Abstract
Stochastic models of biological networks properly take the randomness of molecular dynamics in living cells into account. Numerical solution approaches inspired by computational methods from applied probability can efficiently yield accurate results and have significant advantages compared to stochastic simulation. Examples for the success of non-simulative numerical analysis techniques in systems biology confirm the enormous potential. © 2008 Springer-Verlag Berlin Heidelberg.
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Sandmann, W., & Wolf, V. (2008). Computational probability for systems biology. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 5054 LNBI, pp. 33–47). https://doi.org/10.1007/978-3-540-68413-8_3
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