APPLICATIONS OF TECHNOLOGY:
- Genetic engineering
ADVANTAGES:
- Outperforms kinetic models to predict pathway dynamics in bioengineered systems
ABSTRACT:
Hector Garcia Martin and Zac Costello of Berkeley Lab’s Joint BioEnergy Institute (JBEI) have determined that the combination of machine learning and abundant multiomics data (proteomics and metabolomics) can be used to effectively predict pathway dynamics in an automated fashion. The new method outperforms a classical kinetic model, and produces qualitative and quantitative predictions that can be used to productively guide bioengineering efforts. This method systematically leverages arbitrary amounts of new data to improve predictions, and does not assume any particular interactions, but rather implicitly chooses the most predictive ones.
For details about this technology, go to the researchers’ paper in npj Systems Biology and Applications.
New synthetic biology capabilities hold the promise of dramatically improving our ability to engineer biological systems. However, a fundamental hurdle in realizing this potential is our inability to accurately predict biological behavior after modifying the corresponding genotype. Kinetic models have traditionally been used to predict pathway dynamics in bioengineered systems, but they take significant time to develop, and rely heavily on domain expertise.
DEVELOPMENT STAGE: Proven principle.
STATUS: Patent pending. Available for licensing or collaborative research.
FOR MORE INFORMATION:
Costello, Z., Martin, H. G. “A machine learning approach to predict metabolic pathway dynamics from time-series multiomics data,” npj Systems Biology and Applications, 4, article number 19, 2018.