ArticlePLoS computational biology2013
Reverse-engineering post-transcriptional regulation of gap genes in Drosophila melanogaster.
Article in PLoS computational biology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 47 citations in OpenAlex.
- A conserved coupling of transcriptional ON and OFF periods underlies bursting dynamics.Nature structural & molecular biology · 2025Article
- Multifaceted effects on even-skipped transcriptional dynamics upon Krüppel dosage changes.Development (Cambridge, England) · 2024Article
- Transcriptomics Reveals the Mechanism ofNutrients · 2023Article
- Application of Domain- and Genotype-Specific Models to Infer Post-Transcriptional Regulation of Segmentation Gene Expression inLife (Basel, Switzerland) · 2021Article
- Bayesian inversion of a diffusion model with application to biology.Journal of mathematical biology · 2021Article
- Bayesian model selection for the Drosophila gap gene network.BMC bioinformatics · 2019Article
- Quantitative analysis reveals genotype- and domain- specific differences between mRNA and protein expression of segmentation genes in Drosophila.Developmental biology · 2019Article
- Quantifying post-transcriptional regulation in the development of Drosophila melanogaster.Nature communications · 2018Article
- Dynamic Maternal Gradients Control Timing and Shift-Rates for Drosophila Gap Gene Expression.PLoS computational biology · 2017Article
- Discovering novel phenotypes with automatically inferred dynamic models: a partial melanocyte conversion in Xenopus.Scientific reports · 2017Article
- Parameter estimation in large-scale systems biology models: a parallel and self-adaptive cooperative strategy.BMC bioinformatics · 2017Article
- Gap Gene Regulatory Dynamics Evolve along a Genotype Network.Molecular biology and evolution · 2016Article
- Physiological controls of large-scale patterning in planarian regeneration: a molecular and computational perspective on growth and form.Regeneration (Oxford, England) · 2016Article
- Parallel Simulated Annealing Using an Adaptive Resampling Interval.Parallel computing · 2016Article
- MoCha: Molecular Characterization of Unknown Pathways.Journal of computational biology : a journal of computational molecular cell biology · 2016Article
- Identifying Novel Transcriptional Regulators with Circadian Expression.Molecular and cellular biology · 2016Article
- Inferring regulatory networks from experimental morphological phenotypes: a computational method reverse-engineers planarian regeneration.PLoS computational biology · 2015Article
- BioPreDyn-bench: a suite of benchmark problems for dynamic modelling in systems biology.BMC systems biology · 2015Article
- Article
- SuperFly: a comparative database for quantified spatio-temporal gene expression patterns in early dipteran embryos.Nucleic acids research · 2015Article
Corrections and comments
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Authors and funding
7 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.