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

Area of Science:

• Computational Biology
• Systems Biology
• Bioinformatics

Background:

• Mechanism-based models in biology are essential but computationally intensive.
• Exploring large parametric spaces is a significant bottleneck for biological applications.

Purpose of the Study:

• To develop a computational framework for significantly enhancing the efficiency of biological model exploration.
• To enable rapid screening of biological model parameters for user-defined objectives.

Main Methods:

• Training neural networks on a limited set of simulations from a mechanistic model.
• Utilizing the trained neural network to predict outcomes across a much larger parametric space.
• Employing an ensemble of neural networks for self-contained prediction quality evaluation.

Main Results:

• Demonstrated orders-of-magnitude improvement in computational efficiency.
• Successfully trained neural networks to predict pattern formation and stochastic gene expression.
• Validated the ensemble approach for reliable assessment of prediction accuracy.

Conclusions:

• The proposed framework offers a powerful platform for accelerating parametric space screening in biological modeling.
• This approach significantly reduces computational demands, making complex biological systems more accessible for analysis.
• Ensemble neural networks provide a robust method for evaluating prediction quality in computational biology.