Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Pollination and Flower Structure

Pollination and Flower Structure

Flowers are the reproductive, seed-producing structures of angiosperms. Typically, flowers consist of sepals, petals, stamens, and carpels. Sepals and petals are the vegetative flower organs. Stamens and carpels are the reproductive organs.

You might also read

Related Articles

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

Sort by

Same author

FastTENET: an accelerated TENET algorithm based on manycore computing in Python.

Bioinformatics (Oxford, England)·2024

Same author

A Novel Framework for Melanoma Lesion Segmentation Using Multiparallel Depthwise Separable and Dilated Convolutions with Swish Activations.

Journal of healthcare engineering·2023

Same author

Nezzle: an interactive and programmable visualization of biological networks in Python.

Bioinformatics (Oxford, England)·2022

See all related articles

Search research articles

Related Experiment Video

Updated: Jul 24, 2025

In situ Protocol for Butterfly Pupal Wings Using Riboprobes

In situ Protocol for Butterfly Pupal Wings Using Riboprobes

Published on: May 28, 2007

LPF: a framework for exploring the wing color pattern formation of ladybird beetles in Python.

¹School of Art and Technology, College of Art and Technology, Chung-Ang University, Anseong 17546, Republic of Korea.

Bioinformatics (Oxford, England)

|July 8, 2023

Summary

This study introduces LPF, a Python framework for exploring ladybird wing patterns using reaction-diffusion models and advanced computing. It aids in understanding biological pattern formation through mathematical modeling and simulations.

More Related Videos

At-Risk Butterfly Captive Propagation Programs to Enhance Life History Knowledge and Effective Ex Situ Conservation Techniques

At-Risk Butterfly Captive Propagation Programs to Enhance Life History Knowledge and Effective Ex Situ Conservation Techniques

Published on: February 11, 2020

Methods for Staging Pupal Periods and Measurement of Wing Pigmentation of Drosophila guttifera

Methods for Staging Pupal Periods and Measurement of Wing Pigmentation of Drosophila guttifera

Published on: January 24, 2018

Related Experiment Videos

Last Updated: Jul 24, 2025

In situ Protocol for Butterfly Pupal Wings Using Riboprobes

In situ Protocol for Butterfly Pupal Wings Using Riboprobes

Published on: May 28, 2007

At-Risk Butterfly Captive Propagation Programs to Enhance Life History Knowledge and Effective Ex Situ Conservation Techniques

At-Risk Butterfly Captive Propagation Programs to Enhance Life History Knowledge and Effective Ex Situ Conservation Techniques

Published on: February 11, 2020

Methods for Staging Pupal Periods and Measurement of Wing Pigmentation of Drosophila guttifera

Methods for Staging Pupal Periods and Measurement of Wing Pigmentation of Drosophila guttifera

Published on: January 24, 2018

Area of Science:

Complex Systems
Computational Biology
Developmental Biology

Background:

Biological pattern formation is a complex phenomenon.
Understanding these patterns requires mathematical modeling and computer simulations.
Ladybird wing coloration exhibits significant diversity.

Purpose of the Study:

To introduce LPF, a Python framework for exploring ladybird wing color patterns.
To utilize reaction-diffusion models for simulating pattern formation.
To facilitate in-depth understanding of biological pattern diversity.

Main Methods:

Developed a Python framework (LPF) for systematic exploration of patterns.
Employed reaction-diffusion models to simulate wing color patterns.
Integrated GPU-accelerated computing, visualization tools, and evolutionary algorithms with deep learning for model searching.

Main Results:

LPF enables efficient numerical analysis of partial differential equations.
The framework facilitates concise visualization of various ladybird morphs.
It supports searching for mathematical models using evolutionary and deep learning approaches.

Conclusions:

LPF provides a powerful tool for studying biological pattern formation.
The framework enhances the exploration of ladybird wing color diversity.
It integrates advanced computational techniques for theoretical analysis.