Jove
Visualize
お問い合わせ
JoVE
x logofacebook logolinkedin logoyoutube logo
JoVEについて
概要リーダーシップブログJoVEヘルプセンター
著者向け
出版プロセス編集委員会範囲と方針査読よくある質問投稿
図書館員向け
推薦の声購読アクセスリソース図書館諮問委員会よくある質問
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experimentsアーカイブ
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教員リソースセンター教員サイト
利用規約
プライバシーポリシー
ポリシー

関連する概念動画

What is Gene Expression?01:42

What is Gene Expression?

170.4K
Overview
Gene expression is the process in which DNA directs the synthesis of functional products, that is, proteins. Cells can regulate gene expression at various stages. It allows organisms to generate different cell types and enables cells to adapt to internal and external factors.
Genetic Information Flows from DNA to RNA to Protein
A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is made up of nucleotides and proteins consist of amino...
170.4K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

86
Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
86
DNA Microarrays02:34

DNA Microarrays

18.4K
Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
18.4K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

100
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
100
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

こちらも読む

関連記事

共著者、ジャーナル、引用グラフによってこの研究に関連する記事。

並び替え
Same author

The Role of the Glutamate-Glutamine Cycle in Synaptic Transmission During Ischemia and Recovery.

The European journal of neuroscience·2026
Same author

Investigating transcription factor dynamics in health and disease using FRAP.

FEBS letters·2026
Same author

Modelling stem cell differentiation related processes-A practical overview for biologists.

FEBS letters·2026
Same author

Molecular insights into heart field-specific cardiomyocyte differentiation - A computational study.

PloS one·2026
Same author

On the transition between autonomous and nonautonomous systems: The case of FitzHugh-Nagumo's model.

Chaos (Woodbury, N.Y.)·2024
Same author

Computer-Assisted Proofs of Hopf Bubbles and Degenerate Hopf Bifurcations.

Journal of dynamics and differential equations·2024

関連する実験動画

Updated: Sep 10, 2025

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

634

機械学習を活用した遺伝子発現データを記述するモデルの特定

Lucas F Jansen Klomp1,2, Elena Queirolo3, Janine N Post2

  • 1Mathematics of Imaging & AI, Department of Applied Mathematics, University of Twente, Enschede, The Netherlands.

Interface focus
|August 27, 2025
PubMed
まとめ
この要約は機械生成です。

この研究は,遺伝子規制ネットワークの機械的通常の微分方程式モデルを識別するためにニューラルネットワークを使用するフレームワークを導入します. このアプローチはモデルの解釈性を高め,細胞の分化のような細胞プロセスに関する新しい仮説を生成します.

キーワード:
ODEモデリング遺伝子規制ネットワークグラフニューラルネットワークscRNA-seq について

さらに関連する動画

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

892
A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
09:35

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

17.9K

関連する実験動画

Last Updated: Sep 10, 2025

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

634
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

892
A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research
09:35

A Protocol for Using Gene Set Enrichment Analysis to Identify the Appropriate Animal Model for Translational Research

Published on: August 16, 2017

17.9K

科学分野:

  • システム生物学
  • コンピュータ生物学
  • 機械学習

背景:

  • 機械的普通微分方程式 (ODE) モデルは,細胞の過程を理解し,生物学的仮説を策定するために不可欠です.
  • これらのモデルのデータ主導の推論は増加していますが,解釈性を失うことなく機械学習 (ML) を統合することは依然として課題です.
  • 遺伝子調節ネットワーク (GRNs) は,細胞の分化を含む複雑な細胞内ダイナミクスを支配する.

研究 の 目的:

  • GRNの解釈可能なデータ主導のメカニズム的ODEモデルを特定するためのニューラルネットワークを活用する枠組みを提示する.
  • モデル精度と生物学的洞察を向上させるため,GRN内の新しい接続を提案するためにMLを使用します.
  • 細胞内プロセスのダイナミクスに関する検証可能な仮説を生成する.

主な方法:

  • 機械的なODEモデリングとニューラルネットワークを統合するフレームワークの開発.
  • グラフ・オートエンコーダー・モデルの適用で,GRN内の接続を推論し,提案する.
  • 細胞の分化などの時間依存の細胞内プロセスに対するアプローチの検証.

主要な成果:

  • グラフ・オートエンコーダを成功裏に応用し,新しいGRN接続を提案した.
  • 改善されたグラフ構造がダイナミック・システムの識別を向上させる方法を示した.
  • 特定された細胞プロセスの動態に関する新しい仮説を生成した.

結論:

  • 提案されたフレームワークは,GRNの解釈可能なメカニズムモデルを効果的に識別するためにニューラルネットワークを使用します.
  • このアプローチは,複雑な生物学的システムに対する新しいデータに基づく仮説の生成を容易にする.
  • 機械学習の統合は システム生物学や 細胞メカニズムを理解するための強力なツールです