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Abstraction of Markov Population Dynamics via Generative Adversarial Nets

  • 2021
  • OriginalPaper
  • Chapter
Published in:
Authors
Francesca Cairoli
Ginevra Carbone
Luca Bortolussi
Published in
Computational Methods in Systems Biology
Publisher
Springer International Publishing

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Abstract

The chapter introduces a GAN-based method for abstracting stochastic simulations of chemical reaction networks, aiming to reduce computational costs and improve efficiency. It leverages Wasserstein GANs with gradient penalty (cWCGAN-GP) to learn an abstract probabilistic model that generates approximate trajectories in constant time, independent of the system's complexity. The approach is validated through case studies involving various models, such as the SIR epidemiological model, genetic toggle switch, oscillator, and MAPK cascade. The results demonstrate significant computational gains and accurate trajectory generation, even for complex systems. The method's flexibility in handling partial observability and its potential for active learning strategies are also highlighted, making it a promising tool for multi-scale simulations and statistical model checking.
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previous chapter Reducing Boolean Networks with Backward Boolean Equivalence
next chapter Greening R. Thomas’ Framework with Environment Variables: A Divide and Conquer Approach
Title
Abstraction of Markov Population Dynamics via Generative Adversarial Nets
Authors
Francesca Cairoli
Ginevra Carbone
Luca Bortolussi
Copyright Year
2021
Book
Computational Methods in Systems Biology

Print ISBN: 978-3-030-85632-8

Electronic ISBN: 978-3-030-85633-5

Copyright Year: 2021

DOI
https://doi.org/10.1007/978-3-030-85633-5_2
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