Abstract
Mathematical modeling of biological systems is a powerful technique by which data from empirical measurements can be both corroborated and used to gain a fuller understanding of the system under study. Most empirical studies involve the measurement of individual parts of a complex system while mathematical modeling allows these parts to be brought together as a unified whole. The predictions of a model and the degree to which these predictions are consistent with measurable parameters can be valuable in assessing both the data and the assumptions used in the model construction. Also, the modeling process challenges the modeller to define precisely and then assess critically all of the assumptions and empirical data which are used in developing hypotheses and designing experiments. In this way models are useful in examining the feasibility and the implications of hypotheses which have been created to account for empirical data. Finally, the predictions of a model may elucidate new aspects of a problem and lead to the design of experiments which improve our understanding of complex biological systems.
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© 1989 Kluwer Academic Publishers, Dordrecht
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Gaito, S.T., Hunt, S., Layzell, D.B. (1989). Principles and Approaches in Modeling Steady-State Gas Diffusion in Legume Nodules. In: Torrey, J.G., Winship, L.J. (eds) Applications of Continuous and Steady-State Methods to Root Biology. Developments in Plant and Soil Sciences, vol 34. Springer, Dordrecht. https://doi.org/10.1007/978-94-009-2237-2_6
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DOI: https://doi.org/10.1007/978-94-009-2237-2_6
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