Deeper understanding of the basic principles of biomineralization is a major challenge for present and future research. The high complexity of in‐vivo conditions calls for simplified model systems that still involve dynamic processes like reorganization, formation, self‐organization, and development of patterns, respectively. The present contribution deals with hierarchically ordered spherical aggregates of fluorapatite–gelatin composites with fractal architecture that are grown in gelatin matrices. The model system was chosen to mimic formation conditions on a lower level of complexity compared with the natural formation of calcified tissue (bone, teeth). In order to gain insight into structure formation and the motif for this special kind of morphogenesis we investigated the interaction of the organic and inorganic components of aggregates in different growth stages by detailed chemical analyses, thermoanalytical measurements, X‐ray diffraction on solitary particles, and scanning electron microscopy (SEM) as well as transmission electron microscopy (TEM) investigations. The close relation of the results and observations to calcified tissue may stimulate advanced interests in the fields of medical and materials development.

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We study the problem of simultaneous estimation of parameters and unobserved states from noisy data of nonlinear time-continuous systems, including the case of additive stochastic forcing. We propose a solution by adapting the recently developed statistical method of unscented Kalman filtering to this problem. Due to its recursive and derivative-free structure, this method minimizes the cost function in a computationally efficient and robust way. It is found that parameters as well as unobserved components can be estimated with high accuracy, including confidence bands, from heavily noise-corrupted data.

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