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11 páginas, 4 tablas, 9 figuras, Fish wastage and market prices highly depend on accurate and reliable predictions of product shelf life and quality. The Quality Index Method (QIM) and EU grading criteria for whitefish (Council Regulation(EC) No 2406/96, 1996) are established sensory methods used in the market to monitor fish quality. Each assessment requires the consultation of a panel of trained experts. The indexes refer exclusively to the current state of the fish without any predictions about its evolution in the following days. This work proposes the development of a smart quality sensor which enables to measure quality and to predict its progress through time. The sensor combines information of biochemical and microbial spoilage indexes with dynamic models to predict quality in terms of the QIM and EU grading criteria. Besides, the sensor can account for the variability inside the batch if spoilage indexes are measured in more than one fish sample. The sensor is designed and tested to measure quality in fresh cod (Gadus morhua) under commercial ice storage conditions. Only two spoilage indexes, psychrotrophic counts and total volatile base-nitrogen content, were required to get accurate estimations of the two usual established sensory methods. The sensor is able to account for biological variability as shown with the validation and demonstration data sets. Moreover, new research and technologies are in course to make these measurements faster and non-destructive. This would allow having at hand a smart non-intrusive fish quality sensor, This work has been funded by the Spanish Ministry of Science and Innovation throughout project ISFORQUALITY (AGL2012- 39951-C02-01), RESISTANCE (DPI2014-54085-JIN) and FP7 SPEC- TRAFISH project (FP7/2007-2013: grant no 605399)

One of the most important properties of a mathematical model is the ability to make predictions: to predict that which has not yet been measured. Such predictions can sometimes be obtained from a simple simulation, but that requires that the parameters in the model are known from before. In biology, the parameters are usually both not known from before and not identifiable, i.e. the parameter values cannot be determined uniquely from available data. In such cases of unidentifiability, the space of acceptable parameters is large, often infinite in certain directions. For such large spaces, sampling-based approaches that try to characterize the entire space have difficulties. Recently, a new type of alternative approaches that circumvent this characterization problem has been proposed: where one only searches those directions in the space of acceptable parameters that are relevant for the uncertainty of a particular prediction. In this review chapter, these recently proposed methods are compared and contrasted, both regarding theoretical properties, and regarding user experience. The focus is on methods from the field of systems biology, but also methods from biostatistics, pharmacodynamics, and biochemometrics are discussed. The hope is that this review will increase the usefulness and understanding of already proposed methods, and thereby help foster a tradition where predictions only are deemed interesting if their uncertainties have been determined.