In this section
@article{Calderazzo:2019:bioinformatics/bty782,
author = {Calderazzo, S and Brancaccio, M and Finkenstadt, B},
doi = {bioinformatics/bty782},
journal = {Bioinformatics},
pages = {1380--1387},
title = {Filtering and inference for stochastic oscillators with distributed delays},
url = {http://dx.doi.org/10.1093/bioinformatics/bty782},
volume = {35},
year = {2019}
}
TY - JOUR
AB - MotivationThe time evolution of molecular species involved in biochemical reaction networks often arises from complex stochastic processes involving many species and reaction events. Inference for such systems is profoundly challenged by the relative sparseness of experimental data, as measurements are often limited to a small subset of the participating species measured at discrete time points. The need for model reduction can be realistically achieved for oscillatory dynamics resulting from negative translational and transcriptional feedback loops by the introduction of probabilistic time-delays. Although this approach yields a simplified model, inference is challenging and subject to ongoing research. The linear noise approximation (LNA) has recently been proposed to address such systems in stochastic form and will be exploited here.ResultsWe develop a novel filtering approach for the LNA in stochastic systems with distributed delays, which allows the parameter values and unobserved states of a stochastic negative feedback model to be inferred from univariate time-series data. The performance of the methods is tested for simulated data. Results are obtained for real data when the model is fitted to imaging data on Cry1, a key gene involved in the mammalian central circadian clock, observed via a luciferase reporter construct in a mouse suprachiasmatic nucleus.Availability and implementationProgrammes are written in MATLAB and Statistics Toolbox Release 2016 b, The MathWorks, Inc., Natick, Massachusetts, USA. Sample code and Cry1 data are available on GitHub https://github.com/scalderazzo/FLNADD.
AU - Calderazzo,S
AU - Brancaccio,M
AU - Finkenstadt,B
DO - bioinformatics/bty782
EP - 1387
PY - 2019///
SN - 1367-4803
SP - 1380
TI - Filtering and inference for stochastic oscillators with distributed delays
T2 - Bioinformatics
UR - http://dx.doi.org/10.1093/bioinformatics/bty782
UR - http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000473691900016&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
UR - https://academic.oup.com/bioinformatics/article/35/8/1380/5092929
UR - http://hdl.handle.net/10044/1/79170
VL - 35
ER -
For any enquiries about our lab, please contact:
Dr Marco Brancaccio
m.brancaccio@imperial.ac.uk
+44 (0)20 7594 7907