scholarly journals Statistical and Mathematical Modeling of Spatiotemporal Dynamics of Stem Cells

2019 ◽  
pp. 219-243 ◽  
Author(s):  
Walter de Back ◽  
Thomas Zerjatke ◽  
Ingo Roeder
Keyword(s):  
Mathematical Modeling ◽  
Stem Cells ◽  
Spatiotemporal Dynamics

How to Characterize Stem Cells? Contributions from Mathematical Modeling

2019 ◽  
Vol 5 (2) ◽  
pp. 57-65 ◽  
Author(s):  
Thomas Stiehl ◽  
Anna Marciniak-Czochra
Keyword(s):  
Mathematical Modeling ◽  
Stem Cells

Mathematical modeling method of cell tension and compression based on multi-modal mechanical signals

2020 ◽  
pp. 1-10
Author(s):  
Dongyang Pan ◽  
Jingrui Liu
Keyword(s):  
Mathematical Modeling ◽  
Stem Cells ◽  
Elastic Modulus ◽  
Human Health ◽  
Mechanical Stimulation ◽  
Cell Morphology ◽  
Cell Mechanics ◽  
Modeling Method ◽  
Mechanical Signals ◽  
Stimulation Of

Mechanical biology is the study of the influence of the mechanical environment on human health, disease, or injury. To study the mechanism of the organism’s perception and response to mechanical signals can promote the development of biomedical basic and clinical research, and promote human health. The purpose of this paper is to study the mathematical modeling method of the effect of multimodal mechanical signals on cell stretching and compression. This article first established a cell mechanics model based on the generalization of membrane theory, introduced the micro-manipulation techniques used to characterize cell mechanics and the method of cell mechanics loading, and then explained why mathematical modeling was established. Finally, according to the multi-modality During the mechanical preparation process, the effects of multi-modal mechanical signals on the stretching and compression of annulus fibrosus stem cells were studied. The experimental results in this paper show that after planting fibrous stem cells with different elastic modulus, the cell proliferation is obvious after the tensile mechanical stimulation of different conditions, and the different elastic modulus scaffolds are stimulated by the tensile mechanical stimulation of 2% tensile amplitude. The cell morphology is different. The low elastic modulus is round-like, and the high elastic modulus is fusiform-like. After 5% and 12% stretch amplitude, the cells are oriented at different elastic modulus. Arranged, there is no obvious difference in cell morphology.

Mathematical modeling of growth and death dynamics of mouse embryonic stem cells irradiated with γ-rays

2014 ◽  
Vol 363 ◽  
pp. 374-380 ◽  
Author(s):  
N. Terranova ◽  
P. Rebuzzini ◽  
G. Mazzini ◽  
E. Borella ◽  
C.A. Redi ◽  
...  
Keyword(s):  
Mathematical Modeling ◽  
Stem Cells ◽  
Embryonic Stem Cells ◽  
Embryonic Stem ◽  
Mouse Embryonic Stem Cells ◽  
Γ Rays

Mathematical Modeling of Normal and Cancer Stem Cells

2017 ◽  
Vol 3 (3) ◽  
pp. 232-239 ◽  
Author(s):  
Lora D. Weiss ◽  
Natalia L. Komarova ◽  
Ignacio A. Rodriguez-Brenes
Keyword(s):  
Mathematical Modeling ◽  
Stem Cells ◽  
Cancer Stem Cells

scholarly journals Control in dormancy or eradication of cancer stem cells: Mathematical modeling and stability issues

2018 ◽  
Vol 449 ◽  
pp. 103-123 ◽  
Author(s):  
Walid Djema ◽  
Catherine Bonnet ◽  
Frédéric Mazenc ◽  
Jean Clairambault ◽  
Emilia Fridman ◽  
...  
Keyword(s):  
Mathematical Modeling ◽  
Stem Cells ◽  
Cancer Stem Cells ◽  
Stability Issues

scholarly journals Predicting gene regulatory networks by combining spatial and temporal gene expression data in Arabidopsis root stem cells

2017 ◽  
Vol 114 (36) ◽  
pp. E7632-E7640 ◽  
Author(s):  
Maria Angels de Luis Balaguer ◽  
Adam P. Fisher ◽  
Natalie M. Clark ◽  
Maria Guadalupe Fernandez-Espinosa ◽  
Barbara K. Möller ◽  
...  
Keyword(s):  
Mathematical Modeling ◽  
Stem Cells ◽  
Stem Cell ◽  
Regulatory Networks ◽  
Network Inference ◽  
Quiescent Center ◽  
Cell Regulation ◽  
Gene Regulatory Network Inference ◽  
Stem Cell Regulation ◽  
Gene Regulatory

Identifying the transcription factors (TFs) and associated networks involved in stem cell regulation is essential for understanding the initiation and growth of plant tissues and organs. Although many TFs have been shown to have a role in the Arabidopsis root stem cells, a comprehensive view of the transcriptional signature of the stem cells is lacking. In this work, we used spatial and temporal transcriptomic data to predict interactions among the genes involved in stem cell regulation. To accomplish this, we transcriptionally profiled several stem cell populations and developed a gene regulatory network inference algorithm that combines clustering with dynamic Bayesian network inference. We leveraged the topology of our networks to infer potential major regulators. Specifically, through mathematical modeling and experimental validation, we identified PERIANTHIA (PAN) as an important molecular regulator of quiescent center function. The results presented in this work show that our combination of molecular biology, computational biology, and mathematical modeling is an efficient approach to identify candidate factors that function in the stem cells.

scholarly journals Lineage Specification of Hematopoietic Stem Cells: Mathematical Modeling and Biological Implications

Stem Cells ◽  
2007 ◽  
Vol 25 (7) ◽  
pp. 1791-1799 ◽  
Author(s):  
Ingmar Glauche ◽  
Michael Cross ◽  
Markus Loeffler ◽  
Ingo Roeder
Keyword(s):  
Mathematical Modeling ◽  
Stem Cells ◽  
Hematopoietic Stem Cells ◽  
Lineage Specification ◽  
Hematopoietic Stem

scholarly journals Mathematical Modeling to Study Multistage Stem Cell Transplantation in HIV-1 Patients

2019 ◽  
Vol 2019 ◽  
pp. 1-8
Author(s):  
Manar A. Al Qudah ◽  
Sana’a A. Zarea ◽  
Saoussan A. Kallel-Jallouli
Keyword(s):  
Mathematical Modeling ◽  
Mathematical Model ◽  
Stem Cells ◽  
Stem Cell ◽  
Stem Cell Transplantation ◽  
Cell Transplantation ◽  
Cell Types ◽  
Therapeutic Measure ◽  
The Right ◽  
Hiv 1

Stem cells as a therapeutic measure for the treatment of different diseases have a great potential to give rise to different mature cells as they could be used to treat HIV-1 patients when provided with the convenient factors. Thus, this paper proposes a new mathematical model, represented by a system of ODEs, to study the effect of stem cell transplantation for HIV-1 patients. Since stem cells lineage passes through many stages to become more specialized cell types, investigating (theorizing) the best stage for these cells to be engrafted was needed. The proposed system of ODEs can help medicine make the right decision about the proposed therapy.

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