Artificial neural networks enable genome-scale simulations of intracellular signaling
Name
s41467-022-30684-y.pdf
Description
Published version
Size
4.16 MB
Format
Adobe PDF
Checksum (MD5)
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Author(s)
Nilsson, Avlant
Peters, Joshua M
Meimetis, Nikolaos
Bryson, Bryan
Lauffenburger, Douglas A
Date Issued
2022
Journal
Nature Communications
Publisher
Springer Science and Business Media LLC
Citation
Nilsson, Avlant, Peters, Joshua M, Meimetis, Nikolaos, Bryson, Bryan and Lauffenburger, Douglas A. 2022. "Artificial neural networks enable genome-scale simulations of intracellular signaling." Nature Communications, 13 (1).
Version
Final published version
Abstract
AbstractMammalian cells adapt their functional state in response to external signals in form of ligands that bind receptors on the cell-surface. Mechanistically, this involves signal-processing through a complex network of molecular interactions that govern transcription factor activity patterns. Computer simulations of the information flow through this network could help predict cellular responses in health and disease. Here we develop a recurrent neural network framework constrained by prior knowledge of the signaling network with ligand-concentrations as input and transcription factor-activity as output. Applied to synthetic data, it predicts unseen test-data (Pearson correlation r = 0.98) and the effects of gene knockouts (r = 0.8). We stimulate macrophages with 59 different ligands, with and without the addition of lipopolysaccharide, and collect transcriptomics data. The framework predicts this data under cross-validation (r = 0.8) and knockout simulations suggest a role for RIPK1 in modulating the lipopolysaccharide response. This work demonstrates the feasibility of genome-scale simulations of intracellular signaling.
MIT Department
Massachusetts Institute of Technology. Department of Biological Engineering
Terms of Use
Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.1038/S41467-022-30684-Y
