Artificial neural networks enable genome-scale simulations of intracellular signaling.
Artificial neural networks enable genome-scale simulations of intracellular signaling.
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DOI：
10.1038/s41467-022-30684-y
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发表时间：
2022-06-02
影响因子：
16.6
通讯作者：
中科院分区：
文献类型：
--
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哺乳动物细胞通过与细胞表面受体结合的配体来适应外部信号，从而调整其功能状态。从机制上讲，这涉及到通过控制转录因子活性模式的复杂分子相互作用网络进行信号处理。计算机模拟通过这个网络的信息流可以帮助预测健康和疾病中的细胞反应。在这里，我们开发了一个循环神经网络框架，受信号网络的先验知识的约束，配体浓度作为输入，转录因子活性作为输出。应用于合成数据，它预测了未知的测试数据（Pearson相关r = 0.98）和基因敲除的影响（r = 0.8）。我们用59种不同的配体刺激巨噬细胞，有或没有添加脂多糖，并收集转录组学数据。该框架在交叉验证下预测了这一数据（r = 0.8），敲除模拟表明RIPK1在调节脂多糖反应中起作用。这项工作证明了基因组尺度模拟细胞内信号传导的可行性。许多疾病是由于允许细胞对外部信号作出反应的生化反应网络被破坏而引起的。在这里，Nilsson等人开发了一种方法来模拟细胞信号，使用人工神经网络来预测细胞反应和信号分子的活动。
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. Many diseases are caused by disruptions to the network of biochemical reactions that allow cells to respond to external signals. Here Nilsson et al develop a method to simulate cellular signaling using artificial neural networks to predict cellular responses and activities of signaling molecules.
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影响因子：
4.3
作者：
Bertram, Richard;Rubin, Jonathan E.
通讯作者：
Rubin, Jonathan E.
影响因子：
64.8
作者：
Harris CR;Millman KJ;van der Walt SJ;Gommers R;Virtanen P;Cournapeau D;Wieser E;Taylor J;Berg S;Smith NJ;Kern R;Picus M;Hoyer S;van Kerkwijk MH;Brett M;Haldane A;Del Río JF;Wiebe M;Peterson P;Gérard-Marchant P;Sheppard K;Reddy T;Weckesser W;Abbasi H;Gohlke C;Oliphant TE
通讯作者：
Oliphant TE
影响因子：
64.5
作者：
Antebi YE;Linton JM;Klumpe H;Bintu B;Gong M;Su C;McCardell R;Elowitz MB
通讯作者：
Elowitz MB
影响因子：
48
作者：
Browaeys, Robin;Saelens, Wouter;Saeys, Yvan
通讯作者：
Saeys, Yvan
影响因子：
64.5
作者：
Dixit, Atray;Pamas, Oren;Li, Biyu;Chen, Jenny;Fulco, Charles P.;Jerby-Amon, Livnat;Marjanovic, Nemanja D.;Dionne, Danielle;Burks, Tyler;Raychowdhury, Raktima;Adamson, Britt;Norman, Thomas M.;Lander, Eric S.;Weissman, Jonathan S.;Friedman, Nir;Regev, Aviv
通讯作者：
Regev, Aviv