Evidence map›Paper›PMID 37074582›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2023

Quantitative Imaging Analysis of NF-κB for Mathematical Modeling Applications.

Johannes Nicolaus Wibisana, Takehiko Inaba, Yasushi Sako, Mariko Okada

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Article in Methods in molecular biology (Clifton, N.J.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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1 · What the graph read from it

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.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Johannes Nicolaus WibisanaInstitute for Protein Research, Osaka University, Suita, Osaka, Japan.
Takehiko InabaCellular Informatics Laboratory, RIKEN Cluster for Pioneering Resaerch, Hirosawa, Wako, Japan.
Yasushi SakoCellular Informatics Laboratory, RIKEN Cluster for Pioneering Resaerch, Hirosawa, Wako, Japan.
Mariko OkadaInstitute for Protein Research, Osaka University, Suita, Osaka, Japan. mokada@protein.osaka-u.ac.jp.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Mathematical models can integrate different types of experimental datasets, reconstitute biological systems in silico, and identify previously unknown molecular mechanisms. Over the past decade, mathematical models have been developed based on quantitative observations, such as live-cell imaging and biochemical assays. However, it is difficult to directly integrate next-generation sequencing (NGS) data. Although highly dimensional, NGS data mostly only provides a "snapshot" of cellular states. Nevertheless, the development of various methods for NGS analysis has led to much more accurate predictions of transcription factor activity and has revealed various concepts regarding transcriptional regulation. Therefore, fluorescence live-cell imaging of transcription factors can help alleviate the limitations in NGS data by supplementing temporal information, linking NGS to mathematical modeling. This chapter introduces an analytical method for quantifying dynamics of nuclear factor kappaB (NF-κB) which forms aggregates in the nucleus. The method may also be applicable to other transcription factors regulated in a similar fashion.

Indexed as

NF-kappa BSignal TransductionGene Expression RegulationModels, BiologicalTranscription FactorsNF-kappa BTranscription FactorsDT40Fluorescence microscopyImage analysisNF-κBTranscriptional regulationTranscription factor

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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.