Literature DB >> 2790923

A mathematical model of erythropoiesis in mice and rats. Part 1: Structure of the model.

M Loeffler1, K Pantel, H Wulff, H E Wichmann.   

Abstract

A mathematical model has been developed which describes the regulation of erythropoiesis in mice and rats. The main model assumptions are: (1) Regulation is mediated by erythropoietin (EPO). (2) The production of EPO depends exponentially on the tissue oxygen pressure (e.g. in the renal production sites). (3) There are sigmoidal dose-response curves relating the EPO concentration in the plasma to the mitotic activity of CFU-E and proliferative erythropoietic precursors. For maximum stimulation two to four additional mitoses may occur, while for an absent stimulus three to five mitoses may be omitted. (4) The normal precursor transit time of three to four days may be shortened by more than 50% during maximum stimulation. (5) The erythrocytes have a normal lifespan of 42-56 days, which may be reduced to 15-20 days under erythropoietic stimulation. Among these assumptions, the dose-response relationships between EPO and the mitotic activity of CFU-E and the proliferative erythropoietic precursors are the most important hypotheses of the model. This is the first of a series of three papers and gives a description of the mathematical formalism and the parameters used. In the subsequent papers computer simulations on erythropoietic stimulation and suppression are presented.

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Year:  1989        PMID: 2790923     DOI: 10.1111/j.1365-2184.1989.tb00198.x

Source DB:  PubMed          Journal:  Cell Tissue Kinet        ISSN: 0008-8730


  22 in total

1.  Basic pharmacodynamic models for agents that alter production of natural cells.

Authors:  W Krzyzanski; R Ramakrishnan; W J Jusko
Journal:  J Pharmacokinet Biopharm       Date:  1999-10

2.  A model of erythropoiesis in adults with sufficient iron availability.

Authors:  Doris H Fuertinger; Franz Kappel; Stephan Thijssen; Nathan W Levin; Peter Kotanko
Journal:  J Math Biol       Date:  2012-04-18       Impact factor: 2.259

3.  Comparative performance of cell life span and cell transit models for describing erythropoietic drug effects.

Authors:  Nageshwar R Budha; Andreas Kovar; Bernd Meibohm
Journal:  AAPS J       Date:  2011-10-18       Impact factor: 4.009

4.  Population cell life span models for effects of drugs following indirect mechanisms of action.

Authors:  Juan J Perez-Ruixo; Hui C Kimko; Andrew T Chow; Vladimir Piotrovsky; Wojciech Krzyzanski; William J Jusko
Journal:  J Pharmacokinet Pharmacodyn       Date:  2005-12       Impact factor: 2.745

5.  Modelling human granulopoiesis under poly-chemotherapy with G-CSF support.

Authors:  M Scholz; C Engel; M Loeffler
Journal:  J Math Biol       Date:  2004-12-20       Impact factor: 2.259

6.  A mathematical model for selective differentiation of neural progenitor cells on micropatterned polymer substrates.

Authors:  Cory L Howk; Howard A Levine; Michael W Smiley; Surya K Mallapragada; Marit Nilsen-Hamilton; Jisun Oh; Donald S Sakaguchi
Journal:  Math Biosci       Date:  2012-04-30       Impact factor: 2.144

7.  Stochastic modeling of stress erythropoiesis using a two-type age-dependent branching process with immigration.

Authors:  O Hyrien; S A Peslak; N M Yanev; J Palis
Journal:  J Math Biol       Date:  2014-07-03       Impact factor: 2.259

8.  Dynamical modelling of haematopoiesis: an integrated view over the system in homeostasis and under perturbation.

Authors:  Erica Manesso; José Teles; David Bryder; Carsten Peterson
Journal:  J R Soc Interface       Date:  2013-03-06       Impact factor: 4.118

Review 9.  Lifespan based indirect response models.

Authors:  Wojciech Krzyzanski; Juan Jose Perez Ruixo
Journal:  J Pharmacokinet Pharmacodyn       Date:  2012-01-03       Impact factor: 2.745

10.  Development and evaluation of a population pharmacokinetic-pharmacodynamic model of darbepoetin alfa in patients with nonmyeloid malignancies undergoing multicycle chemotherapy.

Authors:  Balaji Agoram; Anne C Heatherington; Marc R Gastonguay
Journal:  AAPS J       Date:  2006-09-01       Impact factor: 4.009

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