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Protein Diffusion in the Membrane01:24

Protein Diffusion in the Membrane

4.5K
Proteins show rotational as well as lateral diffusion across the membrane. The lateral diffusion of proteins was confirmed through the cell fusion experiment where mouse and human cells were fused, resulting in hybrid cells. When the human and mouse cells fused, the specific membrane proteins on human and mouse cells were marked with the red and green-fluorescent markers, respectively. Initially, the red and green fluorescence was located on the respective hemisphere of the cell. As time...
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Cells Coordinate Growth and Proliferation02:36

Cells Coordinate Growth and Proliferation

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Cell size is a significant factor impacting cellular design, function, and fitness. There exists some internal coordination by which cells double their masses before division, thus, achieving homeostasis. Coordination between cell growth and proliferation depends on the checkpoints in between cell cycle phases. Loss of coordination or failure in the checkpoint mechanism can drive the cell to uncontrolled growth and loss of cellular function. Like dividing cells that coordinate cellular growth,...
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Diversity in Cell Signaling Responses01:22

Diversity in Cell Signaling Responses

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The physiological function of a cell and cellular communication are outcomes of a range of extrinsic signals, intracellular signaling pathways, and cellular responses. No two cell types express the same repertoire of signaling components. Receptors are highly selective for their cognate ligands, but once activated, they can alter multiple cellular processes such as DNA transcription, protein synthesis, and metabolic activity. 
Graded and Abrupt Responses
Some signaling systems generate...
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Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models00:57

Physiological Pharmacokinetic Models: Blood Flow-Limited Versus Diffusion-Limited Models

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Physiological pharmacokinetic models, often called flow-limited or perfusion models, typically assume a swift drug distribution between tissue and venous blood, creating a rapid drug equilibrium. This premise is based on the idea that drug diffusion is extremely fast, and the cell membrane presents no barrier to drug permeation. In this scenario, where no drug binding occurs, the drug concentration in the tissue equals that of the venous blood leaving the tissue. This greatly simplifies the...
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Updated: Sep 9, 2025

A Method for Determination and Simulation of Permeability and Diffusion in a 3D Tissue Model in a Membrane Insert System for Multi-well Plates
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スクイディフ:拡散モデルを使用して,細胞の発達と干渉に対する反応を予測する.

Siyu He, Yuefei Zhu, Daniel Naveed Tavakol

    bioRxiv : the preprint server for biology
    |September 5, 2025
    PubMed
    まとめ
    この要約は機械生成です。

    Squidiffは細胞のトランスクリプトミックの変化を予測し 薬の発見と病気の仕組みの理解を加速します この計算ツールは,精密医療アプリケーションの迅速な仮説生成に役立ちます.

    さらに関連する動画

    Optimized Staining and Proliferation Modeling Methods for Cell Division Monitoring using Cell Tracking Dyes
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    Image Processing Protocol for the Analysis of the Diffusion and Cluster Size of Membrane Receptors by Fluorescence Microscopy
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    関連する実験動画

    Last Updated: Sep 9, 2025

    A Method for Determination and Simulation of Permeability and Diffusion in a 3D Tissue Model in a Membrane Insert System for Multi-well Plates
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    A Method for Determination and Simulation of Permeability and Diffusion in a 3D Tissue Model in a Membrane Insert System for Multi-well Plates

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    Optimized Staining and Proliferation Modeling Methods for Cell Division Monitoring using Cell Tracking Dyes
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    Image Processing Protocol for the Analysis of the Diffusion and Cluster Size of Membrane Receptors by Fluorescence Microscopy
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    科学分野:

    • コンピュータ生物学
    • ゲノミクス
    • システム生物学

    背景:

    • 単細胞配列は細胞の異質性を明らかにしますが,トランスクリプトミックの変化を刺激にマッピングすることは困難です.
    • 放射線や薬のような刺激に対する 細胞の反応を研究する現在の実験方法は 労働集約的です
    • 病気のメカニズムを明らかにするには,様々な条件下で細胞の行動を予測する効率的なツールが必要です.

    研究 の 目的:

    • スクイディフという新しい計算フレームワークを開発し,環境刺激に反応する様々な細胞のトランスクリプトミックの変化を予測する.
    • 複雑な生物学的プロセスをモデル化し 細胞の反応を予測する スクイディフの有用性を実証する
    • 仮説の生成と薬物の発見を加速するために分子風景のスクリーニングを容易にする.

    主な方法:

    • スクイディフ (Squidiff) を開発し,トランスクリプトミックの変化を予測するための拡散モデルベースの生成フレームワークを開発した.
    • 連続的な消音と意味学的な特徴学習を統合し,一時的な細胞状態を捕捉します.
    • 細胞の分化,遺伝子の混乱,薬剤反応の予測を含む様々なシナリオにモデルを適用した.

    主要な成果:

    • スクイディフは様々な細胞の種類や状態で トランスクリプトミックの風景を正確に予測します
    • 細胞の分化,遺伝子の混乱,薬剤反応のモデル化に強度を示した.
    • 血管のオーガノイドの発達と 中性子放射線と成長因子に対する細胞の反応を モデル化しました

    結論:

    • スクイディフは,実験的な限界を乗り越えて,トランスクリプトミックの変化を効率的に予測できます.
    • このフレームワークは,迅速な仮説生成を容易にし,精密医療の洞察を提供します.
    • スクイディフは生物学的研究と薬の開発のための コンピューティングツールにおける 重要な進歩を表しています