Research Articles
Knowledge-Driven DBTL Cycles: Unlocking Mechanistic Insights for Accelerated Biomanufacturing and Drug Discovery
This article explores the transformative impact of knowledge-driven Design-Build-Test-Learn (DBTL) cycles in synthetic biology and biopharmaceutical development.
Closed-Loop DBTL Platforms: How AI Agents Are Revolutionizing Drug Discovery
This article explores the transformative integration of AI agents into closed-loop Design-Build-Test-Learn (DBTL) platforms for drug discovery.
Streamlining Biomanufacturing: A Guide to DoE-Driven Library Reduction in DBTL Cycles
This article explores the strategic integration of Design of Experiments (DoE) to efficiently reduce the combinatorial library size in Design-Build-Test-Learn (DBTL) cycles for biomedical research and drug development.
Combinatorial Pathway Optimization: Mastering DBTL Cycles for Advanced Therapeutics and Bioproduction
This article provides a comprehensive exploration of combinatorial pathway optimization through the lens of the Design-Build-Test-Learn (DBTL) cycle, a foundational framework in synthetic biology and precision medicine.
AI-Driven DBTL Cycles: Accelerating Synthetic Biology and Biomanufacturing with Machine Learning
This article explores the transformative integration of machine learning (ML) into the Design-Build-Test-Learn (DBTL) cycle, a core framework in synthetic biology and metabolic engineering.
Automated DBTL Pipelines: Accelerating Microbial Production of Fine Chemicals and Therapeutics
This article explores the transformative role of automated Design-Build-Test-Learn (DBTL) pipelines in synthetic biology for the microbial production of fine chemicals and pharmaceutical precursors.
AI-Driven DBTL Cycle: Accelerating Precision Biological Engineering and Drug Discovery
This article explores the transformative role of the Design-Build-Test-Learn (DBTL) cycle in modern biological engineering.
LDBT vs. DBTL: How Machine Learning and Cell-Free Systems Are Reshaping Synthetic Biology
This article provides a comparative analysis for researchers and drug development professionals on the evolving engineering cycles in synthetic biology.
AI-Powered DBTL Cycles: Accelerating Systems Metabolic Engineering for Next-Generation Therapeutics
This article provides a comprehensive introduction to the Design-Build-Test-Learn (DBTL) cycle, a foundational framework in modern systems metabolic engineering.
The DBTL Framework in Metabolic Engineering: A Guide to Iterative Strain Development and AI-Driven Optimization
This article provides a comprehensive overview of the Design-Build-Test-Learn (DBTL) framework, a cornerstone methodology in metabolic engineering and synthetic biology for developing efficient microbial cell factories.