We devise a method to obtain a robust estimate of forces in a heterogeneous environment based on experimentally recorded  trajectories. In particular, we focus on systems modeled by the heterogeneous overdamped Langevin equation. Here, the observed force includes a "spurious" force term due to heterogeneous diffusivity . We show how Bayesian inference can be leveraged to reliably infer forces by taking into account various noise sources including such spurious forces  . The method is based on the marginalization of the force posterior over all possible spurious force contributions. The approach is combined with a Bayes factor statistical test for the presence of forces. The performance of our method is investigated analytically, numerically and tested on experimental data sets. The main results are obtained in a closed form allowing for direct exploration of their properties and fast computation. The method is incorporated into  an open-source software platform for automated analysis of random walks .
We devise a method to detect and estimate forces in a heterogeneous environment based on experimentally recorded stochastic trajectories. In particular, we focus on systems modeled by the heterogeneous overdamped Langevin equation. Here, the observed drift includes a "spurious" force term when the diffusivity varies in space . We show how Bayesian inference can be leveraged to reliably infer forces by taking into account such spurious forces of unknown amplitude as well as experimental sources of error . The method is based on marginalizing the force posterior over all possible spurious force contributions. The approach is combined with a Bayes factor statistical test for the presence of forces. The performance of our method is investigated analytically, numerically and tested on experimental data sets. The main results are obtained in a closed form allowing for direct exploration of their properties and fast computation. The method is incorporated into TRamWAy, an open-source software platform for automated analysis of biomolecule trajectories .
R
obtain a robust estimate of
detect and estimate
A
stochastic
R
force
drift
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due to heterogeneous diffusivity
when the diffusivity varies in space
D
various noise sources including
A
of unknown amplitude as well as experimental sources of error
R
the marginalization of the
marginalizing the
A
TRamWAy,
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random walks
biomolecule trajectories