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Dynamic optimal experimental design yields marginal improvement over steady-state results for computational maximisation of regulatory T-cell induc…

A new interesting article has been published in IET Syst Biol. 2018 Dec;12(6):241-246. doi: 10.1049/iet-syb.2018.5014. and titled:

Dynamic optimal experimental design yields marginal improvement over steady-state results for computational maximisation of regulatory T-cell induc…

Authors of this article are:

Sinkoe A, Jayaraman A, Hahn J.

A summary of the article is shown below:

The isolation of T cells, followed by differentiation into Regulatory T cells (Tregs), and re-transplantation into the body has been proposed as a therapeutic option for inflammatory bowel disease. A key requirement for making this a viable therapeutic option is the generation of a large population of Tregs. However, cytokines in the local microenvironment can impact the yield of Tregs during differentiation. As such, experimental design is an essential part of evaluating the importance of different cytokine concentrations for Treg differentiation. However, currently only single, constant concentrations of the cytokines have been investigated. This work addresses this point by performing experimental design in silico which seeks to maximize the predicted induction of Tregs relative to Th17 cells, by selecting an optimal input function for the concentrations of TGF-β, IL-2, IL-6, and IL-23. While this approach sounds promising, the results show that only marginal improvements in the concentration of Tregs can be achieved for dynamic cytokine profiles as compared to optimal constant concentrations. Since constant concentrations are easier to implement in experiments, it is recommended for this particular system to keep the concentrations constant where IL-6 should be kept low and high concentrations of TGF-β, IL-2, and IL-23 should be used.

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