Genenet Technology develops proprietary gene circuit designs by integrating deep learning algorithms to mimic artificial neural networks. This novel biocomputing method enables advanced control over gene expression for applications in synthetic biology and bioproduction. The company provides genetic circuit design services, R&D consultation, and technology licensing for drug discovery, cell and gene therapy, and organoid-on-chip research.
Funding
Funding not disclosed
Founders
Product
Problem
Designing and optimizing genetic circuits and cell‑based bioproduction processes requires extensive experimental iteration, specialized expertise, and costly laboratory infrastructure, limiting speed and accessibility for biotech developers.
Solution
GeneNet offers an AI‑powered bioinformatics platform that automates the design, simulation, and optimization of genetic circuits for cell factories. The platform integrates predictive models trained on large‑scale omics and bioprocess data to suggest circuit architectures that maximize product yield and stability. GeneNet also provides organ‑on‑chip hardware that enables rapid, high‑throughput screening of engineered cells in physiologically relevant microenvironments, reducing the need for large‑scale wet‑lab experiments. Results from the chip assays are fed back into the AI engine, creating a closed‑loop workflow that accelerates design‑build‑test cycles. The solution is delivered via a cloud‑based interface with collaborative project workspaces, allowing teams to share designs, data, and analytics securely.
Target Audience
Primary customers are biotech startups, pharmaceutical R&D labs, and academic research groups focused on synthetic biology, cell‑based therapeutics, and industrial biomanufacturing.
Features
- AI-driven genetic circuit design engine that predicts expression levels, metabolic burden, and stability across host cell lines
- Integrated simulation tools for in silico testing of circuit performance before wet‑lab implementation
- Modular organ‑on‑chip platform for real‑time monitoring of cell metabolism, viability, and product secretion in microphysiological conditions
- Automated data capture and analytics pipeline that feeds experimental results back into the AI model for continuous learning
- Cloud‑based collaborative workspace with version control, secure data sharing, and API access for downstream process tools
- Compatibility with common bioproduction hosts such as CHO and yeast, supporting both small‑molecule and biologics production workflows