Design and execute simultaneous multi-gene editing strategies with our ML-optimized multiplex genome editing platform. Accelerate pathway engineering, combinatorial genetics, and therapeutic development through intelligent gRNA combination design.
Traditional multiplex editing relies on trial-and-error combinations of gRNAs with limited predictive power. Our AI platform analyzes thousands of potential gRNA combinations to identify optimal sets that maximize editing efficiency while minimizing off-target effects and cross-target interference.
Our ML models optimize gRNA combinations by predicting on-target efficiency, off-target potential, and cross-interaction effects between multiple editing targets simultaneously.
Machine learning models trained on large-scale multiplex editing datasets predict the success rate of each gRNA combination and suggest optimal delivery methods for your system.
We analyze genetic interactions between targets to identify synergistic editing combinations that produce desired phenotypes more effectively than single-target approaches.
Comprehensive AI-powered multiplex genome editing services for research, industrial, and therapeutic applications.
Our AI platform designs optimized gRNA sets for simultaneous targeting of 2-10+ genomic loci. ML models predict combination efficiency and minimize off-target interactions.
Design pooled or arrayed combinatorial CRISPR screens with optimal gRNA library composition. We provide statistical power analysis and hit validation strategies.
Combine multiple base editors (CBEs, ABEs) and Cas variants (SpCas9, SaCas9) for complex multi-type editing in a single step with enhanced precision and efficiency.
A streamlined process from target specification to validated multiplex editing results.
We analyze your target genes, desired editing outcomes, and organism system to define optimal multiplex editing parameters.
Our ML models evaluate thousands of gRNA combinations, predicting efficiency, off-target potential, and synergistic effects for optimal selection.
We design experimental validation including NGS analysis, phenotype screening, and off-target verification for your multiplex edited cells.
Optimized delivery of multiplex editing components using RNP, plasmid, or viral systems based on your cell type and editing requirements.
Our multiplex genome editing platform supports diverse applications from basic research to therapeutic development.
Multiplex editing for complex disease modeling, combination target validation, and therapeutic gene circuits. Generate precise tumor models and disease-relevant phenotypes.
Simultaneously edit multiple genes in metabolic pathways to optimize production of high-value compounds, biofuels, and pharmaceuticals in microbial hosts.
Perform combinatorial genetic screens to identify synergistic gene interactions, epistasis analysis, and complex trait dissection in various organisms.
Our multiplex genome editing platform builds upon peer-reviewed research in CRISPR combinatorial design and machine learning optimization.
Chen, L., Liu, G., & Zhang, T. (2024). Integrating machine learning and genome editing for crop improvement. aBIOTECH, 5(3), 262-277.
aBIOTECH | DOI: 10.1007/s42994-023-00133-5Campenhout, C. V., Golsteyn, R. M., & van der H. J. (2022). Covering the combinatorial design space of multiplex CRISPR/Cas experiments in plants. Frontiers in Plant Science, 13, 907095.
Frontiers in Plant Science | DOI: 10.3389/fpls.2022.907095Beurn, R., Rane, D., & Chatterjee, A. (2023). CRISPR-broad: Combined design of multi-targeting gRNAs and broad, multiplex target finding. Scientific Reports, 13(1), 18241.
Scientific Reports | DOI: 10.1038/s41598-023-46212-xCheng, X., Li, Z., Shan, R., Li, Z., Wang, S., Zhao, W., Zhang, H., Chao, L., Peng, J., Fei, T., & Li, W. (2023). Modeling CRISPR-Cas13d on-target and off-target effects using machine learning approaches. Nature Communications, 14, 752.
Nature Communications | PubMed: PMID: 36765063Common questions about our multiplex genome editing services.
The Enginoma platform supports simultaneous editing of 2-10+ targets depending on the complexity. We optimize gRNA combinations, delivery methods, and editing conditions to maximize multiplexed editing efficiency while minimizing off-target effects.
We support multiplex editing in mammalian cells (HEK293, CHO, primary cells), microbial systems (E. coli, yeast), and plant systems. Each system has optimized protocols for multiplex Cas delivery and selection.
Our ML models predict optimal gRNA combinations by considering on-target efficiency, off-target potential, positional effects, and interactions between multiple editing events. This reduces experimental iterations and improves multiplexed editing outcomes.
Yes. We provide complete solutions for pooled and arrayed combinatorial CRISPR screens, including gRNA library design, statistical power analysis, and hit validation strategies for identifying genetic interactions.
Yes. We support simultaneous use of multiple base editors (CBEs, ABEs) and Cas variants (SpCas9, SaCas9) for complex multi-type editing in a single step, enabling precise tumor model generation and pathway engineering.
Contact our team to discuss your multiplex genome editing requirements. We'll provide a customized proposal for your project.
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