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

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

112
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
112
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

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

204
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...
204
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

76
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...
76
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

111
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
111
Environmental Applications of Microorganisms01:30

Environmental Applications of Microorganisms

141
Microorganisms play a pivotal role in maintaining ecosystem balance by recycling essential elements such as carbon, nitrogen, and phosphorus, as well as supporting processes like bioremediation, wastewater treatment, and biofuel production.Microbes in Elemental CyclesIn the carbon cycle, microorganisms decompose organic matter, releasing carbon dioxide via aerobic respiration. This carbon dioxide is subsequently used by photosynthetic organisms to synthesize organic compounds, closing the...
141
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

167
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
167

You might also read

Related Articles

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

Sort by
Same author

NicheTrans: spatial-aware cross-omics translation.

Nature methods·2026
Same author

SOFisher: reinforcement learning-guided experiment designs for spatial omics.

Nature communications·2026
Same author

Benchmarking alignment methods for spatial transcriptomics data.

Nature computational science·2026
Same author

Scalable homology detection with ERAST.

Nature biotechnology·2026
Same author

Scalable discovery of spatial multicellular patterns via neighborhood-to-sequence transformation.

Communications biology·2026
Same author

3d-OT: a deep geometry-aware framework for heterogeneous slices alignment of spatial multi-omics.

Nature methods·2026

Related Experiment Video

Updated: Aug 19, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
09:19

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

5.0K

SOTIP is a versatile method for microenvironment modeling with spatial omics data.

Zhiyuan Yuan1,2,3, Yisi Li4, Minglei Shi5

  • 1Institute of Science and Technology for Brain-Inspired Intelligence; MOE Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence; MOE Frontiers Center for Brain Science, Fudan University, Shanghai, 200433, China. zhiyuan@fudan.edu.cn.

Nature Communications
|November 28, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces SOTIP, a novel method for spatial omics analysis that models microenvironments (MEs) and their interactions. SOTIP reveals spatial heterogeneity patterns and identifies microenvironments linked to patient survival in cancer.

More Related Videos

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
09:53

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

7.3K
Author Spotlight: Unlocking the Mysteries of Oral Potential Malignancies
05:42

Author Spotlight: Unlocking the Mysteries of Oral Potential Malignancies

Published on: August 11, 2023

1.1K

Related Experiment Videos

Last Updated: Aug 19, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
09:19

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection

Published on: July 6, 2022

5.0K
Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography
09:53

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography

Published on: August 16, 2020

7.3K
Author Spotlight: Unlocking the Mysteries of Oral Potential Malignancies
05:42

Author Spotlight: Unlocking the Mysteries of Oral Potential Malignancies

Published on: August 11, 2023

1.1K

Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • Spatial omics technologies generate large-scale, multi-modal datasets.
  • Existing methods often overlook the crucial role of microenvironments (MEs) in cellular dynamics.
  • Understanding ME interrelationships is vital for interpreting complex biological systems.

Purpose of the Study:

  • To develop a versatile computational method, SOTIP (Spatial Omics mulTIPle-task analysis), for integrated analysis of spatial omics data.
  • To incorporate microenvironments and their interrelationships into a unified analytical framework.
  • To enable downstream tasks such as spatial heterogeneity quantification and domain identification.

Main Methods:

  • Development of SOTIP, a graph-based method that models spatial omics data by incorporating microenvironments (MEs).
  • Implementation of modules for spatial heterogeneity quantification, spatial domain identification, and differential microenvironment analysis.
  • Validation of SOTIP's accuracy, robustness, scalability, and interpretability across diverse spatial omics datasets.

Main Results:

  • SOTIP successfully quantifies spatial heterogeneity and identifies distinct spatial domains.
  • Analysis of mouse cerebral cortex data revealed a spatial heterogeneity gradient correlated with cortical depth.
  • In human triple-negative breast cancer data, SOTIP identified molecular polarizations and MEs associated with patient survival outcomes.

Conclusions:

  • SOTIP provides a powerful framework for exploring and interpreting spatial omics data by modeling biologically relevant microenvironments.
  • The method outperforms existing state-of-the-art approaches in analyzing complex spatial omics datasets.
  • SOTIP offers new perspectives for understanding tissue architecture and its relationship to biological functions and disease.