Jiao2018 - Feedback regulation in a stem cell model with acute myeloid leukaemia
Model Identifier
BIOMD0000000898
Short description
This is a mathematical model describing the hematopoietic lineages with leukemia lineages, as controlled by end-product negative feedback inhibition. Variables include hematopoietic stem cells, progenitor cells, terminally differentiated HSCs, leukemia stem cells, and terminally differentiated leukemia stem cells.
Format
SBML
(L2V4)
Related Publication
- Feedback regulation in a stem cell model with acute myeloid leukaemia.
- Jiao J, Luo M, Wang R
- BMC systems biology , 4/ 2018 , Volume 12 , Issue Suppl 4 , pages: 43 , PubMed ID: 29745850
- Department of Mathematics, Shanghai University, Shangda Road No.99, Shanghai, 200444, China.
- BACKGROUND:The haematopoietic lineages with leukaemia lineages are considered in this paper. In particular, we mainly consider that haematopoietic lineages are tightly controlled by negative feedback inhibition of end-product. Actually, leukemia has been found 100 years ago. Up to now, the exact mechanism is still unknown, and many factors are thought to be associated with the pathogenesis of leukemia. Nevertheless, it is very necessary to continue the profound study of the pathogenesis of leukemia. Here, we propose a new mathematical model which include some negative feedback inhibition from the terminally differentiated cells of haematopoietic lineages to the haematopoietic stem cells and haematopoietic progenitor cells in order to describe the regulatory mechanisms mentioned above by a set of ordinary differential equations. Afterwards, we carried out detailed dynamical bifurcation analysis of the model, and obtained some meaningful results. RESULTS:In this work, we mainly perform the analysis of the mathematic model by bifurcation theory and numerical simulations. We have not only incorporated some new negative feedback mechanisms to the existing model, but also constructed our own model by using the modeling method of stem cell theory with probability method. Through a series of qualitative analysis and numerical simulations, we obtain that the weak negative feedback for differentiation probability is conducive to the cure of leukemia. However, with the strengthening of negative feedback, leukemia will be more difficult to be cured, and even induce death. In contrast, strong negative feedback for differentiation rate of progenitor cells can promote healthy haematopoiesis and suppress leukaemia. CONCLUSIONS:These results demonstrate that healthy progenitor cells are bestowed a competitive advantage over leukaemia stem cells. Weak g1, g2, and h1 enable the system stays in the healthy state. However, strong h2 can promote healthy haematopoiesis and suppress leukaemia.
Contributors
Submitter of the first revision: Johannes Meyer
Submitter of this revision: Johannes Meyer
Modellers: Johannes Meyer
Submitter of this revision: Johannes Meyer
Modellers: Johannes Meyer
Metadata information
hasTaxon (1 statement)
hasProperty (2 statements)
hasProperty (2 statements)
Curation status
Curated
Modelling approach(es)
Tags
Connected external resources
Name | Description | Size | Actions |
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Model files |
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Jiao2018.xml | SBML L2V4 Representation of Jiao2018 - Feedback regulation in a stem cell model with acute myeloid leukaemia | 51.57 KB | Preview | Download |
Additional files |
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Jiao2018.cps | COPASI file of Jiao2018 - Feedback regulation in a stem cell model with acute myeloid leukaemia | 86.75 KB | Preview | Download |
Jiao2018.sedml | SED-ML file of Jiao2018 - Feedback regulation in a stem cell model with acute myeloid leukaemia | 3.82 KB | Preview | Download |
- Model originally submitted by : Johannes Meyer
- Submitted: Dec 17, 2019 10:51:45 AM
- Last Modified: Dec 17, 2019 10:51:45 AM
Revisions
Legends
: Variable used inside SBML models
: Variable used inside SBML models
Species
Species | Initial Concentration/Amount |
---|---|
A PC C12662 |
0.0 item |
D TDSC C12551 ; EFO:0002954 |
0.0 item |
S HSC C12551 |
10.0 item |
T TDLC EFO:0002954 ; C41069 |
0.0 item |
L LSC C41069 |
10.0 item |
Reactions
Reactions | Rate | Parameters |
---|---|---|
A_PC => D_TDSC | compartment*(1-p_2_D)*v_2_D*A_PC | p_2_D = 0.68; v_2_D = 0.72 |
=> S_HSC | compartment*p_1_D*(K_1-Z_1)*v_1_D*S_HSC | v_1_D = 0.5; Z_1 = 10.0; p_1_D = 0.45; K_1 = 1.0 |
L_LSC => T_TDLC | compartment*(1-p_30)*v_30*L_LSC | p_30 = 0.8; v_30 = 0.7 |
S_HSC => A_PC | compartment*(1-p_1_D)*v_1_D*S_HSC | v_1_D = 0.5; p_1_D = 0.45 |
=> L_LSC | compartment*p_30*(K_2-Z_2)*v_30*L_LSC | p_30 = 0.8; v_30 = 0.7; K_2 = 1.0; Z_2 = 10.0 |
=> A_PC | compartment*p_2_D*(K_2-Z_2)*v_2_D*A_PC | p_2_D = 0.68; K_2 = 1.0; v_2_D = 0.72; Z_2 = 10.0 |
T_TDLC => | compartment*d_2*T_TDLC | d_2 = 0.3 |
D_TDSC => | compartment*d_1*D_TDSC | d_1 = 0.275 |
Curator's comment:
(added: 17 Dec 2019, 10:51:37, updated: 17 Dec 2019, 10:51:37)
(added: 17 Dec 2019, 10:51:37, updated: 17 Dec 2019, 10:51:37)
Reproduced plot of Figure 5 in the original publication.
Model simulated using COPASI 4.24 (Build 197), plot produced using Wolfram Mathematica 11.3.