Network theory approach for data evaluation in the dynamic force spectroscopy of biomolecular interactions

Abstract

Investigations of bonds between single molecules and molecular complexes by dynamic force spectroscopy are subject to large fluctuations at nanoscale and possible aspecific binding, which mask the experimental output. Big efforts are devoted to develop methods for the effective selection of the relevant experimental data, before the quantitative analysis of bond parameters. Here we present a methodology which is based on the application of graph theory. The forcedistance curves corresponding to repeated pulling events are mapped onto their correlation network (mathematical graph). On these graphs the groups of similar curves appear as topological modules, which are identified using the spectral analysis of graphs. We demonstrate the approach by analyzing a large ensemble of the force-distance curves measured on: ssDNA-ssDNA, peptide-RNA (from HIV1), and peptide-Au surface systems. Within our data sets the methodology systematically separates subgroups of curves which are related to different types of intermolecular interactions and to spatial arrangements in which the molecules are brought together and/or pulling speeds. This demonstrates the sensitivity of the method to the spatial degrees of freedom, suggesting potential applications in the case of large molecular complexes and situations with multiple binding sites.

The Dynamics of Emotional Chats with Bots: Experiment and Agent-Based Simulations

“…Here, we use network theory methods to extract patterns of similar response of users to each particular profile of the Bot. Creating a correlation matrix is a first step to infer such information from multichannel data; it has been practiced in the analysis of large throughput gene expression experiments [36,37], classifying similar force-distance curves in repeated force-spectroscopy measurements of molecular forces [38,39], finding relevant correlations in fluctuations of different shares in stock market [40] and identifying patterns in traffic jamming [41,42]. Here, we construct Pearson's coefficient C i j of the valence time series between all pairs of users, i, j = 1, 2 The time series of valence {v i (t k )} for each user i = 1, 2, • • • 91 are available in the experimental dataset.…”