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      <namePart>Yilmaz, Denizhan</namePart>
   </name>
   <titleInfo>
      <title>KINETIC MODEL FRAMEWORKS OF ANIMAL CELL CULTURES FOR CONTROL AND OPTIMIZATION</title>
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   <originInfo>
      <dateCreated keyDate="yes">2019</dateCreated>
   </originInfo>
   <note displayLabel="Degree Awarded">Spring 2019</note>
   <typeOfResource authority="aat" valueURI="http://vocab.getty.edu/page/aat/300028029">Dissertation</typeOfResource>
   <name type="corporate">
      <affiliation>Illinois Institute of Technology</affiliation>
   </name>
   <name type="corporate">
      <namePart>ChBE / Chemical and Biological Engineering</namePart>
   </name>
   <name authority="wikidata" authorityURI="https://www.wikidata.org" valueURI="https://www.wikidata.org/wiki/Q68519713">
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      <namePart>Parulekar, Satish</namePart>
   </name>
   <subject>
      <topic>Bioengineering</topic>
   </subject>
   <subject>
      <topic>Antibody Production</topic>
   </subject>
   <subject>
      <topic>Cell Culture</topic>
   </subject>
   <subject>
      <topic>Fed-batch</topic>
   </subject>
   <subject>
      <topic>Kinetic Model</topic>
   </subject>
   <language>
      <languageTerm type="code" authority="rfc3066">en</languageTerm>
   </language>
   <abstract>This dissertation proposes four different kinetic model frameworks that havebeen developed for optimization and control of monoclonal antibody producing mammalian cell cultures to improve biopharmaceutical production by decreasing the costof trial and error experimentation. The developed models mainly describe the transient metabolic behavior of mammalian cell culture under different culture conditionsand predicts cell growth and death, cell metabolism, and monoclonal antibody synthesis, and production. These models are developed via ordinary differential equationsbased on the assumption of well-mixing reactor. All developed models were calibrated, and their predictive capabilities were tested with experimental reports published in the literature. Good agreement was obtained between model predictions and experimental data. The presented results illustrate that the developed models successfully describe and predict the transient behavior of mammalian cell cultures and can be a useful tool for biopharmaceutical production.</abstract>
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   <accessCondition type="restrictionOnAccess">Restricted Access</accessCondition>
   <note type="embargo">Embargo Until: 07/02/2020</note>
<identifier type="hdl">http://hdl.handle.net/10560/islandora:1000866</identifier></mods>