Dynamic optimal experimental design yields marginal improvement over steady-state results for computational maximisation of regulatory T-cell induction in ex vivo culture (English)
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- New search for: Sinkoe, Andrew
- New search for: Jayaraman, Arul
- New search for: Hahn, Juergen
- New search for: Sinkoe, Andrew
- New search for: Jayaraman, Arul
- New search for: Hahn, Juergen
In:
IET Systems Biology
;
12
, 6
;
241-246
;
2018
- Article (Journal) / Electronic Resource
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Title:Dynamic optimal experimental design yields marginal improvement over steady-state results for computational maximisation of regulatory T-cell induction in ex vivo culture
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Contributors:
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Published in:IET Systems Biology ; 12, 6 ; 241-246
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Publisher:
- New search for: The Institution of Engineering and Technology
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Publication date:2018-06-28
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Size:6 pages
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ISSN:
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DOI:
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Type of media:Article (Journal)
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Type of material:Electronic Resource
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Language:English
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Keywords:cellular biophysics , regulatory T-cell induction , proteins , single concentrations , dynamic cytokine profiles , optimal input function , IL-6 , dynamic optimal experimental design , inflammatory bowel disease , interleukin-2 , patient treatment , optimal constant concentrations , local microenvironment , IL-23 , Tregs relative , Treg differentiation , viable therapeutic option , predicted induction , molecular biophysics , transforming growth factor-β , diseases , computational maximisation
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Source:
Metadata by IET is licensed under CC BY 3.0
Table of contents – Volume 12, Issue 6
The tables of contents are generated automatically and are based on the data records of the individual contributions available in the index of the TIB portal. The display of the Tables of Contents may therefore be incomplete.
- 233
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Biological pest control using a model‐based robust feedbackPuebla, Hector / Roy, Priti Kumar / Velasco‐Perez, Alejandra / Gonzalez‐Brambila, Margarita M. et al. | 2018
- 241
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Dynamic optimal experimental design yields marginal improvement over steady-state results for computational maximisation of regulatory T-cell induction in ex vivo cultureSinkoe, Andrew / Jayaraman, Arul / Hahn, Juergen et al. | 2018
- 247
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Identification of essential proteins based on a new combination of topological and biological features in weighted protein–protein interaction networksElahi, Abdolkarim / Babamir, Seyed Morteza et al. | 2018
- 258
-
Cancers classification based on deep neural networks and emotional learning approachJafarpisheh, Noushin / Teshnehlab, Mohammad et al. | 2018
- 264
-
Time-invariant biological networks with feedback loops: structural equation models and structural identifiabilityWang, Yulin / Luo, Yu / Wang, Mingwen / Miao, Hongyu et al. | 2018
- 273
-
Identifying cancer‐related microRNAs based on subpathwaysLiu, Wenbin / Cui, Zhendong / Zan, Xiangzhen et al. | 2018
- 279
-
Study of cohabitation and interconnection effects on normal and leukaemic stem cells dynamics in acute myeloid leukaemiaZenati, Abdelhafid / Chakir, Messaoud / Tadjine, Mohamed et al. | 2018
- 289
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Modelling and simulation of photosystem II chlorophyll fluorescence transition from dark‐adapted state to light‐adapted stateFeng, Shaopeng / Fu, Lijiang / Xia, Qian / Tan, Jinglu / Jiang, Yongnian / Guo, Ya et al. | 2018
- 294
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Hierarchical parameter estimation of GRN based on topological analysisZhang, Wei / Zhang, Feng / Zhang, Jianming / Wang, Ning et al. | 2018
- 304
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Modelling and simulation of chlorophyll fluorescence from photosystem II as affected by temperatureXia, Qian / Tan, Jinglu / Ji, Xunsheng / Jiang, Yongnian / Guo, Ya et al. | 2018