32746 20210121114451.0 doi 10.1186/s12859-015-0765-z sideral 92829 ART-2015-92829 eng (orcid)0000-0002-7093-228X Júlvez, J. Universidad de Zaragoza A straightforward method to compute average stochastic oscillations from data samples 2015 Access copy available to the general public Unrestricted Background: 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. info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 2.435 2015 MATHEMATICAL & COMPUTATIONAL BIOLOGY 10 / 56 = 0.179 2015 Q1 T1 BIOTECHNOLOGY & APPLIED MICROBIOLOGY 64 / 161 = 0.398 2015 Q2 T2 BIOCHEMICAL RESEARCH METHODS 39 / 77 = 0.506 2015 Q3 T2 1.737 2015 Applied Mathematics 2015 Q1 Biochemistry 2015 Q1 Computer Science Applications 2015 Q1 Molecular Biology 2015 Q2 Structural Biology 2015 Q2 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 5007 570 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Lenguajes y Sistemas Inf. 16, 1 (2015), [13 pp.] BMC bioinformatics BMC BIOINFORMATICS 1471-2105 15218389 http://zaguan.unizar.es/record/32746/files/texto_completo.pdf Versión publicada 9823 http://zaguan.unizar.es/record/32746/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:32746 articulos driver 2021-01-21-10:46:02 ARTICLE