Synthetic biology has been successfully used to design biological systems with new and improved functions. However, due to the complexity of biological systems, performing synthetic biology in a quantitative and predictive manner still remains a challenge. In recent years, artificial intelligence (AI) and machine learning (ML) that allow computers to learn from experience has emerged […]
Synthetic biology has been successfully used to design biological systems with new and improved functions. However, due to the complexity of biological systems, performing synthetic biology in a quantitative and predictive manner still remains a challenge. In recent years, artificial intelligence (AI) and machine learning (ML) that allow computers to learn from experience has emerged as a potentially powerful tool to address this challenge.
A new Virtual Special Issue from ACS Synthetic Biology will focus on this dynamic topic, including contributions that develop and apply AI and ML tools for synthetic biology applications. The issue will be led by Editor-in-Chief Huimin Zhao with Guest Editors Hector Garcia-Martin and Stanislav Mazurenko.
Relevant topics include:
- AI/ML algorithms relevant to synthetic biology
- AI/ML-guided peptide, protein, and antibody engineering
- AI/ML-guided metabolic engineering
- AI/ML for plant, microbial, and mammalian synthetic biology
- AI/ML for bioprocess development
- AI/ML for systems biology
Author Instructions:
To submit your manuscript, please visit the ACS Synthetic Biology website. Please follow the normal procedures for manuscript submission, and when in the ACS Paragon Plus submission site, select the special issue of “AI for Synthetic Biology.” All manuscripts will undergo the normal peer review process. For additional submission instructions, please see the ACS Synthetic Biology Author Guidelines.
The deadline for submissions is March 31, 2023.
