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Dynamics of Drosophila embryonic patterning network perturbed in space and time using microfluidics.

ABSTRACT: Biochemical networks are perturbed both by fluctuations in environmental conditions and genetic variation. These perturbations must be compensated for, especially when they occur during embryonic pattern formation. Complex chemical reaction networks displaying spatiotemporal dynamics have been controlled and understood by perturbing their environment in space and time. Here, we apply this approach using microfluidics to investigate the robust network in Drosophila melanogaster that compensates for variation in the Bicoid morphogen gradient. We show that the compensation system can counteract the effects of extremely unnatural environmental conditions--a temperature step--in which the anterior and posterior halves of the embryo are developing at different temperatures and thus at different rates. Embryonic patterning was normal under this condition, suggesting that a simple reciprocal gradient system is not the mechanism of compensation. Time-specific reversals of the temperature step narrowed down the critical period for compensation to between 65 and 100 min after onset of embryonic development. The microfluidic technology used here may prove useful to future studies, as it allows spatial and temporal regulation of embryonic development.

SUBMITTER: Lucchetta EM

PROVIDER: S-EPMC2656922 | biostudies-literature | 2005 Apr

REPOSITORIES: biostudies-literature

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Dynamics of Drosophila embryonic patterning network perturbed in space and time using microfluidics.

Lucchetta Elena M EM Lee Ji Hwan JH Fu Lydia A LA Patel Nipam H NH Ismagilov Rustem F RF

Nature 20050401 7037

Biochemical networks are perturbed both by fluctuations in environmental conditions and genetic variation. These perturbations must be compensated for, especially when they occur during embryonic pattern formation. Complex chemical reaction networks displaying spatiotemporal dynamics have been controlled and understood by perturbing their environment in space and time. Here, we apply this approach using microfluidics to investigate the robust network in Drosophila melanogaster that compensates f ...[more]

PMID: 15858575

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Project description:BackgroundOne of the current directions of precision medicine is the use of computational methods to aid in the diagnosis, prognosis, and treatment of disease based on data driven approaches. For instance, in oncology, there has been a particular focus on development of algorithms and biomarkers that can be used for pre-clinical and clinical applications. In particular large-scale omics-based models to predict drug sensitivity in in vitro cancer cell line panels have been used to explore the utility and aid in the development of these models as clinical tools. Additionally, a number of web-based interfaces have been constructed for researchers to explore the potential of drug perturbed gene expression as biomarkers including the NCI Transcriptional Pharmacodynamic Workbench. In this paper we explore the influence of drug perturbed gene dynamics of the NCI Transcriptional Pharmacodynamics Workbench in computational models to predict in vitro drug sensitivity for 15 drugs on the NCI60 cell line panel.ResultsThis work presents three main findings. First, our models show that gene expression profiles that capture changes in gene expression after 24 h of exposure to a high concentration of drug generates the most accurate predictive models compared to the expression profiles under different dosing conditions. Second, signatures of 100 genes are developed for different gene expression profiles; furthermore, when the gene signatures are applied across gene expression profiles model performance is substantially decreased when gene signatures developed using changes in gene expression are applied to non-drugged gene expression. Lastly, we show that the gene interaction networks developed on these signatures show different network topologies and can be used to inform selection of cancer relevant genes.ConclusionOur models suggest that perturbed gene signatures are predictive of drug response, but cannot be applied to predict drug response using unperturbed gene expression. Furthermore, additional drug perturbed gene expression measurements in in vitro cell lines could generate more predictive models; but, more importantly be used in conjunction with computational methods to discover important drug disease relationships.

Dataset Information

Dynamics of Drosophila embryonic patterning network perturbed in space and time using microfluidics.

Publications

Dynamics of Drosophila embryonic patterning network perturbed in space and time using microfluidics.

Similar Datasets

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