Diffusion State Transitions in Single-Particle Trajectories of MET Receptor Tyrosine Kinase Measured in Live Cells

Single-particle tracking enables the analysis of the dynamics of biomolecules in living cells with nanometer spatial and millisecond temporal resolution. This technique reports on the mobility of membrane proteins and is sensitive to the molecular state of a biomolecule and to interactions with other biomolecules. Trajectories describe the mobility of single particles over time and provide information such as the diffusion coefficient and diffusion state. Changes in particle dynamics within single trajectories lead to segmentation, which allows to extract information on transitions of functional states of a biomolecule. Here, mean-squared displacement analysis is developed to classify trajectory segments into immobile, confined diffusing, and freely diffusing states, and to extract the occurrence of transitions between these modes. We applied this analysis to single-particle tracking data of the membrane receptor MET in live cells and analyzed state transitions in single trajectories of the un-activated receptor and the receptor bound to the ligand internalin B. We found that internalin B-bound MET shows an enhancement of transitions from freely and confined diffusing states into the immobile state as compared to un-activated MET. Confined diffusion acts as an intermediate state between immobile and free, as this state is most likely to change the diffusion state in the following segment. This analysis can be readily applied to single-particle tracking data of other membrane receptors and intracellular proteins under various conditions and contribute to the understanding of molecular states and signaling pathways.

GLIDE: combining local methods and diffusion state embeddings to predict missing interactions in biological networks

Motivation One of the core problems in the analysis of biological networks is the link prediction problem. In particular, existing interactions networks are noisy and incomplete snapshots of the true network, with many true links missing because those interactions have not yet been experimentally observed. Methods to predict missing links have been more extensively studied for social than for biological networks; it was recently argued that there is some special structure in protein–protein interaction (PPI) network data that might mean that alternate methods may outperform the best methods for social networks. Based on a generalization of the diffusion state distance, we design a new embedding-based link prediction method called global and local integrated diffusion embedding (GLIDE). GLIDE is designed to effectively capture global network structure, combined with alternative network type-specific customized measures that capture local network structure. We test GLIDE on a collection of three recently curated human biological networks derived from the 2016 DREAM disease module identification challenge as well as a classical version of the yeast PPI network in rigorous cross validation experiments. Results We indeed find that different local network structure is dominant in different types of biological networks. We find that the simple local network measures are dominant in the highly connected network core between hub genes, but that GLIDE’s global embedding measure adds value in the rest of the network. For example, we make GLIDE-based link predictions from genes known to be involved in Crohn’s disease, to genes that are not known to have an association, and make some new predictions, finding support in other network data and the literature. Availability and implementation GLIDE can be downloaded at https://bitbucket.org/kap_devkota/glide. Supplementary information Supplementary data are available at Bioinformatics online.

Are the Contents of International Treaties Copied and Pasted? Evidence from Preferential Trade Agreements

Most accounts of international negotiations suggest that global agreements are individually crafted and distinct, while some emerging scholarship suggests a heavy reliance on models and templates. In this research, we present a comprehensive test of whether new international treaties are heavily copied and pasted from past ones. We specify several reasons to expect widespread copying and pasting, and argue that both the most and least powerful countries should be most likely to do so. Using text analysis to examine several hundred preferential trade agreements (PTAs), we reveal that most PTAs copy a sizable majority of their content word for word from an earlier agreement. At least one hundred PTAs take 80 percent or more of their contents directly from a single, existing treaty—with many copying and pasting 95 percent or more. These numbers climb even higher when we compare important substantive chapters of trade agreements, many of which are copied and pasted verbatim. Such copying and pasting is most prevalent among low-capacity governments that lean heavily on existing templates, and powerful states that desire to spread their preferred rules globally. This widespread replication of existing treaty language reshapes how we think about international cooperation, and it has important implications for literatures on institutional design, policy diffusion, state power, and legal fragmentation.

Influence of Copper Vapors in SF6 Plasma

In this study a theoretical approach allows estimating the ablation mass flux of copper from a corrected Hertz-Knudsen flux. The influence of the copper vapours coming from the anode electrode to an SF₆ plasma is studied in a simplified 2D configuration. Depending on the plasma pressure an ablation or a diffusion state is considered. The amount of copper versus time is presented. An RMS current I=10 kA is applied leading at t=10 ms to an amount of copper equal to 0.55 mg. The vapours change the plasma properties mainly the electrical conductivity and radiation and so the plasma behaviour. At time t=5 ms the electrode erosion leads to a copper plasma. This simple case shows the necessity to well consider the copper erosion in plasma modelling as in High Voltage Circuit Breaker (HVCB) where higher current are considered.

Rapid diffusion-state switching underlies stable cytoplasmic gradients in the Caenorhabditis elegans zygote

Protein concentration gradients organize cells and tissues and commonly form through diffusion away from a local source of protein. Interestingly, during the asymmetric division of the Caenorhabditis elegans zygote, the RNA-binding proteins MEX-5 and PIE-1 form opposing concentration gradients in the absence of a local source. In this study, we use near-total internal reflection fluorescence (TIRF) imaging and single-particle tracking to characterize the reaction/diffusion dynamics that maintain the MEX-5 and PIE-1 gradients. Our findings suggest that both proteins interconvert between fast-diffusing and slow-diffusing states on timescales that are much shorter (seconds) than the timescale of gradient formation (minutes). The kinetics of diffusion-state switching are strongly polarized along the anterior/posterior (A/P) axis by the PAR polarity system such that fast-diffusing MEX-5 and PIE-1 particles are approximately symmetrically distributed, whereas slow-diffusing particles are highly enriched in the anterior and posterior cytoplasm, respectively. Using mathematical modeling, we show that local differences in the kinetics of diffusion-state switching can rapidly generate stable concentration gradients over a broad range of spatial and temporal scales.