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Sagar Chakraborty

Researcher at Massachusetts Institute of Technology

Publications -  7
Citations -  490

Sagar Chakraborty is an academic researcher from Massachusetts Institute of Technology. The author has contributed to research in topics: Gene & Fibroin. The author has an hindex of 4, co-authored 5 publications receiving 404 citations. Previous affiliations of Sagar Chakraborty include Indian Institute of Technology Kharagpur.

Papers
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Engineering lipid overproduction in the oleaginous yeast Yarrowia lipolytica

TL;DR: The development of a microbial catalyst with the highest reported lipid yield, titer and productivity to date is described, an important step towards the development of an efficient and cost-effective process for biodiesel production from renewable resources.
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Integrated bioprocess for conversion of gaseous substrates to liquids.

TL;DR: The presented integrated system demonstrates the feasibility of substantial net fixation of carbon dioxide and conversion of gaseous feedstocks to lipids for biodiesel production and can be used for the economical conversion of waste gases from steel mills to valuable liquid fuels for transportation.
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Freeze-gelled silk fibroin protein scaffolds for potential applications in soft tissue engineering

TL;DR: This technique may be used as an alternative method for 3D scaffolds preparation and may also be utilized for tissue engineering applications.
Patent

Engineered microbes and methods for microbial oil production

TL;DR: In this article, a combination of a step generating metabolites, acetyl-CoA, ATP or NADPH for lipid synthesis, and a step sequestering a product or an intermediate of a lipid synthesis pathway that mediates feedback inhibition of lipid synthesis is described.
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Elucidating the functional roles of prokaryotic proteins using big data and artificial intelligence

TL;DR: In this article , the authors review the aims and methods of protein annotation and explain the different principles behind machine and deep learning algorithms including recent research examples, in order to assist both biologists wishing to apply AI tools in developing comprehensive genome annotations and computer scientists who want to contribute to this leading edge of biological research.