- Predictive modeling for bioengineering and synthetic biology
- Enables accurate testing of biological computational and algorithmic tools
- Provides reliable synthetic biological data
Synthetic biology cannot yet fulfill its potential due to the inability to predict the behavior of biological systems. Computational tools can move the field forward by leveraging multiomics data to predict the outcome of bioengineering efforts.
The Omics Mock Generator (OMG) library, created by Berkeley Lab scientists, is used to provide synthetic multiomics data for testing computational tools for bioengineering metabolic models. Since experimental multiomics data is expensive to produce, OMG provides a simple and efficient way to produce large amounts of multiomics data. This data is both accessible and also biologically accurate such that it can be used to test algorithms and tools systematically.
Omics Mock Generator works by creating fluxes based on Flux Balance Analysis (FBA) and growth rate maximization, leveraging COBRApy. OMG is compatible with any genome-scale model. In order to obtain proteomics data, it can be assumed that the corresponding protein expression and gene transcription are linearly related to the fluxes, while the amount of metabolite present is assumed to be proportional to the sum of absolute fluxes coming in and out of the metabolite.
FOR MORE INFORMATION:
Multiomics Data Collection, Visualization, and Utilization for Guiding Metabolic Engineering
ART: A machine learning Automated Recommendation Tool for guiding synthetic biology 2020-011
- Somtirtha Roy
- Hector Garcia Martin
- Jose Manuel Marti
- Tijana Radivojevic
OPPORTUNITIES: Available /Open Source