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関連する概念動画

Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

6.5K
Transcriptional regulators bind to specific cis-regulatory sequences in the DNA to regulate gene transcription. These cis-regulatory sequences are very short, usually less than ten nucleotide pairs in length. The short length means that there is a high probability of the exact same sequence randomly occurring throughout the genome.  Since regulators can also bind to groups of similar sequences, this further increases the chances of random binding. Transcriptional regulators form...
6.5K
Combinatorial Gene Control02:33

Combinatorial Gene Control

8.4K
Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...
8.4K
Operon Model01:23

Operon Model

110
The operon model represents a fundamental mechanism of gene regulation in prokaryotes, enabling coordinated expression of genes involved in related metabolic or functional pathways. Operons consist of structural genes, a promoter, and an operator, with transcription regulated by repressors, activators, and small effector molecules.Structure and Function of OperonsAn operon is a cluster of structural genes transcribed together under the control of a single promoter. The promoter region...
110
Regulation of Expression at Multiple Steps01:23

Regulation of Expression at Multiple Steps

994
The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the...
994
Inducible Operons: lac Operon01:25

Inducible Operons: lac Operon

129
The lac operon in Escherichia coli is a model for understanding inducible gene regulation and metabolic flexibility. It integrates local control by lactose and global regulation through catabolite repression, enabling E. coli to preferentially metabolize glucose when available and switch to lactose utilization when glucose is scarce.Structure and Function of the lac OperonThe lac operon contains three structural genes: lacZ (β-galactosidase), lacY (lactose permease), and lacA...
129
Cis-regulatory Sequences02:02

Cis-regulatory Sequences

10.1K
Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
10.1K

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関連する実験動画

Updated: Sep 9, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

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遺伝子規制ネットワークの推論問題における実用的な不可分性,ケーススタディ

Cody E FitzGerald, Shelley Reich, Victor Agaba

    ArXiv
    |September 5, 2025
    PubMed
    まとめ

    この研究は,計算生物学モデルの課題を強調しています. 何千ものモデルを比較し 重要な遺伝子相互作用を明らかにしました

    科学分野:

    • コンピュータ生物学
    • システム生物学
    • 発達生物学

    背景:

    • 生物学的データ分析はノイズ,限られた測定,観察されていない状態に直面します.
    • モデル開発は不確実性をもたらし 複数の競合する構造を生み出します
    • モデル選択と構造間の比較は数学生物学では十分に活用されていない.

    研究 の 目的:

    • 生物学的モデリングにおける構造的不確実性の分析の必要性に対処する.
    • 強力なモデリングフレームワークを使用して生物学的データから機械的洞察を推論する.
    • 実験データで裏付けられた重要な規制の特徴とネットワーク構造を特定する.

    主な方法:

    • 数学生物学出版物のメタ分析を行い,モデリングの実践を評価した.
    • ネマトード発達の13824の異なる規制ネットワークモデルを開発し,比較した.
    • モデル評価のために3つの実験条件で実際の生物学的データを利用した.

    主要な成果:

    • 実験条件で支持される一般的な規制の特徴を特定した.
    • 条件を超えて適用可能な規制ネットワークの構造を発見した.
    • 最も適したモデルは,主要な規制当局 (eud-1, sult-1, nhr-40) の間で高いポジティブな規制と相互接続性を示した.

    さらに関連する動画

    Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays
    14:06

    Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

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    Applying an Inducible Expression System to Study Interference of Bacterial Virulence Factors with Intracellular Signaling
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    Applying an Inducible Expression System to Study Interference of Bacterial Virulence Factors with Intracellular Signaling

    Published on: June 25, 2015

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    関連する実験動画

    Last Updated: Sep 9, 2025

    Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
    10:44

    Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

    Published on: December 7, 2021

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    Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays
    14:06

    Mapping Bacterial Functional Networks and Pathways in Escherichia Coli using Synthetic Genetic Arrays

    Published on: November 12, 2012

    46.6K
    Applying an Inducible Expression System to Study Interference of Bacterial Virulence Factors with Intracellular Signaling
    08:51

    Applying an Inducible Expression System to Study Interference of Bacterial Virulence Factors with Intracellular Signaling

    Published on: June 25, 2015

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    結論:

    • 開発されたモデリングフレームワークは,生物学的データから規制ネットワーク構造を効果的に推測します.
    • 特定された規制ネットワークは ネマトード発達のメカニズム的な洞察を提供します.
    • 課題と枠組みは,複雑なデータ分析を必要とする多様な科学分野に広く適用できます.