ArticleCell systems2015
A Systematic Ensemble Approach to Thermodynamic Modeling of Gene Expression from Sequence Data.
Article in Cell systems, 2015. 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.
- Mechanistic analysis of enhancer sequences in the estrogen receptor transcriptional program.Communications biology · 2024Article
- Modeling the Flowering Activation Motif during Vernalization in Legumes: A Case Study ofLife (Basel, Switzerland) · 2023Article
- Improving dynamic predictions with ensembles of observable models.Bioinformatics (Oxford, England) · 2023Article
- What Is a Digital Twin? Experimental Design for a Data-Centric Machine Learning Perspective in Health.International journal of molecular sciences · 2022Article
- Thermodynamics-based modeling reveals regulatory effects of indirect transcription factor-DNA binding.iScience · 2022Article
- Hardware, Software, and Wetware Codesign Environment for Synthetic Biology.Biodesign research · 2022Review
- Fitting thermodynamic-based models: Incorporating parameter sensitivity improves the performance of an evolutionary algorithm.Mathematical biosciences · 2021Article
- Deciphering enhancer sequence using thermodynamics-based models and convolutional neural networks.Nucleic acids research · 2021Article
- Anticipating response function in gene regulatory networks.Journal of the Royal Society, Interface · 2021Article
- Dynamical Modeling of the Core Gene Network Controlling Transition to Flowering inFrontiers in genetics · 2021Article
- Expression pattern determines regulatory logic.PloS one · 2021Article
- Article
- LTMG: a novel statistical modeling of transcriptional expression states in single-cell RNA-Seq data.Nucleic acids research · 2019Article
- Defining informative priors for ensemble modeling in systems biology.Nature protocols · 2018Article
- Modeling craniofacial development reveals spatiotemporal constraints on robust patterning of the mandibular arch.PLoS computational biology · 2018Article
- An information theoretic treatment of sequence-to-expression modeling.PLoS computational biology · 2018Article
- Outstanding questions in developmental ERK signaling.Development (Cambridge, England) · 2018Review
- The Capicua tumor suppressor: a gatekeeper of Ras signaling in development and cancer.Cell cycle (Georgetown, Tex.) · 2018Review
- Dynamical Modeling of the Core Gene Network Controlling Flowering Suggests Cumulative Activation From theFrontiers in genetics · 2018Article
- Quantitative Measurement and Thermodynamic Modeling of Fused Enhancers Support a Two-Tiered Mechanism for Interpreting Regulatory DNA.Cell reports · 2017Article
Corrections and comments
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Authors and funding
8 authors.
Funding
Abstract
To understand the relationship between an enhancer DNA sequence and quantitative gene expression, thermodynamics-driven mathematical models of transcription are often employed. These "sequence-to-expression" models can describe an incomplete or even incorrect set of regulatory relationships if the parameter space is not searched systematically. Here, we focus on an enhancer of the Drosophila gene ind and demonstrate how a systematic search of parameter space can reveal a more comprehensive picture of a gene's regulatory mechanisms, resolve outstanding ambiguities, and suggest testable hypotheses. We describe an approach that generates an ensemble of ind models; all of these models are technically acceptable solutions to the sequence-to-expression problem in light of wild-type data, and some represent mechanistically distinct hypotheses about the regulation of ind. This ensemble can be restricted to biologically plausible models using requirements gleaned from in vivo perturbation experiments. Biologically plausible models make unique predictions about how specific ind enhancer sequences affect ind expression; we validate these predictions in vivo through site mutagenesis in transgenic Drosophila embryos.
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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.