Abstract
Variability is present at all levels of biological systems. At the molecular level Brownian motion of the molecules leads to randomness of the biochemical reactions inside the cells. On a higher level, the molecular noise and other stochastic effects can lead to fundamentally different behavior of the cells in a population. As a consequence, average dynamics of a cell population are often not adequate to understand or control the dynamics of the population as a whole. We discuss how stochastic models of biochemical reaction networks which enable one to study heterogeneous cell populations can be identified from data and how experiments which make this identification as easy as possible can be designed.
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Ruess, J., Lygeros, J. (2013). On the Use of the Moment Equations for Parameter Inference, Control and Experimental Design in Stochastic Biochemical Reaction Networks. In: Gupta, A., Henzinger, T.A. (eds) Computational Methods in Systems Biology. CMSB 2013. Lecture Notes in Computer Science(), vol 8130. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40708-6_1
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DOI: https://doi.org/10.1007/978-3-642-40708-6_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40707-9
Online ISBN: 978-3-642-40708-6
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