Engineer sophisticated synthetic gene circuits with our AI-powered platform. From Boolean logic gates to complex regulatory networks, we enable predictive design of genetic control systems for diverse synthetic biology applications.
Traditional circuit design relies on trial-and-error experimentation due to complex host-context interactions. Our AI platform predicts circuit behavior before wet lab implementation, dramatically reducing development cycles.
Machine learning models trained on part characterization data predict circuit behavior with high accuracy. Move from specifications to validated designs faster.
Design circuits with sophisticated Boolean logic including AND, OR, NOT, NAND, NOR, XOR gates. Implement feedback loops, oscillators, and memory devices.
AI-optimized part selection minimizes parasitic interactions. Achieve orthogonality across regulatory layers for robust circuit operation in complex cellular environments.
Design circuits for bacterial, yeast, and mammalian systems. Our platform accounts for host-specific context effects and regulatory architecture differences.
From specification to validated circuit design, our platform covers the complete workflow.
Implement digital logic principles in living cells. Design combinational and sequential logic circuits with verifiable truth tables and timing characteristics.
Design complex gene regulatory networks with predictive mathematical models. Simulate transcription factor dynamics and CRISPR-based regulation systems.
AI-guided selection of promoters, RBS sequences, terminators, and coding domains. Optimize part combinations for maximum orthogonality and minimal burden.
Streamlined process from specification to validated circuit design.
Define circuit behavior, input/output specifications, and performance criteria. Specify host organism and delivery method.
Construct Boolean logic network, optimize gate topology, and verify truth tables against specifications.
AI-optimized selection of promoters, RBS, coding sequences, and terminators for each circuit component.
Dynamic simulation, parameter optimization, and experimental roadmap generation for wet lab validation.
Comprehensive synthetic biology solutions for your circuit engineering needs.
Design of combinational and sequential logic circuits with verified truth tables. Custom logic gate implementation.
Engineering of genetic biosensors for metabolite, pathogen, or environmental signal detection with defined dynamic range.
Cell-type-specific gene expression circuits for therapeutic applications. AND-gated designs for precision medicine.
Our gene circuit design services support diverse synthetic biology applications.
Dynamic regulatory circuits for optimized metabolic flux. Implement feedback control systems that regulate pathway expression based on metabolite levels.
CRISPR-based gene circuits with programmable transcriptional control. Design dCas9 logic gates and CRISPR interference networks with high specificity.
Synthetic oscillators and genetic timers for sequential gene expression. Design repressilator circuits and delayed feedback systems with precise timing.
Genetic memory circuits that record cellular events. Design toggle switches and hysteresis circuits for bistable state storage and signal history.
Robust biosensors for industrial process monitoring and quality control. Engineer circuits with extended dynamic range and environmental stability.
Cellular biosensors for disease detection and biomarker monitoring. Design circuits with fast response times and high sensitivity.
Our platform builds upon peer-reviewed research in machine learning for synthetic gene circuit engineering.
Palacios, S., Collins, J. J., & Del Vecchio, D. (2025). Machine learning for synthetic gene circuit engineering. Current Opinion in Biotechnology, 92, 103263.
Current Opinion in Biotechnology | PubMed: PMID: 39874719Anzalone, A. V., Gao, X. D., Liu, D. R., et al. (2022). Programmable deletion, insertion, and modification of the human genome using prime editing. Nature Biotechnology, 40(5), 731-740.
Nature Biotechnology | DOI: 10.1038/s41587-021-01124-xWang, X., Chen, Y., & Zhao, H. (2023). Machine learning methods for synthetic biology: Applications and perspectives. Current Opinion in Biotechnology, 79, 102872.
Current Opinion in Biotechnology | DOI: 10.1016/j.copbio.2022.102872Bashor, C. J., Patel, N., Chhabra, S., et al. (2020). Signal processing by synthetic gene circuits: Design and analysis of feedback systems. ACS Synthetic Biology, 9(5), 1048-1060.
ACS Synthetic Biology | DOI: 10.1021/acssynbio.0c00097Common questions about our gene circuit design services.
We design a wide range of synthetic gene circuits including Boolean logic gates (AND, OR, NOT, NAND, NOR, XOR), feedback control systems, oscillators, toggle switches, memory devices, and biosensors. The Enginoma platform supports both bacterial and mammalian systems.
We use machine learning models trained on part characterization data to predict circuit behavior before wet lab implementation. Our models account for context dependence, resource loading, and host interactions to reduce experimental iterations by up to 80%.
Yes. We support mammalian synthetic biology with designs for CRISPR-based circuits, zinc-finger transcription factor circuits, and optogenetic control systems. Our models incorporate chromatin context and epigenetic effects.
We design circuits for plasmid transfection, lentiviral/AAV delivery, and stable cell line integration. Our platform optimizes part selection and expression levels for each delivery method.
Yes. We offer experimental validation including flow cytometry, live-cell imaging, and reporter assays to verify circuit behavior. We provide quantitative performance metrics and optimization recommendations.
Contact our team to discuss your synthetic biology project. We'll provide customized circuit design recommendations.
Tell us about your project and we'll get back within 24 hours.
