Provides an in-silico platform for designing, simulating, and optimizing synthetic biology projects, including genetic circuits and metabolic pathways. The platform integrates tools like Flux Balance Analysis, LCMS data processing, and pathway visualization to improve precision, reduce costs, and accelerate discoveries in metabolomics and biotechnology research.
Funding
Funding not disclosed
Founders
Product
Problem
Synthetic biology and metabolomics research often relies on iterative, resource-intensive lab experiments to design and optimize genetic circuits and metabolic pathways. This trial-and-error approach leads to increased costs, slower discovery, and difficulty in predicting system behavior.
Solution
Metastate Bio offers an in-silico platform that enables researchers to design, simulate, and optimize synthetic biology projects, including genetic circuits and metabolic pathways. The platform integrates tools for Flux Balance Analysis (FBA), LCMS data processing, and pathway visualization, allowing users to create and refine virtual models of microbial systems. By simulating different inputs, environmental conditions, and genetic configurations, Metastate Bio helps researchers predict optimal production conditions, minimize byproducts, and identify bottlenecks before committing to lab experiments. This approach reduces the need for repeated wet-lab experiments, accelerates the discovery process, and facilitates more cost-effective research.
Target Audience
Metastate Bio targets researchers in precision fermentation, pharma, synthetic biology, industrial biotech, and academic research who seek to optimize bioprocesses and accelerate innovation.
Features
- In-silico modeling of metabolic pathways to predict optimal production conditions.
- Flux Balance Analysis (FBA) for precision fermentation and cellular research.
- Integration with Genome Scale Modeling.
- LCMS data processing for targeted and untargeted metabolomics analysis.
- Pathway visualization from databases such as HMDB, BIGG, and KEGG.
- Model fitting with Genetic Algorithms to refine metabolic models.
- Dynamic modeling to integrate factors like temperature and pH.
- Tools for streamlining research documentation and enabling data sharing within teams.