A. James Clark School of Engineering

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Author: search.filters.author.Zargar, Amin

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    Investigations into interkingdom signaling and quorum sensing phenotypes
    (2015) Zargar, Amin; Bentley, William E; Bioengineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Bacteria secrete and recognize communication molecules to coordinate gene expression in a process known as quorum sensing (QS). Through coordinated expression, bacteria are able to influence phenotypic changes on a larger population scale, such as biofilm formation. Recent studies into interkingdom communication have found cross-talk communication among bacteria and eukarya as well, which has been shown to influence actions pathogenicity and inflammation, among others. In this work, we developed E. coli ‘controller cells’ that guide and attenuate harmful bacterial QS phenotypes coordinated by the QS molecule autoinducer-2 (AI-2), as well as further the understanding of the interkingdom effects of these bacterial secretions (secretome) on human cells, particularly intestinal epithelial cells (IECs) that line the GI tract. Extending beyond natural networks, these ‘controller cells’ provide a useful tool in metabolic engineering, as synthetic biologists have incorporated QS networks to create sophisticated genetic circuits.
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    SEQUENCE MODELING OF RAFT POLYMERIZATIONS WITH THE METHOD OF MOMENTS
    (2008-10-13) Zargar, Amin; Schork, Joseph; Chemical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Attempts to model the sequence structure of copolymers consisted of probabilistic functions that were incomplete and inaccurate. A novel technique to track sequence parameters is developed that determines not only copolymer composition, but sequence distribution as well. RAFT polymerizations are simulated with two independent and concurrent models to track MWD, conversion, copolymer composition, and sequence characteristics. Batch polymerizations are simulated with varying reactor conditions as a proof-of-concept to illustrate the power of the sequence model to track the composition of the polymer. Series of CSTR and PFR reactors with varying reactor conditions are then presented as applications to iteratively fine-tune copolymers with predetermined sequence and compositional structure.