Abstract
In the last decade a new research field has emerged: Systems Biology. Based on experimental data and using mathematical and computational methods, systems biology attempts to describe biological behavior in a quantitative dynamic way. Biological data contains a lot of noise and there is only a limited amount available due to high experimental effort and cost. Thus, for parameter estimation from this kind of data, their stochasticity and the problem of non-identifiability of model parameters has to be taken into account. One way to do this is using a Bayesian framework, where one obtains distributions over possible parameter values, and these are then further analyzed. In this article we describe the potential impact of Bayesian parameter learning on systems biology, and discuss challenges arising from a Bayesian approach.
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We are grateful for funding to the German Ministry of Education and Research (BMBF), grant number 0313923 (FORSYS/Viroquant).
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Mazur, J., Kaderali, L. (2013). The Importance and Challenges of Bayesian Parameter Learning in Systems Biology. In: Bock, H., Carraro, T., Jäger, W., Körkel, S., Rannacher, R., Schlöder, J. (eds) Model Based Parameter Estimation. Contributions in Mathematical and Computational Sciences, vol 4. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30367-8_6
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DOI: https://doi.org/10.1007/978-3-642-30367-8_6
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