D
Dennis Vitkup
Researcher at Columbia University
Publications - 68
Citations - 7524
Dennis Vitkup is an academic researcher from Columbia University. The author has contributed to research in topics: Metabolic network & Gene. The author has an hindex of 36, co-authored 65 publications receiving 6902 citations. Previous affiliations of Dennis Vitkup include Harvard University & Massachusetts Institute of Technology.
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Analysis of optimality in natural and perturbed metabolic networks
TL;DR: The method of minimization of metabolic adjustment (MOMA), whereby the hypothesis that knockout metabolic fluxes undergo a minimal redistribution with respect to the flux configuration of the wild type is tested, is tested and found to be useful in understanding the evolutionary optimization of metabolism.
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Rare De Novo Variants Associated with Autism Implicate a Large Functional Network of Genes Involved in Formation and Function of Synapses
TL;DR: This study uses NETBAG to identify a large biological network of genes affected by rare de novo CNVs in autism, which is strongly related to genes previously implicated in autism and intellectual disability phenotypes.
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Tissue of origin dictates branched-chain amino acid metabolism in mutant Kras-driven cancers
Jared R. Mayers,Margaret E. Torrence,Laura V. Danai,Thales Papagiannakopoulos,Shawn M. Davidson,Matthew R. Bauer,Allison N. Lau,Brian W. Ji,Purushottam D. Dixit,Aaron M. Hosios,Alexander Muir,Christopher R. Chin,Elizaveta Freinkman,Tyler Jacks,Brian M. Wolpin,Dennis Vitkup,Matthew G. Vander Heiden +16 more
TL;DR: In this article, the authors argue that tissue of origin is an important determinant of how cancers satisfy their metabolic requirements, and that tissue context defines cancer dependence on specific metabolic pathways is unknown, but despite the same initiating events, these tumors use branched chain amino acids (BCAAs) differently.
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Solvent mobility and the protein 'glass' transition.
TL;DR: A novel molecular dynamics simulation procedure with the protein and solvent at different temperatures has been used, showing the essential role of solvent in controlling functionally important protein fluctuations above 180 K.
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Completeness in structural genomics.
TL;DR: This work evaluates different strategies for optimizing information return on effort and concludes that the strategy that maximizes structural coverage requires about seven times fewer structure determinations compared with the strategy in which targets are selected at random.