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Molecular Models02:00

Molecular Models

Physical models representing molecular architectures of chemical compounds play essential roles in understanding chemistry. The use of molecular models makes it easier to visualize the structures and shapes of atoms and molecules.
Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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 squares (OLS)...
Modeling and Similitude01:12

Modeling and Similitude

Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
Typical Model Studies01:30

Typical Model Studies

Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
Design Example: Creating a Hydraulic Model of a Dam Spillway01:21

Design Example: Creating a Hydraulic Model of a Dam Spillway

Scaled hydraulic models of dam spillways provide a practical way to replicate and study the intricate flow dynamics of these structures. Often built to a 1:15 ratio, these models allow for observing critical water behavior, such as velocity distribution, flow patterns, and energy dissipation.

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Related Experiment Video

Updated: Jun 24, 2026

Brain Organoid Generation from Induced Pluripotent Stem Cells in Home-Made Mini Bioreactors
10:16

Brain Organoid Generation from Induced Pluripotent Stem Cells in Home-Made Mini Bioreactors

Published on: December 11, 2021

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Engineering co-emergence in organoid models.

Ivana Vasic1,2, Todd C McDevitt1,3,4

  • 1Gladstone Institutes, San Francisco, CA, United States of America.

Progress in Biomedical Engineering (Bristol, England)
|December 10, 2025
PubMed
Summary
This summary is machine-generated.

Pluripotent stem cell organoids offer powerful in vitro models for development and disease research. Engineering cooperative emergence addresses variability in stem cell differentiation for improved consistency and control in organoid models.

Keywords:
co-emergencemorphogenesisorganoidspluripotent stem cellssymmetry breakingtissue engineering

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Area of Science:

  • Stem cell biology
  • Developmental biology
  • Regenerative medicine

Background:

  • Pluripotent stem cell-derived organoids are valuable in vitro models for studying development and disease.
  • Their self-renewal and self-organization capabilities enable the creation of complex multicellular structures.
  • Clinical translation is hindered by stochastic differentiation, leading to inconsistencies in cell maturity, tissue function, and reproducibility.

Purpose of the Study:

  • To explore novel approaches for enhancing the consistency and control of organoid models.
  • To address the challenges posed by stochastic stem cell differentiation in organoid development.
  • To leverage developmental biology principles for engineering improved organoid systems.

Main Methods:

  • Utilizing advances in developmental biology to understand pattern formation and symmetry breaking.
  • Engineering cooperative emergence (co-emergence) strategies within organoid models.
  • Investigating mechanisms regulating self-organization in three-dimensional stem cell cultures.

Main Results:

  • New approaches have been developed based on embryonic developmental mechanisms.
  • These methods aim to engineer cooperative emergence in organoid models.
  • The goal is to overcome limitations of stochastic differentiation and improve organoid characteristics.

Conclusions:

  • Engineering cooperative emergence offers a promising strategy to enhance organoid models.
  • This approach addresses key challenges in stem cell differentiation for improved reproducibility and control.
  • Advances in developmental biology are crucial for the future of organoid technology and its clinical applications.