Artificial neural networks enable genome-scale simulations of intracellular signaling
<jats:title>Abstract</jats:title><jats:p>Mammalian 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 tha...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Springer Science and Business Media LLC
2023
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| Online Access: | https://hdl.handle.net/1721.1/147780 |
| _version_ | 1826204998554353664 |
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| author | Nilsson, Avlant Peters, Joshua M Meimetis, Nikolaos Bryson, Bryan Lauffenburger, Douglas A |
| author2 | Massachusetts Institute of Technology. Department of Biological Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Biological Engineering Nilsson, Avlant Peters, Joshua M Meimetis, Nikolaos Bryson, Bryan Lauffenburger, Douglas A |
| author_sort | Nilsson, Avlant |
| collection | MIT |
| description | <jats:title>Abstract</jats:title><jats:p>Mammalian 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 <jats:italic>r</jats:italic> = 0.98) and the effects of gene knockouts (<jats:italic>r</jats:italic> = 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 (<jats:italic>r</jats:italic> = 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.</jats:p> |
| first_indexed | 2024-09-23T13:05:27Z |
| format | Article |
| id | mit-1721.1/147780 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T13:05:27Z |
| publishDate | 2023 |
| publisher | Springer Science and Business Media LLC |
| record_format | dspace |
| spelling | mit-1721.1/1477802023-02-01T03:28:51Z Artificial neural networks enable genome-scale simulations of intracellular signaling Nilsson, Avlant Peters, Joshua M Meimetis, Nikolaos Bryson, Bryan Lauffenburger, Douglas A Massachusetts Institute of Technology. Department of Biological Engineering <jats:title>Abstract</jats:title><jats:p>Mammalian 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 <jats:italic>r</jats:italic> = 0.98) and the effects of gene knockouts (<jats:italic>r</jats:italic> = 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 (<jats:italic>r</jats:italic> = 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.</jats:p> 2023-01-30T15:13:18Z 2023-01-30T15:13:18Z 2022 2023-01-30T15:08:51Z Article http://purl.org/eprint/type/JournalArticle https://hdl.handle.net/1721.1/147780 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). en 10.1038/S41467-022-30684-Y Nature Communications Creative Commons Attribution 4.0 International license https://creativecommons.org/licenses/by/4.0/ application/pdf Springer Science and Business Media LLC Nature |
| spellingShingle | Nilsson, Avlant Peters, Joshua M Meimetis, Nikolaos Bryson, Bryan Lauffenburger, Douglas A Artificial neural networks enable genome-scale simulations of intracellular signaling |
| title | Artificial neural networks enable genome-scale simulations of intracellular signaling |
| title_full | Artificial neural networks enable genome-scale simulations of intracellular signaling |
| title_fullStr | Artificial neural networks enable genome-scale simulations of intracellular signaling |
| title_full_unstemmed | Artificial neural networks enable genome-scale simulations of intracellular signaling |
| title_short | Artificial neural networks enable genome-scale simulations of intracellular signaling |
| title_sort | artificial neural networks enable genome scale simulations of intracellular signaling |
| url | https://hdl.handle.net/1721.1/147780 |
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