ArticleNature communications2022
Artificial neural networks enable genome-scale simulations of intracellular signaling.
Article in Nature communications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers.
What it found
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Who cites it
32 citing papers in PubMed, 42 citations in OpenAlex.
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- Mathematical and Computational Models of Biochemical Reactions and Cell Signaling-From Ordinary Differential Equations to Machine Learning.International journal of molecular sciences · 2026Review
- Computational prediction of TCR cross-reactivity: principles, challenges and translational opportunities.Journal of translational medicine · 2026Review
- Lung mucosal and systemic responses at single-cell resolution in an aerosolized Mycobacterium bovis BCG human challenge model.Cell reports. Medicine · 2026Article
- MultiOmicsXplorer, a tool to browse, access and analyse multi-omics data.BMC bioinformatics · 2026Article
- Zero-shot prediction of drug responses using biologically informed neural networks trained on phosphoproteomic timeseries.PLoS computational biology · 2026Article
- Circadian rhythms in the tumor microenvironment: spatiotemporal immune regulation and chronotherapeutic opportunities.Frontiers in immunology · 2026Review
- Biologically informed neural network models are robust to spurious interactions via self-pruning.bioRxiv : the preprint server for biology · 2025Article
- Decoding lysophosphatidic acid signaling in physiology and disease: mapping the multimodal and multinodal signaling networks.Signal transduction and targeted therapy · 2025Review
- The Dawn of High-Throughput and Genome-Scale Kinetic Modeling: Recent Advances and Future Directions.ACS synthetic biology · 2025Review
- Library-based single-cell analysis of CAR signaling reveals drivers of in vivo persistence.Cell systems · 2025Article
- Systems Biology of the Cancer Cell.Annual review of biomedical engineering · 2025Review
- Towards an interpretable deep learning model of cancer.NPJ precision oncology · 2025Review
- A mechanism-informed deep neural network enables prioritization of regulators that drive cell state transitions.Nature communications · 2025Article
- RBI: a novel algorithm for regulatory-metabolic network model in designing the optimal mutant strain.PeerJ. Computer science · 2025Article
- Molecular causality in the advent of foundation models.Molecular systems biology · 2024Review
- Bridging systems biology and tissue engineering: Unleashing the full potential of complex 3DBiophysics reviews · 2024Review
- Inference of drug off-target effects on cellular signaling using interactome-based deep learning.iScience · 2024Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
Abstract
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 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.