Reverse Engineering the Gap Gene Network of Drosophila melanogaster

Perkins, Theodore J.; Jaeger, Johannes; Reinitz, John; Glass, Leon

doi:10.1371/journal.pcbi.0020051

Cited by 174 publications

(243 citation statements)

References 53 publications

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“…Longabaugh et al, 2005;Perkins et al, 2006). The main parameters in this model are thresholds in gene regulation functions.…”

Section: Discussionmentioning

confidence: 99%

“…For simplicity, we assumed that all species have the same decay rate. Following previous models of gene regulation networks (Plahte, 2001;Bolouri and Davidson, 2002;Albert and Othmer, 2003;Thieffry and Sanchez, 2003;Longabaugh et al, 2005;Perkins et al, 2006), gene expression is approximated by Heaviside functions. The Heaviside function is equal to one when its argument is greater than zero and is equal to zero otherwise.…”

Section: Mathematical Modelmentioning

confidence: 99%

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EGFR-dependent network interactions that pattern Drosophila eggshell appendages

et al. 2012

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SUMMARYSimilar to other organisms, Drosophila uses its Epidermal Growth Factor Receptor (EGFR) multiple times throughout development. One crucial EGFR-dependent event is patterning of the follicular epithelium during oogenesis. In addition to providing inductive cues necessary for body axes specification, patterning of the follicle cells initiates the formation of two respiratory eggshell appendages. Each appendage is derived from a primordium comprising a patch of cells expressing broad (br) and an adjacent stripe of cells expressing rhomboid (rho). Several mechanisms of eggshell patterning have been proposed in the past, but none of them can explain the highly coordinated expression of br and rho. To address some of the outstanding issues in this system, we synthesized the existing information into a revised mathematical model of follicle cell patterning. Based on the computational model analysis, we propose that dorsal appendage primordia are established by sequential action of feed-forward loops and juxtacrine signals activated by the gradient of EGFR signaling. The model describes pattern formation in a large number of mutants and points to several unanswered questions related to the dynamic interaction of the EGFR and Notch pathways.

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“…Longabaugh et al, 2005;Perkins et al, 2006). The main parameters in this model are thresholds in gene regulation functions.…”

Section: Discussionmentioning

confidence: 99%

Section: Mathematical Modelmentioning

confidence: 99%

EGFR-dependent network interactions that pattern Drosophila eggshell appendages

et al. 2012

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“…All the images and quantitative data are stored in the FlyEx database and are widely used by scientific community to study the mechanism of pattern formation, infer regulatory interactions in the segmentation genetic network and develop new mathematical models. 12,[31][32][33][34][35][36][37][38][39][40][41][42][43][44][45] Generally, it is a hard task to adapt the problem-oriented methods and their software implementations to solve similar problems in different biological systems. Our pipeline was successfully adapted to acquire data on expression of segmentation genes at the RNA level.…”

Section: Discussionmentioning

confidence: 99%

Pipeline for acquisition of quantitative data on segmentation gene expression from confocal images

Surkova

Myasnikova²,

Janssens³

et al. 2008

Fly

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We describe a data pipeline developed to extract the quantitative data on segmentation gene expression from confocal images of gene expression patterns in Drosophila. The pipeline consists of five steps: image segmentation, background removal, temporal characterization of an embryo, data registration and data averaging. This pipeline was successfully applied to obtain quantitative gene expression data at cellular resolution in space and at the 6.5-minute resolution in time, as well as to construct a spatiotemporal atlas of segmentation gene expression. Each data pipeline step can be easily adapted to process a wide range of images of gene expression patterns. IntroductionBiology is increasingly asking quantitative questions. Quantification is essential to understand the principles of organism functioning. Modern physics and engineering provide tools and strategies for accurate measurements and the acquisition of comprehensive and consistent data sets. For example, microarrays are widely used to quantify the levels of gene expression, 1 and fluorescence restoration after photobleaching (FRAP) is applied to measure diffusion rate or molecular transport. 2 In the last decade developmental biology has achieved tremendous progress. Due to the success of genetics and functional genomics a large number of genes controlling development has been cloned and sequenced, the products of these genes have been identified, and the function of many of these molecules has been revealed. However, despite of these spectacular achievements the integrative picture of how an organism controls the phenotype of tissues and organs is still absent.The morphogenetic field is a basic unit of ontogeny. 3 This physically detached area is formed by complex coordinated interactions of transcripts and proteins that make up the field. Understanding the developmental processes within the morphogenetic field requires a quantitative characterization of the dynamics of each of the field components.Currently available methods for quantification of gene expression like microarrays, quantitative PCR and CAT assays have some limitations for investigations of developmental processes. All of these methods are based on the preparation of homogenates of cells and are unable to capture spatial information about gene expression.The study of development in morphogenetic fields requires methods for the acquisition of gene expression data that will allow monitoring the expression time course of all of the genes simultaneously at the resolution of a single cell. Confocal scanning microscopy of fluorescently tagged molecules constitute the most common data acquisition strategy, which preserves spatial information about the distribution of gene products. This method involves the recognition of protein or RNA in situ by target-specific primary antibodies or a labeled antisense RNA probe. The detection method is usually based on the use of secondary antibodies conjugated with a fluorophore. Confocal microscopy provides high quality digital images ready for computer pr...

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“…These properties allow us to simplify the system to a one-dimensional array of (dividing) nuclei along the A-P axis. Indeed, due to its modelling-friendly properties, the gap gene system has been studied with a variety of models and methods [29][30][31][32][33][34][35].…”

Section: Introductionmentioning

confidence: 99%

Scatter Search Applied to the Inference of a Development Gene Network

et al. 2017

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Efficient network inference is one of the challenges of current-day biology. Its application to the study of development has seen noteworthy success, yet a multicellular context, tissue growth, and cellular rearrangements impose additional computational costs and prohibit a wide application of current methods. Therefore, reducing computational cost and providing quick feedback at intermediate stages are desirable features for network inference. Here we propose a hybrid approach composed of two stages: exploration with scatter search and exploitation of intermediate solutions with low temperature simulated annealing. We test the approach on the well-understood process of early body plan development in flies, focusing on the gap gene network. We compare the hybrid approach to simulated annealing, a method of network inference with a proven track record. We find that scatter search performs well at exploring parameter space and that low temperature simulated annealing refines the intermediate results into excellent model fits. From this we conclude that for poorly-studied developmental systems, scatter search is a valuable tool for exploration and accelerates the elucidation of gene regulatory networks.

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Reverse Engineering the Gap Gene Network of Drosophila melanogaster

Cited by 174 publications

References 53 publications

EGFR-dependent network interactions that pattern Drosophila eggshell appendages

EGFR-dependent network interactions that pattern Drosophila eggshell appendages

Pipeline for acquisition of quantitative data on segmentation gene expression from confocal images

Scatter Search Applied to the Inference of a Development Gene Network

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