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

You might also read

Related Articles

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

Sort by

Same author

Enhancing the performance and interpretability of epigenetic clocks.

Nucleic acids research·2026

Same author

Literature-informed gene extraction and ranking for multimodal data fusion.

Briefings in bioinformatics·2026

Same author

A Dataset of Benchmark Boolean Models for Gene Regulatory Networks.

Scientific data·2026

Same author

Genetic characteristics and targeted treatments of primary bladder and urachal adenocarcinomas: a systematic review with pooled descriptive genomic analyses.

Cancer metastasis reviews·2026

Same author

A Retrospective, Multicenter Analysis Within the National Network Genomic Medicine Lung Cancer in Germany to Detect RET Fusions as a Possible Mechanism of Resistance in Patients With EGFR Mutations.

Clinical lung cancer·2026

Same author

GPS1 Exon 9 Mutations Represent a Rare Genetic Event in Penile Squamous Cell Carcinoma Pathogenesis.

International journal of molecular sciences·2026

Search research articles

Related Experiment Video

Updated: Jun 7, 2025

Modeling Chemotherapy Resistant Leukemia In Vitro

Modeling Chemotherapy Resistant Leukemia In Vitro

Published on: February 9, 2016

Boolean network modeling and its integration with experimental read-outs : An interdisciplinary presentation using a

Julia Maier^1,2, Julian D Schwab¹, Silke D Werle¹

¹Institute of Medical Systems Biology, Ulm University, Ulm, Germany.

Pathologie (Heidelberg, Germany)

|November 13, 2024

Summary

Boolean network (BN) models offer an in silico strategy to streamline cancer research by analyzing molecular signaling pathways. This computational approach aids in identifying new drug targets and biomarkers, guiding experimental efforts in wet-laboratory settings.

Keywords:

Drug target screening Dynamic modeling Large regulatory networks Systems biology Tumor driver screening

More Related Videos

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

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

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds

Published on: April 6, 2016

Related Experiment Videos

Last Updated: Jun 7, 2025

Modeling Chemotherapy Resistant Leukemia In Vitro

Modeling Chemotherapy Resistant Leukemia In Vitro

Published on: February 9, 2016

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

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

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds

A Combined 3D Tissue Engineered In Vitro/In Silico Lung Tumor Model for Predicting Drug Effectiveness in Specific Mutational Backgrounds

Published on: April 6, 2016

Area of Science:

Computational Biology
Systems Biology
Cancer Research

Background:

Experimental cancer research faces limitations due to scarce animal models and cell lines, alongside time and cost constraints of wet-laboratory studies.
Molecular signaling pathways are complex, making it challenging to understand their dynamics and crosstalks through traditional experimental methods alone.

Purpose of the Study:

To present Boolean network (BN) models as an in silico strategy for streamlining experimental cancer research.
To demonstrate the application of BN models in analyzing molecular signaling pathways, guiding experimental design, and identifying potential therapeutic targets and biomarkers.
To illustrate the establishment, validation, and utilization of BN models in wet-laboratory research using a chronic lymphocytic leukemia (CLL) model.

Main Methods:

Development and validation of a specific tumor Boolean network (BN) model.
Dynamic analysis of large molecular signaling pathways and their crosstalks using BN models.
In silico screening for tumor drivers and drug targets within the established BN model.

Main Results:

BN models enable dynamic analysis of complex molecular signaling networks and their crosstalks.
In silico screenings can effectively identify potential intervention targets and biomarkers for tumor evolution.
The study demonstrates the practical application of BN modeling in guiding wet-laboratory experiments, using a chronic lymphocytic leukemia (CLL) BN model as a case study.

Conclusions:

Boolean network modeling provides a powerful in silico approach to overcome limitations in experimental cancer research.
BN models facilitate mechanistic insights into tumor cell behavior and accelerate the discovery of novel therapeutic strategies and biomarkers.
Integrating BN modeling with wet-laboratory research enhances efficiency and focuses experimental efforts for a deeper understanding of cancer biology.