Massive computational acceleration by using neural networks to emulate mechanism-based biological models
Abstract
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.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Sep 25, 2019
- Source ID
- 10.1038/s41467-019-12342-y
Entities
People
- Carolyn Zhang
- Feilun Wu
- Kai Fan
- Katherine A. Heller
- Lingchong You
- Nan Luo
- Shangying Wang
- Yangxiaolu Cao
Organizations
- David and Lucile Packard Foundation
- National Institutes of Health
- National Science Foundation
- Office of Naval Research