A two-dimensional simulation model of the bicoid gradient in Drosophila.

Autor: Jingyuan Deng, Wei Wang, Long Jason Lu, Jun Ma
Jazyk: angličtina
Rok vydání: 2010
Předmět:
Zdroj: PLoS ONE, Vol 5, Iss 4, p e10275 (2010)
Druh dokumentu: article
ISSN: 1932-6203
DOI: 10.1371/journal.pone.0010275
Popis: BACKGROUND:Bicoid (Bcd) is a Drosophila morphogenetic protein responsible for patterning the anterior structures in embryos. Recent experimental studies have revealed important insights into the behavior of this morphogen gradient, making it necessary to develop a model that can recapitulate the biological features of the system, including its dynamic and scaling properties. METHODOLOGY/PRINCIPAL FINDINGS:We present a biologically realistic 2-D model of the dynamics of the Bcd gradient in Drosophila embryos. This model is based on equilibrium binding of Bcd molecules to non-specific, low affinity DNA sites throughout the Drosophila genome. It considers both the diffusion media within which the Bcd gradient is formed and the dynamic and other relevant properties of bcd mRNA from which Bcd protein is produced. Our model recapitulates key features of the Bcd protein gradient observed experimentally, including its scaling properties and the stability of its nuclear concentrations during development. Our simulation model also allows us to evaluate the effects of other biological activities on Bcd gradient formation, including the dynamic redistribution of bcd mRNA in early embryos. Our simulation results suggest that, in our model, Bcd protein diffusion is important for the formation of an exponential gradient in embryos. CONCLUSIONS/SIGNIFICANCE:The 2-D model described in this report is a simple and versatile simulation procedure, providing a quantitative evaluation of the Bcd gradient system. Our results suggest an important role of Bcd binding to non-specific, low-affinity DNA sites in proper formation of the Bcd gradient in our model. They demonstrate that highly complex biological systems can be effectively modeled with relatively few parameters.
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