Abstract
Stochastic models of biological networks are well-established in computational systems biology. However, models are abstractions and parameters may be inaccurate or perturbed. An important topic is thus the sensitivity of analysis results to parameter perturbations. This investigates how seriously potential parameter perturbations affect the analysis results and to which parameters the results are most sensitive. In this paper, a stochastic simulation algorithm is presented that yields results for multiple perturbed models from a single simulation experiment and that is thus able to perform comparisons of results for various parameter sets without explicitly simulating each of these separately. The algorithm essentially makes use of likelihood ratios in a similar fashion as in the Importance Sampling technique for variance reduction. With a suitable adaptation to the context of perturbed model parameters it yields substantial runtime savings compared to multiple separate simulations of the perturbed models without any loss in statistical accuracy.
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Sandmann, W. (2007). Simultaneous Stochastic Simulation of Multiple Perturbations in Biological Network Models. In: Calder, M., Gilmore, S. (eds) Computational Methods in Systems Biology. CMSB 2007. Lecture Notes in Computer Science(), vol 4695. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75140-3_2
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DOI: https://doi.org/10.1007/978-3-540-75140-3_2
Publisher Name: Springer, Berlin, Heidelberg
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