TU Darmstadt / ULB / TUprints

Stabilized Reconstruction of Signaling Networks from Single-Cell Cue-Response Data

Kumar, Sunil ; Lun, Xiao-Kang ; Bodenmiller, Bernd ; Rodríguez Martínez, María ; Koeppl, Heinz (2022)
Stabilized Reconstruction of Signaling Networks from Single-Cell Cue-Response Data.
In: Scientific Reports, 2020, (1)
doi: 10.26083/tuprints-00021513
Article, Secondary publication, Publisher's Version

[img] Text
s41598-019-56444-5.pdf
Copyright Information: CC BY 4.0 International - Creative Commons, Attribution.

Download (2MB)
[img] Text (Supplement)
41598_2019_56444_MOESM1_ESM.pdf
Copyright Information: CC BY 4.0 International - Creative Commons, Attribution.

Download (8MB)
Item Type: Article
Type of entry: Secondary publication
Title: Stabilized Reconstruction of Signaling Networks from Single-Cell Cue-Response Data
Language: English
Date: 2022
Place of Publication: Darmstadt
Year of primary publication: 2020
Publisher: Springer
Journal or Publication Title: Scientific Reports
Issue Number: 1
Series Volume: 10
Collation: 9 Seiten
DOI: 10.26083/tuprints-00021513
Corresponding Links:
Origin: Secondary publication service
Abstract:

Inferring cell-signaling networks from high-throughput data is a challenging problem in systems biology. Recent advances in cytometric technology enable us to measure the abundance of a large number of proteins at the single-cell level across time. Traditional network reconstruction approaches usually consider each time point separately, resulting thus in inferred networks that strongly vary across time. To account for the possibly time-invariant physical couplings within the signaling network, we extend the traditional graphical lasso with an additional regularizer that penalizes network variations over time. ROC evaluation of the method on in silico data showed higher reconstruction accuracy than standard graphical lasso. We also tested our approach on single-cell mass cytometry data of IFNγ-stimulated THP1 cells with 26 phospho-proteins simultaneously measured. Our approach recapitulated known signaling relationships, such as connection within the JAK/STAT pathway, and was further validated in characterizing perturbed signaling network with PI3K, MEK1/2 and AMPK inhibitors.

Status: Publisher's Version
URN: urn:nbn:de:tuda-tuprints-215133
Classification DDC: 000 Generalities, computers, information > 004 Computer science
500 Science and mathematics > 510 Mathematics
600 Technology, medicine, applied sciences > 660 Chemical engineering
Divisions: 18 Department of Electrical Engineering and Information Technology > Institute for Telecommunications > Bioinspired Communication Systems
18 Department of Electrical Engineering and Information Technology > Self-Organizing Systems Lab
Date Deposited: 20 Jul 2022 13:39
Last Modified: 13 Apr 2023 10:52
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/21513
PPN: 50676379X
Export:
Actions (login required)
View Item View Item