AML/T cell interactomics uncover correlates of patient outcomes and the key role of ICAM1 in T cell killing of AML
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1069616 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR27733922_1.fastq.gz | Fastqsanger.gz | |||
| SRR27733922_2.fastq.gz | Fastqsanger.gz | |||
| SRR27733923_1.fastq.gz | Fastqsanger.gz | |||
| SRR27733923_2.fastq.gz | Fastqsanger.gz | |||
| SRR27733924_1.fastq.gz | Fastqsanger.gz |
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Created by COPASI 4.26 (Build 213)
This model is described in the article:
Optimal control of acute myeloid leukaemia
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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.
To cite BioModels Database, please use: BioModels Database: An enhanced, curated and annotated resource for published quantitative kinetic models .
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Please refer to CC0 Public Domain Dedication for more information.
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