Projects per year
Often the time-derivative of a measured variable is of as much interest as the variable itself. For a growing population of biological cells, for example, the population's growth rate is typically more important than its size. Here we introduce a non-parametric method to infer first and second time-derivatives as a function of time from time-series data. Our approach is based on Gaussian processes and applies to a wide range of data. In tests, the method is at least as accurate as others, but has several advantages: it estimates errors both in the inference and in any summary statistics, such as lag times, and allows interpolation with the corresponding error estimation. As illustrations, we infer growth rates of microbial cells, the rate of assembly of an amyloid fibril, and both the speed and acceleration of two separating spindle pole bodies. Our algorithm should thus be broadly applicable.
|Number of pages||8|
|Publication status||Published - 12 Dec 2016|
- Time series
FingerprintDive into the research topics of 'Inferring time-derivatives including cell growth rates using Gaussian processes'. Together they form a unique fingerprint.
- 1 Finished
SynthSys-Mammalian: Edinburgh Mammalian Synthetic Biology Research Centre
14/11/14 → 31/03/22
Inferring time derivatives, including cell growth rates, using Gaussian processes
Swain, P. (Creator), Edinburgh DataShare, May 2016
- School of Biological Sciences - SULSA Chair of Systems Biology
- Centre for Engineering Biology
Person: Academic: Research Active