Machine learning for synthetic gene circuit engineering

2025/01/27 by Sebastian Palacios, James J. Collins, James J Collins +1 · 1 voice · 3 citations
Biochemistry, Genetics and Molecular Biology · #CRISPR and Genetic Engineering #Gene Regulatory Network Analysis #Viral Infectious Diseases and Gene Expression in Insects

paper · doi:10.1016/j.copbio.2025.103263

openalex publication_date 2025/01/27 · crossref created 2025/01/27 · crossref issued 2025/04/01 · crossref published 2025/04/01 · crossref published-print 2025/04/01 · crossref deposited 2025/09/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/22 · crossref indexed 2026/07/31

Citations

• Auto-Encoding Variational Bayes
• ImageNet classification with deep convolutional neural networks
• Taking the Human Out of the Loop: A Review of Bayesian Optimization
• Denoising Diffusion Probabilistic Models
• Physics-informed machine learning
• Highly accurate protein structure prediction with AlphaFold
• A guide to machine learning for biologists
• De novo design of protein structure and function with RFdiffusion
• Accurate structure prediction of biomolecular interactions with AlphaFold 3
• Programming mammalian cell behaviors by physical cues
• Deep generative design of RNA aptamers using structural predictions
• Sequence modeling and design from molecular to genome scale with Evo
• Partitioning of a 2-bit hash function across 66 communicating cells

Cited by

• Modular Machine Learning with Applications to Genetic Circuit Composition
• A tool for modeling gene regulatory networks (GRN_modeler) and its applications to synthetic biology
• Opportunities for artificial intelligence and synthetic biology in designing living drug delivery systems

Discussions

• Machine learning for synthetic gene circuit engineering doi.org/10.1016/j.co... [bsky, 2 points, 0 comments]