CODEX, a neural network approach to explore signaling dynamics landscapes.
Journal:
Molecular systems biology
PMID:
33835701
Abstract
Current studies of cell signaling dynamics that use live cell fluorescent biosensors routinely yield thousands of single-cell, heterogeneous, multi-dimensional trajectories. Typically, the extraction of relevant information from time series data relies on predefined, human-interpretable features. Without a priori knowledge of the system, the predefined features may fail to cover the entire spectrum of dynamics. Here we present CODEX, a data-driven approach based on convolutional neural networks (CNNs) that identifies patterns in time series. It does not require a priori information about the biological system and the insights into the data are built through explanations of the CNNs' predictions. CODEX provides several views of the data: visualization of all the single-cell trajectories in a low-dimensional space, identification of prototypic trajectories, and extraction of distinctive motifs. We demonstrate how CODEX can provide new insights into ERK and Akt signaling in response to various growth factors, and we recapitulate findings in p53 and TGFβ-SMAD2 signaling.
Authors
Keywords
Algorithms
Animals
Cell Line
Databases as Topic
Dose-Response Relationship, Radiation
Drosophila
Extracellular Signal-Regulated MAP Kinases
Fluorescent Dyes
Humans
Intercellular Signaling Peptides and Proteins
Light
Machine Learning
Movement
Neural Networks, Computer
Proto-Oncogene Proteins c-akt
Radiation, Ionizing
Signal Transduction
Transforming Growth Factor beta
Tumor Suppressor Protein p53