A system of recurrent neural networks for modularising, parameterising and dynamic analysis of cell signalling networks

Samarasinghe, Sandhya; Ling, Hong

doi:10.1016/j.biosystems.2017.01.001

Cited by 4 publications

(2 citation statements)

References 51 publications

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“…However, FFNN do not allow feedback loops, which are frequent in signaling, and therefore recurrent neural networks (RNN) may be a more suitable architecture for modeling signaling networks. It has previously been shown that a RNN without prior knowledge constraints can recapitulate the output of a small ODE-model of signaling 23 .…”

Section: Introductionmentioning

confidence: 99%

Artificial neural networks enable genome-scale simulations of intracellular signaling

et al. 2022

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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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Section: Introductionmentioning

confidence: 99%

Artificial neural networks enable genome-scale simulations of intracellular signaling

et al. 2022

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“…However, FFNN do not allow feedback loops, which are frequent in signaling, and therefore recurrent neural networks (RNN) may be a more suitable architecture for modeling signaling networks. It has previously been shown that a an RNN without prior knowledge constraints can recapitulate the output of a small ODE-model of signaling 23 .…”

Section: Introductionmentioning

confidence: 99%

Artificial neural networks enable genome-scale simulations of intracellular signaling

Nilsson

Peters

Bryson

et al. 2021

Preprint

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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 (TF) 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 constrained by prior knowledge of the signaling network with ligand concentrations as input, TF activity as output and signaling molecules as hidden nodes. Simulations are assumed to reach steady state, and we regularize the parameters to enforce this. Using synthetic data, we train models that generalize to unseen data and predict the effects of gene knockouts. We also fit models to a small experimental data set from literature and confirm the predictions using cross validation. This demonstrates the feasibility of simulating intracellular signaling at the genome-scale.

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Cell cycle involvement in cancer therapy; WEE1 kinase, a potential target as therapeutic strategy

Vakili-Samiani

Khanghah

Gholipour

et al. 2022

Mutation Research/Fundamental and Molecular Mechanisms of Mutag

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A system of recurrent neural networks for modularising, parameterising and dynamic analysis of cell signalling networks

Cited by 4 publications

References 51 publications

Artificial neural networks enable genome-scale simulations of intracellular signaling

Artificial neural networks enable genome-scale simulations of intracellular signaling

Artificial neural networks enable genome-scale simulations of intracellular signaling

Cell cycle involvement in cancer therapy; WEE1 kinase, a potential target as therapeutic strategy

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