Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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...
Nonlinear Pharmacokinetics: Causes of Nonlinearity01:22

Nonlinear Pharmacokinetics: Causes of Nonlinearity

Nonlinearity in drug pharmacokinetics is caused by various factors influencing how a drug is absorbed, distributed, metabolized, and excreted. Understanding these nonlinear processes is crucial for predicting drug behavior in the body and optimizing drug dosing regimens.
Nonlinear drug absorption can occur when the process is rate-limited by solubility, carrier-mediated transport systems, or saturation of the presystemic gut wall or hepatic metabolism. For instance, high doses of riboflavin...
Methods of Medium Optimization01:28

Methods of Medium Optimization

Optimizing growth media enhances microbial proliferation and maximizes product yield. Statistical experimental design methodologies provide structured and reproducible approaches, offering progressively higher levels of robustness and efficiency.The One-Factor-at-a-Time (OFAT) MethodThe One-Factor-at-a-Time (OFAT) method involves adjusting a single variable while keeping all others constant. However, it cannot detect interactions between variables, often leading to suboptimal outcomes when...
Pharmacodynamic Models: Additive and Proportional Drug Effect Model01:09

Pharmacodynamic Models: Additive and Proportional Drug Effect Model

Drug response models describe how pharmacological agents interact with biological systems to produce measurable effects. Baseline responses are inherent physiological activities without a drug significantly influencing the observed pharmacological outcomes. Depending on the drug response model employed, these baseline responses may combine with the drug's effect in either an additive or proportional manner.Additive Drug Response ModelIn the additive model, the drug effect is independent of the...
Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model01:13

Parameters Affecting Nonlinear Elimination: Zero-Order Input, First-Order Absorption and Two-Compartment Model

Drugs administered through various routes can lead to nonlinear elimination, resulting in complex pharmacokinetic behaviors crucial to understanding efficacious drug dosing.
When a drug is administered through a constant intravenous infusion and eliminated via nonlinear pharmacokinetics, it follows zero-order input. For example, oral drugs undergo first-order absorption upon administration and are eliminated through nonlinear pharmacokinetics.
In the case of subcutaneously administered drugs,...
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Subclinical immunometabolic perturbations in the neonatal lung following maternal microplastic exposure in mice.

Toxicology·2026
Same author

A multi-task masked autoencoder with GAN-based augmentation for PD-L1 prediction from chest CT images.

Scientific reports·2026
Same author

Hippo signaling pathway regulates branching morphogenesis of the fetal lung under hypoxia.

Pediatric research·2026
Same author

ASO Visual Abstract: CT-Based Prediction of Visceral Pleural Invasion in Lung Adenocarcinoma ≤ 3 cm: Enhancing Deep Learning Specificity by Waiving Chest Wall Information.

Annals of surgical oncology·2026
Same author

CT-based Prediction of Visceral Pleural Invasion in Lung Adenocarcinoma ≤ 3 cm: Enhancing Deep Learning Specificity by Waiving Chest Wall Information.

Annals of surgical oncology·2026
Same author

Optimizing maternal weight gain to improve neonatal Health: A nationwide birth cohort analysis in Taiwan.

Journal of the Formosan Medical Association = Taiwan yi zhi·2026
Same journal

Correction to: A quantitative systems pharmacology (QSP) model for Pneumocystis treatment in mice.

BMC systems biology·2019
Same journal

Predicting disease-related phenotypes using an integrated phenotype similarity measurement based on HPO.

BMC systems biology·2019
Same journal

Fusing gene expressions and transitive protein-protein interactions for inference of gene regulatory networks.

BMC systems biology·2019
Same journal

A fast and efficient count-based matrix factorization method for detecting cell types from single-cell RNAseq data.

BMC systems biology·2019
Same journal

GNE: a deep learning framework for gene network inference by aggregating biological information.

BMC systems biology·2019
Same journal

FCMDAP: using miRNA family and cluster information to improve the prediction accuracy of disease related miRNAs.

BMC systems biology·2019
See all related articles

Related Experiment Video

Updated: Jun 15, 2026

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

Inferring genetic interactions via a nonlinear model and an optimization algorithm.

Chung-Ming Chen1, Chih Lee, Cheng-Long Chuang

  • 1Institute of Statistical Science, Academia Sinica, No 128, Sec 2, Academia Road, Taipei 115, Taiwan.

BMC Systems Biology
|February 27, 2010
PubMed
Summary
This summary is machine-generated.

A new algorithm, GASA, infers genetic and transcriptional interactions from gene expression data, outperforming existing methods. This tool models complex interactions for predicting biological pathways and understanding disease mechanisms.

Related Experiment Videos

Last Updated: Jun 15, 2026

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules
10:58

Protein WISDOM: A Workbench for In silico De novo Design of BioMolecules

Published on: July 25, 2013

Area of Science:

  • Systems Biology
  • Computational Biology
  • Bioinformatics

Background:

  • Biochemical pathways are crucial for complex human diseases.
  • Microarray Gene Expression Data (MGED) enables nonlinear modeling of genetic/transcriptional interactions.
  • Existing nonlinear models do not capture cooperative or competitive interactions of multiple transcription factors (TFs).

Purpose of the Study:

  • To develop a novel nonlinear modeling approach for inferring genetic and transcriptional regulatory interactions.
  • To address limitations of existing models in handling multiple TF interactions.
  • To apply the developed method to biological networks for pathway inference.

Main Methods:

  • Developed an S-shape model and a hybrid optimization algorithm (GASA) combining Genetic Algorithm (GA), Simulated Annealing (SA), and steepest gradient descent.
  • Evaluated GASA's performance using simulated data with varying noise levels, model selection criteria, and search spaces.
  • Compared GASA against Network Component Analysis, Time Series Network Inference Algorithm (TSNI), and GA with regular GA (GAGA) or SA (GASA).

Main Results:

  • GASA demonstrated superior performance compared to existing methods like TSNI, GAGA, and GA with regular SA.
  • Applied to human T-cell apoptosis, GASA inferred a subnetwork with interactions supported by existing literature.
  • Inferred transcriptional factors for yeast cell cycle targets, outperforming GAGA and TSNI, with results aligning with YEASTRACT data.

Conclusions:

  • GASA effectively infers genetic and transcriptional regulatory interactions, capturing nonlinear mechanisms.
  • The method shows promise for inferring transcriptional interactions in yeast and potentially other organisms.
  • Predicted interactions for human T-cell apoptosis suggest GASA's utility in inferring complex biochemical pathways.