ArticleJournal of computational biology : a journal of computational molecular cell biology2012
Using noise for model-testing.
Article in Journal of computational biology : a journal of computational molecular cell biology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
- Stochastic sensitivity analysis and kernel inference via distributional data.Biophysical journal · 2014Article
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Abstract
For realistic models in molecular biology, you need to consider the noise in the cellular and intracellular environments. In this article, we present a novel approach for testing the validity of nonlinear models representing a biological system affected by noise. Our approach is based on results by Kushner and Øksendal and uses computational techniques that rely on efficient solvers. By providing analytically upper bounds for the exit probability of solution trajectories of a system from a particular set in the phase space, we can compare measurement data with this prediction and try to invalidate models with certain parameter values or noise properties. Thus, our approach complements the usual methods that are based on deterministic models. It is particularly useful in the field of reverse engineering in systems biology, when one seeks to determine model parameters and noise properties as we show in the Results section, where we applied the approach to examples of increasing complexity and to the Hog1 signalling pathway.
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