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

Morphogenesis02:19

Morphogenesis

Plant morphogenesis—the development of a plant’s form and structure—involves several overlapping developmental processes, including growth and cell differentiation. Precursor cells differentiate into specific cell types, which are organized into the tissues and organ systems that make up the functional plant.
Meristems and Plant Growth02:36

Meristems and Plant Growth

Plants grow throughout their lives; this is called indeterminate growth, and it distinguishes plants from most animals. Although certain parts of plants stop growing (e.g., leaves and flowers), others grow continuously—like roots and stems.
Growth Models with Integration: Problem Solving01:27

Growth Models with Integration: Problem Solving

In population modeling, integration provides a systematic way to determine accumulated quantities from known rates of change. One such application arises in ecology, where the total weight of a fish population in a body of water is referred to as its biomass. When the rate of growth of this biomass is known as a function of time, calculus can be used to determine the total biomass at a future date.Growth Rate and Biomass FunctionLet the growth rate of the fish population be represented by a...
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...
Modeling with Differential Equations01:25

Modeling with Differential Equations

Population dynamics can be described mathematically by considering the population size P(t) as a function of time. The rate of change of the population is then represented by the derivative of P(t). A simple assumption is that the rate of growth is proportional to the size of the population itself. This leads to an exponential growth model, where the population increases rapidly without bound. While this is a useful first approximation, it does not reflect realistic long-term...
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.

You might also read

Related Articles

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

Sort by
Same author

ROS inhibits microtubule dynamics and cell growth heterogeneity during Arabidopsis sepal morphogenesis.

iScience·2026
Same author

How to grow a flat leaf.

Current biology : CB·2026
Same author

Single-nucleus transcriptomics resolves multiple fate dynamics between inflorescence meristem and primary stem.

Science advances·2026
Same author

Spatial and single-cell transcriptomics uncover brassinosteroid-mediated coordination of sepal elongation in Arabidopsis.

Genome biology·2026
Same author

Growth under pressure: The pros and cons of polyploidy induced by stress.

Proceedings of the National Academy of Sciences of the United States of America·2026
Same author

How cells grow differently from their neighbors: How noise becomes a symphony.

Current opinion in plant biology·2026
Same journal

Long-Range Signals Built upon Plant Structural Continuity.

Annual review of plant biology·2026
Same journal

The Power of Symbiosis in Life and Science.

Annual review of plant biology·2026
Same journal

RNA Meets Agriculture: From Molecular Mechanisms to Market Applications.

Annual review of plant biology·2026
Same journal

Sensing Plant Photosynthesis Using Solar-Induced Chlorophyll Fluorescence: From Chloroplasts to the Globe.

Annual review of plant biology·2026
Same journal

The Structure and Function of the Chloroplast Import Apparatus.

Annual review of plant biology·2026
Same journal

A Multidimensional View of Biomolecular Condensates in Plant Biology.

Annual review of plant biology·2026
See all related articles

Related Experiment Video

Updated: Jun 15, 2026

Kinematic Analysis of Cell Division and Expansion: Quantifying the Cellular Basis of Growth and Sampling Developmental Zones in Zea mays Leaves
08:31

Kinematic Analysis of Cell Division and Expansion: Quantifying the Cellular Basis of Growth and Sampling Developmental Zones in Zea mays Leaves

Published on: December 2, 2016

Computational morphodynamics: a modeling framework to understand plant growth.

Vijay Chickarmane1, Adrienne H K Roeder, Paul T Tarr

  • 1Division of Biology, California Institute Technology, Pasadena, California 91125, USA. vchickar@caltech.edu

Annual Review of Plant Biology
|March 3, 2010
PubMed
Summary
This summary is machine-generated.

Computational morphodynamics uses computer models to study organism development. This review explores challenges in modeling plant growth, integrating mechanics, signaling, and cell-to-tissue development for experimental validation.

More Related Videos

Robotic Sensing and Stimuli Provision for Guided Plant Growth
08:02

Robotic Sensing and Stimuli Provision for Guided Plant Growth

Published on: July 1, 2019

Live Cell Imaging of Microtubule Cytoskeleton and Micromechanical Manipulation of the Arabidopsis Shoot Apical Meristem
07:52

Live Cell Imaging of Microtubule Cytoskeleton and Micromechanical Manipulation of the Arabidopsis Shoot Apical Meristem

Published on: May 23, 2020

Related Experiment Videos

Last Updated: Jun 15, 2026

Kinematic Analysis of Cell Division and Expansion: Quantifying the Cellular Basis of Growth and Sampling Developmental Zones in Zea mays Leaves
08:31

Kinematic Analysis of Cell Division and Expansion: Quantifying the Cellular Basis of Growth and Sampling Developmental Zones in Zea mays Leaves

Published on: December 2, 2016

Robotic Sensing and Stimuli Provision for Guided Plant Growth
08:02

Robotic Sensing and Stimuli Provision for Guided Plant Growth

Published on: July 1, 2019

Live Cell Imaging of Microtubule Cytoskeleton and Micromechanical Manipulation of the Arabidopsis Shoot Apical Meristem
07:52

Live Cell Imaging of Microtubule Cytoskeleton and Micromechanical Manipulation of the Arabidopsis Shoot Apical Meristem

Published on: May 23, 2020

Area of Science:

  • Developmental Biology
  • Computational Biology
  • Biophysics

Background:

  • Computational morphodynamics integrates biological experiments with computer modeling to understand organism development.
  • Accurate and predictive models of growth are constructed from experimental data.
  • These models generate testable predictions, driving iterative refinement through experimentation.

Purpose of the Study:

  • To review computational approaches for modeling plant growth.
  • To highlight fundamental challenges in morphodynamics: feedback between growth mechanics and signaling, and integrating single-cell to tissue-level development.
  • To demonstrate how computational modeling and experimentation interplay to explore plant development.

Main Methods:

  • Utilizing computer modeling to simulate biological growth processes.
  • Integrating experimental results into predictive growth models.
  • Reviewing various modeling approaches for plant development.

Main Results:

  • Identified key challenges in computational morphodynamics: linking mechanics with signaling and bridging scales from cells to tissues.
  • Discussed diverse model types applicable to plant growth.
  • Showcased the synergistic relationship between computational modeling and experimentation.

Conclusions:

  • The interplay between computational modeling and experimentation is crucial for advancing the understanding of plant morphodynamics.
  • Addressing the identified challenges will enhance the predictive power of growth models.
  • Further research integrating mechanics, signaling, and multi-scale development is warranted.