Epidemic inference through generative neural networks

Biazzo, Indaco; Braunstein, Alfredo; Dall’Asta, Luca; Mazza, Fabio

doi:10.48550/arxiv.2111.03383

Cited by 4 publications

(5 citation statements)

References 15 publications

(17 reference statements)

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“…1). The VAN has been applied to statistical physics [38,39], quantum-many body systems [40][41][42], open quantum systems [43,44], and computational biology [45,46], where it enables directly sampling configurations, computing the normalized probability of configurations, and estimating macroscopic thermodynamical quantities including the free energy and entropy. Here, we extend the VAN to characterize the joint probability distribution of species counts for the stochastic reaction network.…”

Section: Connected Configurationsmentioning

confidence: 99%

Neural-network solutions to stochastic reaction networks

2023

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Section: Connected Configurationsmentioning

confidence: 99%

Neural-network solutions to stochastic reaction networks

2023

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Section: Connected Configurationsmentioning

confidence: 99%

Neural-network solutions to stochastic reaction networks

Tang

Weng

Zhang

2022

Preprint

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The stochastic reaction network is widely used to model stochastic processes in physics, chemistry and biology. However, the size of the state space increases exponentially with the number of species, making it challenging to investigate the time evolution of the chemical master equation for the reaction network. Here, we propose a machine-learning approach using the variational autoregressive network to solve the chemical master equation. The approach is based on the reinforcement learning framework and does not require any data simulated in prior by another method. Different from simulating single trajectories, the proposed approach tracks the time evolution of the joint probability distribution in the state space of species counts, and supports direct sampling of configurations and computing their normalized joint probabilities. We apply the approach to various systems in physics and biology, and demonstrate that it accurately generates the probability distribution over time in the genetic toggle switch, the early life self-replicator, the epidemic model and the intracellular signaling cascade. The variational autoregressive network exhibits a plasticity in representing the multimodal distribution by feedback regulations, cooperates with the conservation law, enables time-dependent reaction rates, and is efficient for high-dimensional reaction networks with allowing a flexible upper count limit. The results suggest a general approach to investigate stochastic reaction networks based on modern machine learning.

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“…1). The VAN has been applied to the statistical physics [38,39], quantum-many body systems [40][41][42], open quantum system [43,44], and computational biology [45,46], where it enables to directly sample configurations, compute the normalized probability of configurations, and estimate macroscopic thermodynamical quantities including the free energy and entropy. Here, we extend the VAN to characterize the joint probability distribution of species counts for the stochastic reaction network.…”

Section: Chemical Master Equationmentioning

confidence: 99%