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

The Uncertainty Principle

The Uncertainty Principle

Werner Heisenberg considered the limits of how accurately one can measure properties of an electron or other microscopic particles. He determined that there is a fundamental limit to how accurately one can measure both a particle’s position and its momentum simultaneously. The more accurate the measurement of the momentum of a particle is known, the less accurate the position at that time is known and vice versa. This is what is now called the Heisenberg uncertainty principle. He...

Uncertainty in Measurement: Reading Instruments

Uncertainty in Measurement: Reading Instruments

Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...

Uncertainty: Overview

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.

Uncertainty in Measurement: Significant Figures

Uncertainty in Measurement: Significant Figures

All the digits in a measurement, including the uncertain last digit, are called significant figures or significant digits. Note that zero may be a measured value; for example, if a scale that shows weight to the nearest pound reads “140,” then the 1 (hundreds), 4 (tens), and 0 (ones) are all significant (measured) values.

Uncertainty: Confidence Intervals

Uncertainty: Confidence Intervals

The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...

Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

You might also read

Related Articles

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

Sort by

Same author

Immune evasion in locally advanced mismatch repair-deficient microsatellite instability-high colorectal cancer: Reduced T-cell infiltration and upregulation of epithelial IDO1 expression.

Oncoimmunology·2026

Same author

Tumor mutations predict HER2-targeted therapy resistance in primary HER2-positive breast cancer.

NPJ breast cancer·2026

Same author

BRAF<sup>V600E</sup> patient derived colon cancer organoids identify biomarkers of response to EGFR and BRAF inhibition and replicate clinical data.

Journal of experimental & clinical cancer research : CR·2026

Same author

Proteomic profiling of colorectal liver metastases reveals histopathological response-specific molecular signatures of chemotherapy efficacy.

Journal of translational medicine·2026

Same author

Cancer evolution and multi-omic profile of relapsed colorectal liver metastases after treatment.

Genome medicine·2026

Same author

Node and edge control strategy identification via trap spaces in Boolean networks.

BMC bioinformatics·2025

Search research articles

Related Experiment Video

Updated: Feb 3, 2026

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

Published on: September 7, 2019

Evaluating Uncertainty in Signaling Networks Using Logical Modeling.

Kirsten Thobe^1,2, Christina Kuznia^3,4, Christine Sers³

¹Group for Discrete Biomathematics, Department for Mathematics and Computer Science, Freie Universität Berlin, Berlin, Germany.

Frontiers in Physiology

|October 27, 2018

Summary

This summary is machine-generated.

This study introduces a new computational workflow for systems biology that integrates prior knowledge with data-driven uncertainty. The approach revealed distinct cellular wiring differences between two renal cancer cell lines, explaining their varied responses to Sorafenib.

Keywords:

constraint based modeling logical modeling model checking signaling pathways systems biology

More Related Videos

Evaluating Cell Death Signaling by Immunofluorescence in a Rat Model of Ischemic Stroke

Evaluating Cell Death Signaling by Immunofluorescence in a Rat Model of Ischemic Stroke

Published on: January 3, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Related Experiment Videos

Last Updated: Feb 3, 2026

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

Split Point Analysis and Uncertainty Quantification of Thermal-Optical Organic/Elemental Carbon Measurements

Published on: September 7, 2019

Evaluating Cell Death Signaling by Immunofluorescence in a Rat Model of Ischemic Stroke

Evaluating Cell Death Signaling by Immunofluorescence in a Rat Model of Ischemic Stroke

Published on: January 3, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

Area of Science:

Systems biology
Computational biology
Cancer research

Background:

Traditional systems biology models struggle to incorporate prior knowledge and uncertainty.
Bottom-up models lack uncertainty handling, while top-down models ignore existing biological information.

Purpose of the Study:

To develop a novel computational workflow integrating prior knowledge and uncertainty for biological modeling.
To analyze differences in growth factor signaling pathways between two renal cancer cell lines with distinct Sorafenib responses.
To investigate the impact of Sorafenib targets, pathway crosstalk, and mTOR mutation on cellular behavior.

Main Methods:

Developed a workflow combining logical modeling for uncertainty with data-driven filtering.
Created specific templates for signaling network analysis.
Applied the pipeline to two renal cancer cell lines with differing Sorafenib sensitivity.
Analyzed uncertainties including drug targets, pathway crosstalk, and mTOR mutation.

Main Results:

The computational models for the two cell lines were distinct, indicating differences in cellular wiring.
The mutation in mammalian target of Rapamycin (mTOR) did not significantly affect its pathway activity.
The analysis provided insights into Sorafenib's differential effects but did not fully elucidate the mechanisms.

Conclusions:

The developed workflow effectively integrates prior knowledge and uncertainty in biological modeling.
Discrepancies in renal cancer cell line behavior towards Sorafenib are attributed to differences in cellular wiring, not mTOR mutation.
This approach facilitates hypothesis generation for complex biological questions in cancer research.