CODEX, a neural network approach to explore signaling dynamics landscapes.

Jacques, Marc-Antoine; Dobrzyński, Maciej; Gagliardi, Paolo Armando; Sznitman, Raphael; Pertz, Olivier (2021). CODEX, a neural network approach to explore signaling dynamics landscapes. Molecular systems biology, 17(4), e10026. EMBO Press 10.15252/msb.202010026

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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.

Item Type:

Journal Article (Original Article)

Division/Institute:

10 Strategic Research Centers > ARTORG Center for Biomedical Engineering Research
08 Faculty of Science > Department of Biology > Institute of Cell Biology
09 Interdisciplinary Units > Microscopy Imaging Center (MIC)

UniBE Contributor:

Jacques, Marc-Antoine Frédéric Roméo; Dobrzyński, Maciej; Gagliardi, Paolo Armando; Sznitman, Raphael and Pertz, Olivier

Subjects:

500 Science > 570 Life sciences; biology
600 Technology > 610 Medicine & health

ISSN:

1744-4292

Publisher:

EMBO Press

Language:

English

Submitter:

Olivier Pertz

Date Deposited:

31 May 2021 10:56

Last Modified:

06 Jun 2021 03:17

Publisher DOI:

10.15252/msb.202010026

PubMed ID:

33835701

Uncontrolled Keywords:

cell signaling convolutional neural network data exploration live biosensor imaging time series analysis

BORIS DOI:

10.48350/156472

URI:

https://boris.unibe.ch/id/eprint/156472

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