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

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Abstract

A very high number of different types of blood cells must be generated daily through a process called haematopoiesis in order to meet the physiological requirements of the organism. All blood cells originate from a population of relatively few haematopoietic stem cells residing in the bone marrow, which give rise to specific progenitors through different lineages. Steady-state dynamics are governed by cell division and commitment rates as well as by population sizes, while feedback components guarantee the restoration of steady-state conditions. In this study, all parameters governing these processes were estimated in a computational model to describe the haematopoietic hierarchy in adult mice. The model consisted of ordinary differential equations and included negative feedback regulation. A combination of literature data, a novel divide et impera approach for steady-state calculations and stochastic optimization allowed one to reduce possible configurations of the system. The model was able to recapitulate the fundamental steady-state features of haematopoiesis and simulate the re-establishment of steady-state conditions after haemorrhage and bone marrow transplantation. This computational approach to the haematopoietic system is novel and provides insight into the dynamics and the nature of possible solutions, with potential applications in both fundamental and clinical research.

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Subject classification (UKÄ) – MANDATORY

  • Cell and Molecular Biology
Original languageEnglish
Article number20120817
JournalJournal of the Royal Society Interface
Volume10
Issue number80
Publication statusPublished - 2013
Publication categoryResearch
Peer-reviewedYes

Bibliographic note

The information about affiliations in this record was updated in December 2015. The record was previously connected to the following departments: Stem Cell Center (013022011), Division of Molecular Hematology (DMH) (013017011), Computational biology and biological physics (000006113)