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

Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

420
Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length,...
420
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

1.6K
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...
1.6K
Linear Approximation in Frequency Domain01:26

Linear Approximation in Frequency Domain

433
Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear....
433
Linear time-invariant Systems01:23

Linear time-invariant Systems

1.1K
A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be...
1.1K
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

2.2K
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...
2.2K
Classification of Systems-I01:26

Classification of Systems-I

671
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
671

You might also read

Related Articles

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

Sort by
Same author

From FAIR to CURE: guidelines for computational models of biological systems.

NPJ systems biology and applications·2026
Same author

A comparative computational analysis of IFN-alpha pharmacokinetics and its induced cellular response in mice and humans.

PLoS computational biology·2025
Same author

Regulation of PV interneuron plasticity by neuropeptide-encoding genes.

Nature·2025
Same author

From FAIR to CURE: Guidelines for Computational Models of Biological Systems.

ArXiv·2025
Same author

The cytochrome P450 subfamilies CYP392A and CYP392D are key players in acaricide metabolism in Tetranychusurticae.

Pesticide biochemistry and physiology·2024
Same author

A meta-analysis of genetic and phenotypic diversity of European local pig breeds reveals genomic regions associated with breed differentiation for production traits.

Genetics, selection, evolution : GSE·2023

Related Experiment Video

Updated: Apr 3, 2026

Image-based Lagrangian Particle Tracking in Bed-load Experiments
10:32

Image-based Lagrangian Particle Tracking in Bed-load Experiments

Published on: July 20, 2017

9.6K

Deterministic inference for stochastic systems using multiple shooting and a linear noise approximation for the

Christoph Zimmer1, Sven Sahle2

  • 1BioQuant, University of Heidelberg, Im Neuenheimer Feld 267, 69120 Heidelberg, Germany. christoph.zimmer@bioquant.uni-heidelberg.de.

IET Systems Biology
|September 26, 2015
PubMed
Summary

This study enhances parameter estimation for stochastic models using an extended multiple shooting for stochastic systems (MSS) method. The approach accurately estimates parameters, even with intrinsic stochasticity, at speeds comparable to traditional methods.

More Related Videos

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

3.1K

Related Experiment Videos

Last Updated: Apr 3, 2026

Image-based Lagrangian Particle Tracking in Bed-load Experiments
10:32

Image-based Lagrangian Particle Tracking in Bed-load Experiments

Published on: July 20, 2017

9.6K
A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

3.1K

Area of Science:

  • Systems Biology
  • Computational Modeling
  • Biophysics

Background:

  • Parameter estimation is vital for computational models in systems biology.
  • Stochastic models are increasingly important, driving the need for advanced estimation methods.
  • Existing methods may struggle with the intrinsic stochasticity of these models.

Purpose of the Study:

  • To present an extension of the multiple shooting for stochastic systems (MSS) method for parameter estimation.
  • To improve the handling of intrinsic stochasticity in parameter estimation for stochastic models.
  • To offer a computationally efficient alternative to existing methods.

Main Methods:

  • Approximation of transition probabilities using normal distributions.
  • Linear noise approximation to calculate means and variances over short intervals.
  • Application of the extended MSS method to systems with intrinsic stochasticity.

Main Results:

  • The extended MSS method successfully estimates parameters, particularly in scenarios with significant intrinsic stochasticity.
  • Comparison with reversible jump techniques shows no loss of accuracy.
  • The method achieves computational speeds comparable to conventional least-squares estimation.

Conclusions:

  • The enhanced MSS method provides an accurate and efficient approach for parameter estimation in stochastic systems biology models.
  • This technique effectively addresses challenges posed by intrinsic stochasticity.
  • The method offers a viable alternative for researchers working with complex stochastic models.