000032746 001__ 32746
000032746 005__ 20210121114451.0
000032746 0247_ $$2doi$$a10.1186/s12859-015-0765-z
000032746 0248_ $$2sideral$$a92829
000032746 037__ $$aART-2015-92829
000032746 041__ $$aeng
000032746 100__ $$0(orcid)0000-0002-7093-228X$$aJúlvez, J.$$uUniversidad de Zaragoza
000032746 245__ $$aA straightforward method to compute average stochastic oscillations from data samples
000032746 260__ $$c2015
000032746 5060_ $$aAccess copy available to the general public$$fUnrestricted
000032746 5203_ $$aBackground: Many biological systems exhibit sustained stochastic oscillations in their steady state. Assessing these oscillations is usually a challenging task due to the potential variability of the amplitude and frequency of the oscillations over time. As a result of this variability, when several stochastic replications are averaged, the oscillations are flattened and can be overlooked. This can easily lead to the erroneous conclusion that the system reaches a constant steady state. Results: This paper proposes a straightforward method to detect and asses stochastic oscillations. The basis of the method is in the use of polar coordinates for systems with two species, and cylindrical coordinates for systems with more than two species. By slightly modifying these coordinate systems, it is possible to compute the total angular distance run by the system and the average Euclidean distance to a reference point. This allows us to compute confidence intervals, both for the average angular speed and for the distance to a reference point, from a set of replications. Conclusions: The use of polar (or cylindrical) coordinates provides a new perspective of the system dynamics. The mean trajectory that can be obtained by averaging the usual cartesian coordinates of the samples informs about the trajectory of the center of mass of the replications. In contrast to such a mean cartesian trajectory, the mean polar trajectory can be used to compute the average circular motion of those replications, and therefore, can yield evidence about sustained steady state oscillations. Both, the coordinate transformation and the computation of confidence intervals, can be carried out efficiently. This results in an efficient method to evaluate stochastic oscillations.
000032746 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/
000032746 590__ $$a2.435$$b2015
000032746 591__ $$aMATHEMATICAL & COMPUTATIONAL BIOLOGY$$b10 / 56 = 0.179$$c2015$$dQ1$$eT1
000032746 591__ $$aBIOTECHNOLOGY & APPLIED MICROBIOLOGY$$b64 / 161 = 0.398$$c2015$$dQ2$$eT2
000032746 591__ $$aBIOCHEMICAL RESEARCH METHODS$$b39 / 77 = 0.506$$c2015$$dQ3$$eT2
000032746 592__ $$a1.737$$b2015
000032746 593__ $$aApplied Mathematics$$c2015$$dQ1
000032746 593__ $$aBiochemistry$$c2015$$dQ1
000032746 593__ $$aComputer Science Applications$$c2015$$dQ1
000032746 593__ $$aMolecular Biology$$c2015$$dQ2
000032746 593__ $$aStructural Biology$$c2015$$dQ2
000032746 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion
000032746 7102_ $$15007$$2570$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Lenguajes y Sistemas Inf.
000032746 773__ $$g16, 1 (2015), [13 pp.]$$pBMC bioinformatics$$tBMC BIOINFORMATICS$$x1471-2105
000032746 8564_ $$s15218389$$uhttps://zaguan.unizar.es/record/32746/files/texto_completo.pdf$$yVersión publicada
000032746 8564_ $$s9823$$uhttps://zaguan.unizar.es/record/32746/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada
000032746 909CO $$ooai:zaguan.unizar.es:32746$$particulos$$pdriver
000032746 951__ $$a2021-01-21-10:46:02
000032746 980__ $$aARTICLE