ReviewCritical reviews in biochemistry and molecular biology2011
Mathematical modeling of gene expression: a guide for the perplexed biologist.
Review in Critical reviews in biochemistry and molecular biology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 56 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
56 citing papers in PubMed.
- Computational Systems Biology Approaches to Cellular Aging - Integrating Network Maps and Dynamical Models.Quantitative biology (Beijing, China) · 2025Article
- Bulk-level maps of pioneer factor binding dynamics during the Drosophila maternal-to-zygotic transition.Development (Cambridge, England) · 2025Article
- Refining Boolean models with the partial most permissive scheme.Bioinformatics (Oxford, England) · 2025Article
- Investigating the dependency of in vitro benchmark concentrations on exposure time in transcriptomics experiments.Toxicology in vitro : an international journal published in association with BIBRA · 2024Article
- Differential sensitivity to longitudinal and transverse stretch mediates transcriptional responses in mouse neonatal ventricular myocytes.American journal of physiology. Heart and circulatory physiology · 2024Article
- A Kinetic Finite Volume Discretization of the Multidimensional PIDE Model for Gene Regulatory Networks.Bulletin of mathematical biology · 2024Article
- A bistable autoregulatory module in the developing embryo commits cells to binary expression fates.Current biology : CB · 2023Article
- What Is a Digital Twin? Experimental Design for a Data-Centric Machine Learning Perspective in Health.International journal of molecular sciences · 2022Article
- Predictive Modelling in Clinical Bioinformatics: Key Concepts for Startups.Biotech (Basel (Switzerland)) · 2022Article
- Stability selection enables robust learning of differential equations from limited noisy data.Proceedings. Mathematical, physical, and engineering sciences · 2022Article
- Quantifying the regulatory role of individual transcription factors in Escherichia coli.Cell reports · 2021Article
- Emergent correlations in gene expression dynamics as footprints of resource competition.The European physical journal. E, Soft matter · 2021Article
- Deciphering enhancer sequence using thermodynamics-based models and convolutional neural networks.Nucleic acids research · 2021Article
- Homology of process: developmental dynamics in comparative biology.Interface focus · 2021Article
- Nucleocytoplasmic Shuttling of the Mechanosensitive Transcription Factors MRTF and YAP /TAZ.Methods in molecular biology (Clifton, N.J.) · 2021Article
- System-Level Scenarios for the Elucidation of T Cell-Mediated Germinal Center B Cell Differentiation.Frontiers in immunology · 2021Review
- An IRF1-IRF4 Toggle-Switch Controls Tolerogenic and Immunogenic Transcriptional Programming in Human Langerhans Cells.Frontiers in immunology · 2021Article
- How and why to build a mathematical model: A case study using prion aggregation.The Journal of biological chemistry · 2020Review
- LTMG: a novel statistical modeling of transcriptional expression states in single-cell RNA-Seq data.Nucleic acids research · 2019Article
- An expectation-maximization algorithm enables accurate ecological modeling using longitudinal microbiome sequencing data.Microbiome · 2019Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
The detailed analysis of transcriptional networks holds a key for understanding central biological processes, and interest in this field has exploded due to new large-scale data acquisition techniques. Mathematical modeling can provide essential insights, but the diversity of modeling approaches can be a daunting prospect to investigators new to this area. For those interested in beginning a transcriptional mathematical modeling project, we provide here an overview of major types of models and their applications to transcriptional networks. In this discussion of recent literature on thermodynamic, Boolean, and differential equation models, we focus on considerations critical for choosing and validating a modeling approach that will be useful for quantitative understanding of biological systems.
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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.