Data-Driven Design of Synthetic Niches for Precision Control of Microbiome Dynamics
91,373
2024-08-01 to 2025-07-31
Collaborative R&D
Matrix Bio proposes enhancing its integrated high-throughput hydrogel screening and machine learning platform to advance microbiome research. By focusing existing automation, characterization, and informatics capabilities on microbiome engineering, we can help overcome limitations in tools to selectively culture and study complex communities. Our automated workflow for rapid hydrogel formulation screening will be directed at identifying compositions tailored to support co-culture of human cells and microbiota. Molecular barcoding will enable tracking community dynamics during multiplexed experiments. Proprietary algorithms will discern patterns in the expansive datasets to uncover determinants of selective enrichment or inhibition for key species. Tailored hydrogels produced can better recapitulate microbiome-host interactions for applications from live biotherapeutics to improved disease models. This project expands Matrix Bio's leadership in converging automation, big data, and AI to accelerate materials discovery into the emerging microbiome sphere.
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