Machine Learning For Deeper Understanding Of Biological Metabolisms
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Machine Learning For Deeper Understanding Of Biological Metabolisms

For a better in-depth understanding of enzyme kinetics & other complex metabolic processes, Machine Learning is being used by researchers from Heinrich Heine University Düsseldorf and the University of California. The study was published in current issue of Nature Communications Journal.

Synthetic biology has been a trending topic of discussion last year. In order to explore further in this field, to understand complex systems in biological cells a detailed knowledge is required. Researchers understand Biological metabolisms, their process of function, that there are thousands of enzymes involved but key details such as the role of each enzyme in the process are very little understood.

Dr. David Heckmann, a former doctoral researcher under Professor Martin Lercher at HHU’s Institute for Computational Cell Biology along with his fellow researchers have turned towards bioinformatics to reach the bottom level of how each enzyme’s function, their composition & properties. Machine learning, a sub-field of artificial intelligence (AI) is being employed by the team to understand each component of biological metabolism.

Via machine learning the team was able to report enzyme properties that are deciding factors for their activity. They could further analyze and describe the kinetics of a large number of enzymes, which was never

possible to date.

Machine Learning has been implemented in several fields like Traffic management, Railway management, Gaming and many more. Its utilization in cell biology will definitely broaden the horizons of research.

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