title: Computational modeling of the expansion of human cord blood CD133+ hematopoietic stem/progenitor cells with different cytokine combinations.
abstract: BACKGROUND
  Many important problems in cell biology require dense non-linear interactions between functional modules to be considered. The importance of computer simulation in understanding cellular processes is now widely accepted, and a variety of simulation algorithms useful for studying certain subsystems have been designed. Expansion of hematopoietic stem and progenitor cells (HSC/HPC) in ex vivo culture with cytokines and small molecules is a method to increase the restricted numbers of stem cells found in umbilical cord blood (CB), while also enhancing the content of early engrafting neutrophil and platelet precursors. The efficacy of the expanded product depends on the composition of the cocktail of cytokines and small molecules used for culture. Testing the influence of a cytokine or small molecule on the expansion of HSC/HPC is a laborious and expensive process. We therefore developed a computational model based on cellular signaling interactions that predict the influence of a cytokine on the survival, duplication and differentiation of the CD133(+) HSC/HPC subset from human umbilical CB.
  
  
  RESULTS
  We have used results from in vitro expansion cultures with different combinations of one or more cytokines to develop an ordinary differential equation model that includes the effect of cytokines on survival, duplication and differentiation of the CD133(+) HSC/HPC. Comparing the results of in vitro and in silico experiments, we show that the model can predict the effect of a cytokine on the fold expansion and differentiation of CB CD133(+) HSC/HPC after 8-day culture on a 3D scaffold. Supplementary data are available at Bioinformatics online.
authors: Stem Cell Research, Nuffield Division of Clinical Laboratory Sciences, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 9DU, UK, NHS Blood and Transplant Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK.
authors: Francesca
authors: F
authors: Gullo
authors: Stem Cell Research, Nuffield Division of Clinical Laboratory Sciences, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 9DU, UK, NHS Blood and Transplant Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK.
authors: Mark
authors: M
authors: van der Garde
authors: Department of Drug Science and.
authors: Giulia
authors: G
authors: Russo
authors: Department of Mathematics and Computer Science, University of Catania, 95125 Catania, Italy.
authors: Marzio
authors: M
authors: Pennisi
authors: Department of Mathematics and Computer Science, University of Catania, 95125 Catania, Italy.
authors: Santo
authors: S
authors: Motta
authors: Department of Drug Science and.
authors: Francesco
authors: F
authors: Pappalardo
authors: Stem Cell Research, Nuffield Division of Clinical Laboratory Sciences, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 9DU, UK, NHS Blood and Transplant Oxford, John Radcliffe Hospital, Oxford OX3 9DU, UK.
authors: Suzanne
authors: S
authors: Watt
mesh_terms: D018952:Antigens, CD34 / Q000378:metabolism*; D002454:Cell Differentiation / Q000187:drug effects*; D002470:Cell Survival / Q000187:drug effects; D002478:Cells, Cultured; D019295:Computational Biology / Q000379:methods*; D003198:Computer Simulation; D016207:Cytokines / Q000494:pharmacology*; D005312:Fetal Blood / Q000166:cytology* / Q000187:drug effects / Q000378:metabolism; D006412:Hematopoietic Stem Cells / Q000166:cytology* / Q000187:drug effects / Q000378:metabolism; D006801:Humans
chemical_list: D018952:Antigens, CD34; D016207:Cytokines