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

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Canada LLMs Explained - Aggregate Intellect - AI.SCIENCE
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Published on ● Video Link: https://www.youtube.com/watch?v=dmEOyFlro-o


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Duration: 31:24
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For slides and more information on the paper, visit https://aisc.ai.science/events/2020-03-31

Discussion lead: Shangying Wang
Discussion facilitator(s): Rouzbeh Afrasiabi

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.



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Tags:
neural networks
bilogical molecules
machine learning
deep learning