Deep Learning Approaches for Predictive Modeling and Optimization of Metabolic Fluxes in Engineered Microorganism

Abstract

Deep learning approaches have emerged as powerful tools for predictive modeling and optimization of metabolic fluxes in engineered microorganisms. These approaches leverage the capabilities of deep neural networks to capture complex patterns and relationships in large-scale biological datasets. This paper provides an overview of the deep learning techniques commonly employed in this field, including Deep Neural Networks (DNNs), Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), Generative Adversarial Networks (GANs), Reinforcement Learning (RL), and Transfer Learning. Each approach is briefly described, highlighting its potential applications in predicting and optimizing metabolic fluxes. The importance of data preprocessing, model architecture selection, and optimization techniques is also emphasized. The promising results obtained from these deep learning approaches suggest their potential to enhance metabolic engineering strategies and facilitate the design of more efficient and sustainable bioprocesses.