Cellular signal transduction system is a complex reaction network consisting of many elements, which allows cells to respond appropriately to external stimuli. In order to understand and predict such a complex system, it should be reconstructed as a computational simulation. However, since mathematical models of living systems inherently contain uncertainty, it is often difficult to predict cell behaviors in simulations. In this study, we aim to overcome this difficulty by using a data assimilation method that combines numerical simulation with observed data. The chemotaxis signaling pathway of Dictyostelium cells exhibits a variety of complex pattern formation such as traveling wave on the cell membrane of single cells. In this work, we apply the data assimilation method to this phenomenon. By combining a mathematical model that describes various pattern formation with fluorescence microscopy images, we characterize pattern formation behaviors in the signal transduction system and construct a simulation that can predict the behavior of cells.
invited by Carsten Beta
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