Biofuel Market to Experience Boom as Researchers Devise a Method for Producing an Acid that can help increase Production of Biofuel
Posted On November 24, 2021
Fossil fuels pose a significant environmental threat. Stopping their use and replacing it with biofuels, on the other hand, is a difficult task. This is mainly because most parts of our societies have been developed keeping in mind properties of fossil fuels such as energy grids, manufacturing processes of some textile and other products. This denotes that transitioning from fossil fuels to any green energy would involve colossal costs and challenges.
A new study might help overcome these challenges. Researchers have developed a technique to design yeast to make itaconic acid, a significant commodity chemical, using data integration and supercomputing power as a guide. The devised way might benefit the Biofuel Market immensely as it can be an economically attractive replacement for fossil fuels.
Itaconic acid could be a prospective renewable chemical building block. It may be able to replace some fossil-fuel-derived products. The researchers set out to create itaconic acid using microbes at a low cost because they perceived its promise as a petrochemical alternative.
The scientists examined this profile using machine learning to determine which nonessential genes might be eliminated from the yeast and which useful ones could be added to improve itaconic acid synthesis.
After that, they worked towards selecting genes to "create" the organism. They developed various yeast versions where they added genes in some versions while removing some in other versions. This decision was taken based on computational predictions. The team then tested all the different types of yeast to check if carbon flow around the itaconic acid production pathway experienced any effects.
The machine learning analysis received from RNA sequencing data demonstrated that the computational predictions were similar to the outcomes of the experiments. Furthermore, the team added that detailed gene predictions for future analysis were also similar.
The current research is in its initial phases. Nonetheless, it holds tremendous potential. This is because it demonstrates how machine learning and causal inference may be used to create new ways of looking at the complicated cell system found in yeast. Much testing is required for this model; nonetheless, the work shows potential to expand this bioengineering led by computation to more systems. The strategy might open a new era of biosystem designs for the creation of eco-friendly chemicals.