AI Article Synopsis
- Single-particle tracking (SPT) allows researchers to study the movement of individual proteins in living cells with high precision, revealing how proteins like Rho GTPases interact with their environment.
- The new computational method, perturbation expectation-maximization (pEM), analyzes protein movement to identify different diffusive states, their characteristics, and the likelihood of trajectories corresponding to each state.
- The text also offers a detailed guide on using pEM effectively, along with insights into its advantages and limitations in practical applications.
Article Abstract
Single-particle tracking (SPT) enables the ability to noninvasively probe the diffusive motions of individual proteins inside living cells at sub-diffraction-limit resolution. The stochastic motions of diffusing Rho GTPases encode information concerning its interactions with binding partners and with its local environment. By identifying Rho GTPases' diffusive states, insight can thus be gained into the spatiotemporal in vivo biochemistry inside live cells at a single-molecule resolution. Here we present perturbation expectation-maximization (pEM), a computational method which simultaneously analyzes a population of protein trajectories to uncover the system of diffusive behaviors: (1) the number of diffusive states, (2) the properties of each such diffusive state, and (3) the probabilities of each trajectory to a respective diffusive state. We provide a step-by-step guide to pEM and discuss considerations for its practical applications, including pEM's capabilities and limitations.
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| http://dx.doi.org/10.1007/978-1-4939-8612-5_5 | DOI Listing |
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