By Jakob Ruess, John Lygeros (auth.), Ashutosh Gupta, Thomas A. Henzinger (eds.)
This booklet constitutes the court cases of the eleventh overseas convention on Computational equipment in structures Biology, CMSB 2013, held in Klosterneuburg, Austria, in September 2013. The 15 commonplace papers integrated during this quantity have been rigorously reviewed and chosen from 27 submissions. They take care of computational types for all degrees, from molecular and mobile, to organs and full organisms.
Read or Download Computational Methods in Systems Biology: 11th International Conference, CMSB 2013, Klosterneuburg, Austria, September 22-24, 2013. Proceedings PDF
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Additional resources for Computational Methods in Systems Biology: 11th International Conference, CMSB 2013, Klosterneuburg, Austria, September 22-24, 2013. Proceedings
Example text
FAU generalises adaptive uniformisation [18] by accelerating the discretisation and neglecting states with insignificant probability. Standard uniformisation is implemented in a number of tools, including PRISM, which we enhance with the FAU functionality in this paper. SABRE [6] is the first tool to implement FAU without cumulative rewards. Both PRISM and SABRE support models written in Systems Biology Markup Language (SBML) as input, in addition to their native modelling languages. SABRE is a stand-alone tool available for download or as a web interface; it additionally offers deterministic approximations using differential equations (by the Runge-Kutta fourth order method), which is faster and leads to accurate results for large numbers of molecules.
To deal with the unbounded state space, FAU explores only the relevant states, ignoring the probability of the insignificant states. Thus, the number of states to be maintained in memory can be kept small, at a cost of some loss of precision. Importantly, the FAU method can also speed up the analysis of very large finite models. Fast adaptive uniformisation was implemented [6] and applied successfully on a variety of biological systems [6,17], but for transient probabilities only. Many useful quantitative analyses involve the computation of expected rewards, which can be instantaneous (incurred at time t) or cumulated (until time t).
If μ = 1, species are treated completely population-based. , the reaction fires equally frequent inside all Bs. Afterwards, the whole species comprises again 1000 identical As, each enclose 1000 identical Bs. If μ = 2, the As are separated into two groups, each comprises 500 As. For each group of As, the Bs are separated analogously. One reaction firing number is then computed for each group of Bs, and the amount of C is reduced accordingly. Thus, a total of four Poisson numbers are sampled. , one million Poisson numbers would be sampled.