Download e-book for iPad: Computational Methods in Systems Biology: International by Muffy Calder, Stephen Gilmore

By Muffy Calder, Stephen Gilmore

ISBN-10: 3540751394

ISBN-13: 9783540751397

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

The sixteen revised complete papers provided have been rigorously reviewed and chosen. The papers current numerous concepts from laptop technology, resembling language layout, concurrency conception, software program engineering, and formal tools, for biologists, physicists, and mathematicians drawn to the systems-level figuring out of mobile processes.

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

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In [3] a kinetic-dynamic model was proposed to simulate RNA processing by determining the essential reaction rates, including the rates of transcription, pre-mRNA turnover, pre-mRNA splicing, and mRNA decay. A simulator based on the family competition evolutionary algorithm was applied on several artificial datasets and on a simplified yeast expression dataset. The authors of [4] presented and analysed a model of protein translation at the scale of an individual messenger RNA (mRNA) transcript. In [5] the authors proposed the development of mathematical models that quantitatively describe the complex process of transcription, RNA processing, transport, translation and mRNA turnover.

1) occur, how often certain sets of states are reached, or at what time certain (possibly absorbing) states are reached for the first time. For stochastic chemical kinetics the latter corresponds to the question how long it takes until molecules of certain species are exhausted. 2 Importance Sampling for Biological Networks For CTMCs, the probability measures P and P ∗ are path distributions and absolute continuity corresponds to the condition that all paths that are possible under P, that is in the original model, must remain possible under P ∗ .

In any case, it is of course not sufficient to generate only one single trajectory since this only corresponds to one single realization obtained by one specific set of random numbers. Different sets of random numbers imply different trajectories and the resulting estimator, the sample mean, is formally a random variable that has a variance. It is thus necessary to perform sufficiently many independent simulation runs (generate sufficiently many trajectories) such that the variance of the sample mean lies within a reasonable range.

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Computational Methods in Systems Biology: International Conference CMSB 2007, Edinburgh, Scotland, September 20-21, 2007, Proceedings by Muffy Calder, Stephen Gilmore


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