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Dimitris Papamichail

Researcher at The College of New Jersey

Publications -  36
Citations -  1719

Dimitris Papamichail is an academic researcher from The College of New Jersey. The author has contributed to research in topics: Codon usage bias & Computer science. The author has an hindex of 9, co-authored 33 publications receiving 1570 citations. Previous affiliations of Dimitris Papamichail include Brookhaven National Laboratory & State University of New York System.

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Virus attenuation by genome-scale changes in codon pair bias.

TL;DR: De novo large DNA molecules are synthesized using hundreds of over-or underrepresented synonymous codon pairs to encode the poliovirus capsid protein and polioviruses containing such amino acid–independent changes were attenuated in mice.
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Reduction of the Rate of Poliovirus Protein Synthesis through Large-Scale Codon Deoptimization Causes Attenuation of Viral Virulence by Lowering Specific Infectivity

TL;DR: Direct analysis indicated a striking reduction of the specific infectivity of PV-AB-type virus particles, suggesting that codon-deoptimized viruses should have genetically stable phenotypes, and they may prove suitable as attenuated substrates for the production of poliovirus vaccines.
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Live attenuated influenza virus vaccines by computer-aided rational design

TL;DR: The previously described synthetic attenuated virus engineering (SAVE) approach to influenza virus strain A/PR/8/34 was applied to rationally design live attenuated influenza virus vaccine candidates through genome-scale changes in codon-pair bias.
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Elevated atmospheric CO2 affects soil microbial diversity associated with trembling aspen.

TL;DR: Changes in soil biota are evidence for altered interactions between trembling aspen and the microorganisms in its surrounding soil, and support the theory that greater plant detritus production under elevated CO(2) significantly alters soil microbial community composition.
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Computational Tools and Algorithms for Designing Customized Synthetic Genes

TL;DR: This review studies some of the algorithmic issues behind gene optimization and the approaches that different tools have adopted to redesign genes and optimize desired coding features and utilizes test cases to demonstrate the efficiency of each approach, as well as identify their strengths and limitations.