Expression profiling with RNA-seq between mouse AML cells with or without chemotherapy
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ABSTRACT:
PROVIDER: PRJNA678444 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR13061100_1.fastq.gz | Fastqsanger.gz | |||
| SRR13061100_2.fastq.gz | Fastqsanger.gz | |||
| SRR13061101_1.fastq.gz | Fastqsanger.gz | |||
| SRR13061101_2.fastq.gz | Fastqsanger.gz | |||
| SRR13061102_1.fastq.gz | Fastqsanger.gz |
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Project description:We reported the expression profiling between mouse AML cells with or without chemotherapy. For this purpose, mouse AML cells were harvested from mice treated with or without chemotherapy drugs on day 7. Gene expression analysis of AML cells with or without chemotherapy identified clearly distinguishable transcriptomic profiles. GO analysis revealed that gene alterations after chemotherapy were related to immune response, RNA biology and several metabolomic processes. mTORC1 signal-related GSEA signatures were also enriched in AML cells after chemotherapy including those of stemness and impaired differentiation.
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Created by COPASI 4.26 (Build 213)
This model is described in the article:
Optimal control of acute myeloid leukaemia
Jesse A. Sharp, Alexander P Browning, Tarunendu Mapder, Kevin Burrage, Matthew J Simpson
Journal of Theoretical Biology 470 (2019) 30–42
Abstract:
Acute myeloid leukaemia (AML) is a blood cancer affecting haematopoietic stem cells. AML is routinely treated with chemotherapy, and so it is of great interest to develop optimal chemotherapy treatment strategies. In this work, we incorporate an immune response into a stem cell model of AML, since we find that previous models lacking an immune response are inappropriate for deriving optimal control strategies. Using optimal control theory, we produce continuous controls and bang-bang controls, corre- sponding to a range of objectives and parameter choices. Through example calculations, we provide a practical approach to applying optimal control using Pontryagin’s Maximum Principle. In particular, we describe and explore factors that have a profound influence on numerical convergence. We find that the convergence behaviour is sensitive to the method of control updating, the nature of the control, and to the relative weighting of terms in the objective function. All codes we use to implement optimal control are made available.
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To the extent possible under law, all copyright and related or neighbouring rights to this encoded model have been dedicated to the public domain worldwide.
Please refer to CC0 Public Domain Dedication for more information.
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