Model-based optimal experimental design for biological systems

Sinkoe, Andrew
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Hahn, Juergen
Julius, Anak Agung
Wan, Leo Q.
Gilbert, Ryan
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Biomedical engineering
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In this work, three optimal experimental design problems were formulated and solved. In one problem, the production of flavonoids, plant metabolites with potential therapeutic properties, was maximized in metabolically engineered bacteria. This led to a 65% increase in production compared to the production levels achieved before optimization. In the second problem, parameter accuracy was maximized for a mathematical model of interleukin-6 (IL-6) extracellular inflammation signaling. A substantial increase in parameter accuracy was observed when the parameters were fitted using data from simulated experiments in which the optimal designs were implemented. In the third problem, in silico differentiation of regulatory T cells (Tregs) from naive T cells was maximized relative to differentiation of pro-inflammatory T-helper-17 (Th17) cells, for use in cell-based treatment of chronic inflammation. It was shown here that time-dependent concentrations of extracellular cytokines can slightly improve Treg induction relative to Th17 induction in silico, compared to constant extracellular cytokine concentrations, but that constant cytokine concentrations may suffice experimentally. This result can potentially facilitate the induction of Tregs for clinical use to treat chronic inflammation.
December 2017
School of Engineering
Dept. of Biomedical Engineering
Rensselaer Polytechnic Institute, Troy, NY
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