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Metabolic P systems are a modeling framework for metabolic, regulatory and signaling processes. The key point of MP systems are flux regulation functions, which determine the evolution of a system from a given initial state. This paper presents important improvements to a technique, based on genetic algorithms and multiple linear regression, for inferring regulation functions that reproduce observed behaviors (time series datasets). An accurate analysis of three case studies, namely the mitotic oscillator in early amphibian embryos, the Lodka-Volterra predator-prey model and the chaotic logistic map show that this methodology can provide, from observed data, significant knowledge about the regulation mechanisms underlying biological processes. [ABSTRACT FROM AUTHOR]