. (2013). A Model of Living Organisms to Integrate Multiple Relationship Network Descriptions. In: Yamamoto, S. (eds) Human Interface and the Management of Information. Information and Interaction Design. HIMI 2013. Lecture Notes in Computer Science, vol 8016. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39209-2_54
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DOI: https://doi.org/10.1007/978-3-642-39209-2_54
Publisher Name: Springer, Berlin, Heidelberg
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