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

Propagation of Uncertainty from Random Error

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...

Propagation of Uncertainty from Systematic Error

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...

Multimachine Stability

Multimachine Stability

Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:

Random and Systematic Errors

Random and Systematic Errors

Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...

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.

BIBO stability of continuous and discrete -time systems

BIBO stability of continuous and discrete -time systems

System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....

You might also read

Related Articles

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

Sort by

Same author

Channel Transport: Gating, Geometry, and Heterogeneous Diffusion.

Bulletin of mathematical biology·2026

Same author

Diffusive search for buried targets.

Physical chemistry chemical physics : PCCP·2026

Same author

Diffusive flux into a stochastically gated tube.

The Journal of chemical physics·2026

Same author

Mitigating antibiotic resistance from missed doses.

Journal of pharmacokinetics and pharmacodynamics·2026

Same author

Escape from a channel.

The Journal of chemical physics·2026

Same author

Passage times of fast inhomogeneous immigration processes.

Chaos (Woodbury, N.Y.)·2025

Search research articles

Related Experiment Video

Updated: Jun 28, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Understanding and Quantifying Network Robustness to Stochastic Inputs.

Hwai-Ray Tung¹, Sean D Lawley²

¹Department of Mathematics, University of Utah, Salt Lake City, UT, 84112, USA.

Bulletin of Mathematical Biology

|April 12, 2024

Summary

We developed a new statistic to measure how biological networks resist random fluctuations. This network robustness metric, based on random walk passage times, helps understand why systems maintain stable outputs despite noisy inputs.

Keywords:

Homeostasis Medication adherence Medication nonadherence Pharmacodynamics Pharmacokinetics Robustness

More Related Videos

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Related Experiment Videos

Last Updated: Jun 28, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

Published on: March 2, 2015

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Area of Science:

Biophysics
Systems Biology
Biochemistry

Background:

Biomedical systems are often modeled using differential equations with stochastic inputs.
Robustness, the ability to maintain constant output despite fluctuating inputs, is crucial in biological systems (e.g., biochemical networks, drug efficacy).
Understanding the mechanisms underlying network robustness to noise is a significant challenge due to complex network parameters and input types.

Purpose of the Study:

To propose a novel summary statistic for quantifying the robustness of linear differential equation networks (first-order mass-action systems).
To analyze how specific network motifs contribute to enhanced robustness.
To provide intuitive explanations for observed network robustness.

Main Methods:

Developed a summary statistic based on the variance of a specific random walk passage time on the network.
Applied the statistic to analyze networks of linear differential equations.
Utilized computational methods for rapid calculation on large-scale networks.
Proved theorems regarding the impact of network motifs on robustness.

Main Results:

The proposed statistic effectively quantifies network robustness to stochastic noise.
The statistic can be computed efficiently for complex networks with thousands of nodes.
Identified specific network motifs that demonstrably increase robustness.
The analysis provides clear insights into the sources of robustness in biological networks.

Conclusions:

The variance of random walk passage time is a powerful and computationally efficient metric for network robustness.
This approach offers valuable intuition into the design principles underlying robust biological systems.
The findings have implications for understanding and engineering biological and pharmacological systems.