Stochastic networks articles from across Nature Portfolio

Latest Research and Reviews

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  Emergence of a mutual-growth mechanism in networks evolved by social preference based on indirect utility

  • Jong-Hyeok Lee
  • Ken-ichiro Ogawa

  ResearchOpen Access07 Dec 2023 Scientific Reports

  Volume: 13, P: 21680
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  Integrating glycolysis, citric acid cycle, pentose phosphate pathway, and fatty acid beta-oxidation into a single computational model

  • Sylwester M. Kloska
  • Krzysztof Pałczyński
  • Tadeusz A. Wysocki

  ResearchOpen Access02 Sept 2023 Scientific Reports

  Volume: 13, P: 14484
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  Neural-network solutions to stochastic reaction networks

  Stochastic reaction networks involve solving a system of ordinary differential equations, which becomes challenging as the number of reactive species grows, but a new approach based on evolving a variational autoregressive neural network provides an efficient way to track time evolution of the joint probability distribution for general reaction networks.

  • Ying Tang
  • Jiayu Weng
  • Pan Zhang

  Research16 Mar 2023 Nature Machine Intelligence

  Volume: 5, P: 376-385
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  Learning complex dependency structure of gene regulatory networks from high dimensional microarray data with Gaussian Bayesian networks

  • Catharina E. Graafland
  • José M. Gutiérrez

  ResearchOpen Access04 Nov 2022 Scientific Reports

  Volume: 12, P: 18704
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  Frequency spectra and the color of cellular noise

  The invention of the Fourier integral in the 19th century laid the foundation for modern spectral analysis methods. Here the authors develop frequency-based methods for analyzing the reaction mechanisms within living cells from distinctively noisy single-cell output trajectories and present forward engineering of synthetic oscillators and controllers.

  • Ankit Gupta
  • Mustafa Khammash

  ResearchOpen Access25 Jul 2022 Nature Communications

  Volume: 13, P: 4305
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  A scalable, open-source implementation of a large-scale mechanistic model for single cell proliferation and death signaling

  Mechanistic models of how single cells respond to different perturbations can help integrate disparate big data sets or predict response to varied drug combinations. Here the authors develop a scalable, open-source pipeline for constructing and simulating large-scale, single-cell mechanistic models, an important building block for clinically-predictive mechanistic models and interpretable big data integration.

  • Cemal Erdem
  • Arnab Mutsuddy
  • Marc R. Birtwistle

  ResearchOpen Access21 Jun 2022 Nature Communications

  Volume: 13, P: 3555