Unknown

Dataset Information

0

Learning dynamical information from static protein and sequencing data.


ABSTRACT: Many complex processes, from protein folding to neuronal network dynamics, can be described as stochastic exploration of a high-dimensional energy landscape. Although efficient algorithms for cluster detection in high-dimensional spaces have been developed over the last two decades, considerably less is known about the reliable inference of state transition dynamics in such settings. Here we introduce a flexible and robust numerical framework to infer Markovian transition networks directly from time-independent data sampled from stationary equilibrium distributions. We demonstrate the practical potential of the inference scheme by reconstructing the network dynamics for several protein-folding transitions, gene-regulatory network motifs, and HIV evolution pathways. The predicted network topologies and relative transition time scales agree well with direct estimates from time-dependent molecular dynamics data, stochastic simulations, and phylogenetic trees, respectively. Owing to its generic structure, the framework introduced here will be applicable to high-throughput RNA and protein-sequencing datasets, and future cryo-electron microscopy (cryo-EM) data.

SUBMITTER: Pearce P 

PROVIDER: S-EPMC6879630 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

Similar Datasets

| S-EPMC7906608 | biostudies-literature
| S-EPMC6527573 | biostudies-other
| S-EPMC9747975 | biostudies-literature
| S-EPMC3400147 | biostudies-literature
| S-EPMC6238365 | biostudies-literature
| S-EPMC4333612 | biostudies-other
| S-EPMC7547683 | biostudies-literature
| S-EPMC4758070 | biostudies-literature
| S-EPMC6258556 | biostudies-literature
| S-EPMC8696089 | biostudies-literature