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Artificial neural networks enable genome-scale simulations of intracellular signaling

Author

Listed:
  • Avlant Nilsson

    (Massachusetts Institute of Technology
    Chalmers University of Technology
    Ragon Institute of MGH, MIT, and Harvard)

  • Joshua M. Peters

    (Massachusetts Institute of Technology
    Ragon Institute of MGH, MIT, and Harvard)

  • Nikolaos Meimetis

    (Massachusetts Institute of Technology)

  • Bryan Bryson

    (Massachusetts Institute of Technology
    Ragon Institute of MGH, MIT, and Harvard)

  • Douglas A. Lauffenburger

    (Massachusetts Institute of Technology
    Ragon Institute of MGH, MIT, and Harvard)

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.

Suggested Citation

  • Avlant Nilsson & Joshua M. Peters & Nikolaos Meimetis & Bryan Bryson & Douglas A. Lauffenburger, 2022. "Artificial neural networks enable genome-scale simulations of intracellular signaling," Nature Communications, Nature, vol. 13(1), pages 1-16, December.
  • Handle: RePEc:nat:natcom:v:13:y:2022:i:1:d:10.1038_s41467-022-30684-y
    DOI: 10.1038/s41467-022-30684-y
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    References listed on IDEAS

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    1. Mak, K.L. & Peng, J.G. & Xu, Z.B. & Yiu, K.F.C., 2007. "A new stability criterion for discrete-time neural networks: Nonlinear spectral radius," Chaos, Solitons & Fractals, Elsevier, vol. 31(2), pages 424-436.
    2. Charles R. Harris & K. Jarrod Millman & Stéfan J. Walt & Ralf Gommers & Pauli Virtanen & David Cournapeau & Eric Wieser & Julian Taylor & Sebastian Berg & Nathaniel J. Smith & Robert Kern & Matti Picu, 2020. "Array programming with NumPy," Nature, Nature, vol. 585(7825), pages 357-362, September.
    3. Magnus, Jan R., 1985. "On Differentiating Eigenvalues and Eigenvectors," Econometric Theory, Cambridge University Press, vol. 1(2), pages 179-191, August.
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    Cited by:

    1. Xi Xi & Jiaqi Li & Jinmeng Jia & Qiuchen Meng & Chen Li & Xiaowo Wang & Lei Wei & Xuegong Zhang, 2025. "A mechanism-informed deep neural network enables prioritization of regulators that drive cell state transitions," Nature Communications, Nature, vol. 16(1), pages 1-18, December.

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