Machine Learning and Deep Learning in Synthetic Biology: Key Architectures, Applications, and Challenges

Abstract

Machine learning (ML), particularly deep learning (DL), has made rapid and substantial progress in synthetic biology in recent years. Biotechnological applications of biosystems, including pathways, enzymes, and whole cells, are being probed frequently with time. The intricacy and interconnectedness of biosystems make it challenging to design them with the desired properties. ML and DL have a synergy with synthetic biology. Synthetic biology can be employed to produce large data sets for training models (for instance, by utilizing DNA synthesis), and ML/DL models can be employed to inform design (for example, by generating new parts or advising unrivaled experiments to perform). This potential has recently been brought to light by research at the intersection of engineering biology and ML/DL through achievements like the design of novel biological components, best experimental design, automated analysis of microscopy data, protein structure prediction, and biomolecular implementations of ANNs (Artificial Neural Networks). I have divided this review into three sections. In the first section, I describe predictive potential and basics of ML along with myriad applications in synthetic biology, especially in engineering cells, activity of proteins, and metabolic pathways. In the second section, I describe fundamental DL architectures and their applications in synthetic biology. Finally, I describe different challenges causing hurdles in the progress of ML/DL and synthetic biology along with their solutions.

Multiplex CRISPR-Cas Genome Editing: Next-Generation Microbial Strain Engineering

“…A machine learning model identified the optimal combination of promoters and terminators for each gene in the heterologous violacein biosynthesis pathway, resulting in a 2.4-fold increase in productivity in S. cerevisiae . In addition to fine-tuning the details of biosynthetic pathways to identify the optimal combination, the optimization of metabolic flux analysis is also becoming feasible with AI technologies . With the application of multiplex genome editing technologies, these approaches could quickly determine which target genes to select and how to edit their sequences for high yield and productivity in metabolite production.…”

The Development and Opportunities of Predictive Biotechnology

“…The effective and sustainable utilization of data through methods like machine learning and deep learning still needs to be explored. [4,9,16,42,[63][64][65] This endeavor holds immense potential for reinterpreting and integrating stored data into new value chains. To make this possible, collaborative efforts are required to establish central databases, standardize data, develop universally linkable user interfaces, and clarify IP rights and usage costs.…”