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Filtering and inference for stochastic oscillators with distributed delays.
Silvia Calderazzo1,2, Marco Brancaccio3, Bärbel Finkenstädt1
1Department of Statistics, University of Warwick, Coventry, UK.
Bioinformatics (Oxford, England)
|September 12, 2018
Summary
This study introduces a new filtering method for stochastic systems with delays, enabling inference of parameters and states from limited data. The approach is validated using simulated and real biological data, including the mammalian circadian clock gene Cry1.
Area of Science:
- Systems biology
- Computational neuroscience
- Biophysics
Background:
- Biochemical reaction networks involve complex stochastic processes and sparse experimental data, challenging system inference.
- Model reduction for oscillatory dynamics in negative feedback loops can be achieved using probabilistic time-delays, but inference remains difficult.
- The linear noise approximation (LNA) offers a stochastic framework for analyzing such complex systems.
Purpose of the Study:
- To develop a novel filtering approach for the LNA in stochastic systems with distributed delays.
- To enable inference of parameter values and unobserved states from univariate time-series data for stochastic negative feedback models.
- To apply and validate the developed method using both simulated and real biological data.
Main Methods:
- Development of a novel filtering approach tailored for the linear noise approximation (LNA) in stochastic systems.
- Application of probabilistic time-delays for model reduction in systems with oscillatory dynamics and negative feedback loops.
- Inference of model parameters and unobserved states from univariate time-series data.
Main Results:
- The novel filtering approach successfully infers parameters and states from univariate time-series data in simulated stochastic systems.
- The method was applied to real biological data, fitting a model to imaging data of the Cry1 gene in the mammalian circadian clock.
- The approach demonstrated its utility in analyzing complex biological systems with limited experimental observations.
Conclusions:
- The developed filtering approach provides a powerful tool for inferring properties of stochastic systems with delays from sparse data.
- This method advances the analysis of complex biological oscillators, such as the mammalian circadian clock.
- The study highlights the potential of the linear noise approximation combined with probabilistic time-delays for systems biology research.
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