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Reverse-Engineering Post-Transcriptional Regulation of Gap Genes in Drosophila melanogaster

Overview of attention for article published in PLoS Computational Biology, October 2013
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Title
Reverse-Engineering Post-Transcriptional Regulation of Gap Genes in Drosophila melanogaster
Published in
PLoS Computational Biology, October 2013
DOI 10.1371/journal.pcbi.1003281
Pubmed ID
Authors

Kolja Becker, Eva Balsa-Canto, Damjan Cicin-Sain, Astrid Hoermann, Hilde Janssens, Julio R. Banga, Johannes Jaeger

Abstract

Systems biology proceeds through repeated cycles of experiment and modeling. One way to implement this is reverse engineering, where models are fit to data to infer and analyse regulatory mechanisms. This requires rigorous methods to determine whether model parameters can be properly identified. Applying such methods in a complex biological context remains challenging. We use reverse engineering to study post-transcriptional regulation in pattern formation. As a case study, we analyse expression of the gap genes Krüppel, knirps, and giant in Drosophila melanogaster. We use detailed, quantitative datasets of gap gene mRNA and protein expression to solve and fit a model of post-transcriptional regulation, and establish its structural and practical identifiability. Our results demonstrate that post-transcriptional regulation is not required for patterning in this system, but is necessary for proper control of protein levels. Our work demonstrates that the uniqueness and specificity of a fitted model can be rigorously determined in the context of spatio-temporal pattern formation. This greatly increases the potential of reverse engineering for the study of development and other, similarly complex, biological processes.

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Geographical breakdown

Country Count As %
Spain 1 2%
United States 1 2%
Unknown 55 96%

Demographic breakdown

Readers by professional status Count As %
Researcher 16 28%
Student > Ph. D. Student 11 19%
Student > Bachelor 6 11%
Professor > Associate Professor 4 7%
Student > Doctoral Student 3 5%
Other 11 19%
Unknown 6 11%
Readers by discipline Count As %
Agricultural and Biological Sciences 22 39%
Biochemistry, Genetics and Molecular Biology 8 14%
Computer Science 5 9%
Engineering 4 7%
Physics and Astronomy 4 7%
Other 7 12%
Unknown 7 12%