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Aldis Darzins

Researcher at Promega

Publications -  60
Citations -  10338

Aldis Darzins is an academic researcher from Promega. The author has contributed to research in topics: Mutant & Hydrolase. The author has an hindex of 34, co-authored 60 publications receiving 9456 citations. Previous affiliations of Aldis Darzins include University of Illinois at Chicago & Ohio State University.

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Microalgal triacylglycerols as feedstocks for biofuel production: perspectives and advances

TL;DR: A brief summary of the current knowledge on oleaginous algae and their fatty acid and TAG biosynthesis, algal model systems and genomic approaches to a better understanding of TAG production, and a historical perspective and path forward for microalgae-based biofuel research and commercialization are provided.
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HaloTag: A Novel Protein Labeling Technology for Cell Imaging and Protein Analysis

TL;DR: The utility of this modular protein tagging system for cellular imaging and protein immobilization is demonstrated by analyzing multiple molecular processes associated with NF-kappaB-mediated cellular physiology, including imaging of subcellular protein translocation and capture of protein--protein and protein--DNA complexes.
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Genetic Engineering of Algae for Enhanced Biofuel Production

TL;DR: Potential avenues of genetic engineering that may be undertaken in order to improve microalgae as a biofuel platform for the production of biohydrogen, starch-derived alcohols, diesel fuel surrogates, and/or alkanes are focused on.
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The promise and challenges of microalgal‐derived biofuels

TL;DR: A brief overview of past algal research sponsored by the US Department of Energy and a discussion of the technical and economic barriers that need to be overcome before production of microalgal-derived diesel-fuel substitutes can become a large-scale commercial reality can be found in this article.
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Theoretical Maximum Algal Oil Production

TL;DR: In this paper, the authors present a theoretical approach to calculate an absolute upper limit to algal production based on physical laws and assumptions of perfect efficiencies, and present a best case approach that represents an optimistic target for production with realistic efficiencies.