Jiao2018 - Feedback regulation in a stem cell model with acute myeloid leukaemia

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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.
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.
Submitter of the first revision: Johannes Meyer
Submitter of this revision: Johannes Meyer
Modellers: Johannes Meyer

Metadata information

hasTaxon (1 statement)
Taxonomy Homo sapiens

hasProperty (2 statements)
Mathematical Modelling Ontology Ordinary differential equation model
NCIt Acute Myeloid Leukemia

Curation status


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Model files

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

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
  • Version: 2 public model Download this version
    • Submitted on: Dec 17, 2019 10:51:45 AM
    • Submitted by: Johannes Meyer
    • With comment: Automatically added model identifier BIOMD0000000898
: Variable used inside SBML models

Species Initial Concentration/Amount

0.0 item

C12551 ; EFO:0002954
0.0 item

10.0 item

EFO:0002954 ; C41069
0.0 item

10.0 item
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)
Reproduced plot of Figure 5 in the original publication. Model simulated using COPASI 4.24 (Build 197), plot produced using Wolfram Mathematica 11.3.