Massive computational acceleration by using neural networks to emulate mechanism-based biological models.

Published

Journal Article

For many biological applications, exploration of the massive parametric space of a mechanism-based model can impose a prohibitive computational demand. To overcome this limitation, we present a framework to improve computational efficiency by orders of magnitude. The key concept is to train a neural network using a limited number of simulations generated by a mechanistic model. This number is small enough such that the simulations can be completed in a short time frame but large enough to enable reliable training. The trained neural network can then be used to explore a much larger parametric space. We demonstrate this notion by training neural networks to predict pattern formation and stochastic gene expression. We further demonstrate that using an ensemble of neural networks enables the self-contained evaluation of the quality of each prediction. Our work can be a platform for fast parametric space screening of biological models with user defined objectives.

Full Text

Duke Authors

Cited Authors

  • Wang, S; Fan, K; Luo, N; Cao, Y; Wu, F; Zhang, C; Heller, KA; You, L

Published Date

  • September 25, 2019

Published In

Volume / Issue

  • 10 / 1

Start / End Page

  • 4354 -

PubMed ID

  • 31554788

Pubmed Central ID

  • 31554788

Electronic International Standard Serial Number (EISSN)

  • 2041-1723

International Standard Serial Number (ISSN)

  • 2041-1723

Digital Object Identifier (DOI)

  • 10.1038/s41467-019-12342-y

Language

  • eng