CRISPR/CAS-BASED GENOME EDITING AND SYSTEMS BIOLOGY APPROACHES FOR ENGINEERING HIGH-PERFORMANCE BIOLOGICAL SYSTEMS

Abstract

The engineering of high-performance biological systems has been fundamentally transformed by the convergence of CRISPR/Cas-based genome editing and systems biology. This review examines the synergistic integration of precision genome editing technologies with computational modeling frameworks to enable predictive, iterative strain optimization. We trace the evolution of programmable nucleases from protein-guided ZFNs and TALENs to RNA-guided CRISPR/Cas9 and Cas12a systems, highlighting recent advances in double-strand break-free precision tools including base editors, prime editors, and recombinase-assisted large-cargo integration platforms. The Design-Build-Test-Learn (DBTL) cycle serves as the central operational framework, wherein genome-scale metabolic models, machine learning algorithms, and artificial intelligence guide target identification, experimental design, and data-driven model refinement. We examine industrial chassis engineering across Escherichia coli, Saccharomyces cerevisiae, Yarrowia lipolytica, and Corynebacterium glutamicum, documenting quantitative production benchmarks including β-thymidine (25.4 g/L), S-adenosyl-L-methionine (13.96 g/L), and ergothioneine (385.70 mg/L). Machine learning platforms such as OptiPrime and PRIDICT are discussed for their capacity to predict mismatch repair evasion and optimize prime editing guide RNA design. Critical bottlenecks including host DNA repair constraints, delivery vector payload limitations, and metabolic burden during scale-up are analyzed. Future directions emphasize MMR-evasive editing strategies, high-capacity adenoviral vectors, split-intein architectures, and biosensor-controlled dynamic regulation circuits that balance cellular maintenance with target product synthesis, establishing a predictive science of industrial strain optimization.