About this Research Topic
The understanding of complex systems is a key element to control the system's dynamics. To gain deeper insights into the underlying actions of complex systems, typically observations are analyzed what allows to derive corresponding models. These days more and more data of diverse types are available that mirror the systems dynamics, whereas system models are still hard to derive. This difficulty results from, inter alia, the system's complexity and from the diversity of gathered data. Consequently, developing and establishing techniques that permit to gain models well-adapted to observed data is a long-standing dream of every scientific field. This holds especially true in life sciences. Corresponding systems exhibit a dramatic range of diverse dynamics on different spatial and temporal scales, which are captured today by various observation technologies all reflecting different aspects of the system's dynamics. Conversely, typical corresponding models are quite too abstract and unrealistic to be able to explain most of the diverse data.
To this end, data assimilation and control theory provide important techniques to match diverse experimental data with an underlying model. The techniques yield an optimal combination of observations and model to achieve a certain goal. This goal may represent optimal fitting of model parameters, providing optimal forecast estimations or control of the system's dynamics to make the system perform a specific task. Besides the classic applications in meteorology and robot control, in recent years an increasing number of applications have been found in life sciences, such as neuroscience, biology, biochemistry or medicine.
The present Research Topic aims to bring together both recent theoretical work in data assimilation and control and applications in life sciences. This collection will reflect the state-of-the-art in current research in data assimilation and control in, originally, distinct research domains. Examples of theoretical topics (as an unconstrained open list) are Kalman filters, variational assimilation techniques, regression techniques, stochastic optimization techniques, adaptive, optimal and stochastic control. Applications may range from the parameter estimation in genetic regulatory networks over forecasts of cardio-vascular activity to control of human limb movements.
To this end, data assimilation and control theory provide important techniques to match diverse experimental data with an underlying model. The techniques yield an optimal combination of observations and model to achieve a certain goal. This goal may represent optimal fitting of model parameters, providing optimal forecast estimations or control of the system's dynamics to make the system perform a specific task. Besides the classic applications in meteorology and robot control, in recent years an increasing number of applications have been found in life sciences, such as neuroscience, biology, biochemistry or medicine.
The present Research Topic aims to bring together both recent theoretical work in data assimilation and control and applications in life sciences. This collection will reflect the state-of-the-art in current research in data assimilation and control in, originally, distinct research domains. Examples of theoretical topics (as an unconstrained open list) are Kalman filters, variational assimilation techniques, regression techniques, stochastic optimization techniques, adaptive, optimal and stochastic control. Applications may range from the parameter estimation in genetic regulatory networks over forecasts of cardio-vascular activity to control of human limb movements.
Keywords: Kalman filter, forecast, optimal parameter search, neuroscience, biology
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