Identifying targets for gene therapy of β-globin disorders using quantitative modeling approach - Bashirov, Rza; Mehraei, Mani (Elsevier, 2017)
Sickle cell disease and β-thalassemia are well-known genetic disorders which are caused by mutations in β-globin gene. Reactivation of fetal hemoglobin in adulthood through induction of γ-globin gene expression has proven be sufficient to ameliorate sickle cell disease and β-thalassemia. In the last few decades, substantial efforts have been made to identify potential target candidates for β-globin diseases. In the present work, we propose an innovative approach for identifying novel molecular targets of β-globin diseases. Our approach is based on quantitative modeling of fetal-to-adult hemoglobin switching network with hybrid functional Petri nets. We verify the coherence of deterministic quantitative model created in this research to be sure it is consistent with qPCR data available for known siRNA- and shRNA-based strategies. Comparison of simulation results for the proposed strategy with the ones obtained for already existing RNAi-mediated treatments shows that our strategy is optimal, as it leads to the highest level of γ-globin induction. Consequently, it has potential beneficial therapeutic effect on β-globin diseases. This study also provides an innovative strategy for the target-based treatment of many other diseases.
The file in this item is the post-print version of the article (author’s copy; author’s final manuscript, accepted for publication after peer-review process). Due to copyright restrictions, the access to the publisher version (published version) of this article is only available via subscription. You may click URI and have access to the Publisher Version of this article through the publisher web site or online databases, if your Library or institution has subscription to the related journal or publication.
Keywords: Hybrid functional Petri net; Quantitative modeling; Cell illustrator; β-thalassemia; Sickle cell disease; Fetal to adult hemoglobin switching network