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Computational models and simulations can be powerful tools for gaining an insight into the complex world of the biological mechanisms. Among them the growth processes are perhaps the most challenging and elusive to describe, for the different time scales and species specific features involved. Using a recently proposed technique for the analysis of experimental datasets (the Phenomenological Universalities Approach: PUN), we have succeeded to reproduce, to an excellent level of reliability, many results from experimental ‘in vitro’ and ‘in vivo’ tumor growth studies (e.g. the ‘multipassaged’ tumors described in the paper). Also the description of human growth, with its important implications for monitoring children development and diagnosing metabolic diseases, can be approached using the PUN method.
best fitting, biological models, complexity, human growth, metabolic diseases, nonlinearity
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