Stämm introduces its Multi-Omic Network Atlas (MoNA), an AI-powered transomics platform that closes the loop from bio to digital accelerating bio-innovation cycles.
Cell and gene therapies face significant challenges due to the uncertainty of wet lab assays. To reduce this uncertainty while saving time and lab resources, Stämm developed MoNA. The technology maps biomolecular expression profiles of cell samples by combining multi-omic data (such as transcriptomics and proteomics, among others) with environmental or protocol conditions.
This approach bridges experimental research with computational biology pipelines, enabling more accurate decision-making and interpretation in wet lab assays. With MoNA, Stämm completes the cycle of R&D bioengineering, biomanufacturing, and now in-silico cell models, providing valuable feedback for scientists.
Combining generative machine learning, bioinformatics pipelines, and multi-omic databases, the platform models biomolecular interactions and cell behavior under various protocols and conditions. This in-silico approach reduces brute-force trial and error by understanding the relation between multi-omic expression profiles and their experimental conditions.
The software streamlines research time by enhancing scientists’ decision-making accuracy. By bridging wet lab and computational cell representations, it creates a closed-loop system that continuously refines itself, optimizing outcomes for researchers in multiple fields, such as biomedicine, clinical trials, and cell line development.
The atlas is beneficial for cell therapy applications, based on results in interpreting wet lab assays for reprogrammed IPSCs. MoNA refines treatment development by ranking cell candidates for the target tissue. Beyond that, it serves as a co-pilot to establish the best parameters for cell and gene therapy biomanufacturing.
Contact Stämm for a demo and co-development opportunities.