
Physiology constitutes a broad discipline that covers the study of the different hierarchical levels of living organisms, from the cellular one to higher levels, such as tissues or organs. The articles of this monography provide an excellent example of the telescopic capacity of this discipline. A common nexus among all these levels is that physiological systems are epistemologically complex. This implies that their study requires a necessary reduction of the complexity in order to elaborate a formal and manageable description of the system, giving rise to a model. Therefore, we need to develop mathematical models that represent the original system instead of studying it. Nevertheless, even this representation can present an unmanageable degree of complexity. Typically, the description of the physiological processes relies on non-linear relationships among a high number of variables that involve many parameters of unknown value.
modelling, computational methods, machine learning, mathematical physiology, omics integration, Physiology, QP1-981, Métodos, Fisiología, biostatistical methods, Aprendizaje automático
modelling, computational methods, machine learning, mathematical physiology, omics integration, Physiology, QP1-981, Métodos, Fisiología, biostatistical methods, Aprendizaje automático
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