By Raya Khanin, Desmond J. Higham (auth.), Muffy Calder, Stephen Gilmore (eds.)

ISBN-10: 3540751394

ISBN-13: 9783540751397

This booklet constitutes the refereed lawsuits of the overseas convention on Computational tools in structures Biology, CMSB 2007, held in Edinburgh, Scotland, September 20-21, 2007.

The sixteen revised complete papers offered have been rigorously reviewed and chosen. The papers current numerous recommendations from machine technology, reminiscent of language layout, concurrency conception, software program engineering, and formal tools, for biologists, physicists, and mathematicians drawn to the systems-level knowing of mobile processes.

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Extra resources for Computational Methods in Systems Biology: International Conference CMSB 2007, Edinburgh, Scotland, September 20-21, 2007. Proceedings

Sample text

5. Pulse-labelling experiment result: different pre-rRNAs are deposited at different levels within the gel according to their relative sizes (horizontal bands). As time progresses greater amounts of radioactive uracil are present and the intensity increases (vertical stripes moving from left to right). where N = 17 is the total number of possible levels. The proportional factor is obtained by considering the experimental steady state values of all the components. In Dizzy we can define new species representing the intensity of the elements detected in the experiments by using the expression 1.

The algorithm is based on likelihood ratios that appropriately weight the simulation results to obtain unbiased estimates. The algorithm is particularly useful for comparisons of a large number of parameter settings and it yields a large amount of runtime gain compared to multiple separate simulations. Thereby, no approximation and thus no loss in statistical validity and accuracy is introduced. , tau leaping. 3 For small parameter perturbations this runtime is roughly the same for all perturbed parameter settings.

G. [23,24]) guarantees that the Radon-Nikodym derivative L = dP/dP ∗ exists and that L(ω)dP ∗ . ∀A ∈ A : P (A) = (8) A In the context of Importance Sampling, the probability measure P ∗ is called the Importance Sampling measure, and the Radon-Nikodym derivative L is usually referred to as the likelihood ratio. The basic property exploited by Importance Sampling is that for any random variable Y on (Ω, A), expectations with respect to P are identical to expectations with respect to P ∗ when weighting by the likelihood ratio.

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Computational Methods in Systems Biology: International Conference CMSB 2007, Edinburgh, Scotland, September 20-21, 2007. Proceedings by Raya Khanin, Desmond J. Higham (auth.), Muffy Calder, Stephen Gilmore (eds.)


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