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

387
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,...
387
BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

1.0K
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....
1.0K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

376
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
376
Classification of Systems-I01:26

Classification of Systems-I

647
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:
647
State Space Representation01:27

State Space Representation

645
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
645
Classification of Systems-II01:31

Classification of Systems-II

542
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
542

You might also read

Related Articles

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

Sort by
Same author

Continuous multi-omics pathway enrichment analysis resolves hidden functional heterogeneity.

Briefings in bioinformatics·2026
Same author

Transcriptional repression by TGIF2 coordinates neurogenic priming and neural stem cell maintenance.

Science advances·2026
Same author

UniversalEPI: robust prediction of cell type-specific and differential chromatin interactions from DNA sequence and chromatin accessibility.

Nucleic acids research·2026
Same author

RegVelo: Gene-regulatory-informed dynamics of single cells.

Cell·2026
Same author

Glial multicellular programs reveal distinct patient stratification in Parkinson's disease.

Research square·2026
Same author

TarDis: Achieving robust and structured disentanglement of multiple covariates.

Cell systems·2026
Same journal

Biomedical Concept Recognition with Error-aware Negative-enhanced Ranking Framework.

Bioinformatics (Oxford, England)·2026
Same journal

TEDLH: Domain HMMs for sensitive detection of remote homologues.

Bioinformatics (Oxford, England)·2026
Same journal

PLNFGL: Joint Estimation of Multi-Condition Gene Networks from Single-cell RNA-seq Data.

Bioinformatics (Oxford, England)·2026
Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026
Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026
Same journal

EDEL: Enhancing Dense Retrievers for Curation of Biomedical Knowledge Bases.

Bioinformatics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: Mar 9, 2026

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis
06:44

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis

Published on: September 23, 2025

658

Parameter estimation for dynamical systems with discrete events and logical operations.

Fabian Fröhlich1,2, Fabian J Theis1,2, Joachim O Rädler3

  • 1Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg 85764, Germany.

Bioinformatics (Oxford, England)
|January 2, 2017
PubMed
Summary
This summary is machine-generated.

We developed new methods for parameter estimation in ordinary differential equation (ODE) models with events, improving computational efficiency and robustness. This enables the use of event-resolved data for more accurate model calibration in systems biology.

More Related Videos

Interactive and Visualized Online Experimentation System for Engineering Education and Research
08:35

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

3.0K

Related Experiment Videos

Last Updated: Mar 9, 2026

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis
06:44

Age-dependent Dynamics of Locomotion in Caenorhabditis elegans: A Lyapunov Exponent Analysis

Published on: September 23, 2025

658
Interactive and Visualized Online Experimentation System for Engineering Education and Research
08:35

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

3.0K

Area of Science:

  • Systems Biology
  • Computational Biology
  • Biochemical Modeling

Background:

  • Ordinary differential equation (ODE) models are crucial for describing biochemical dynamics.
  • Events are often added to ODE models to represent fast latent processes.
  • Parameter estimation in ODE models is essential for predictive power, but existing methods struggle with event-dependent dynamics.

Purpose of the Study:

  • To describe sensitivity equations for ODE models with events.
  • To enable parameter estimation from event-resolved data using event-triggered observations.
  • To improve computational efficiency and robustness of parameter estimation for complex models.

Main Methods:

  • Derived and implemented sensitivity equations for ODE models incorporating parameter- and state-dependent events.
  • Developed methods for parameter estimation utilizing event-triggered observations.
  • Integrated these methods into the Advanced MATLAB Interface for CVODES and IDAS (AMICI) toolbox.

Main Results:

  • Demonstrated improved computational efficiency and robustness in parameter estimation for models with events.
  • Successfully utilized event-resolved data, such as time-to-event data, for parameter estimation.
  • Validated the approach using models for GFP expression and spiking neurons.

Conclusions:

  • The developed methods enhance parameter estimation for ODE models with events.
  • Event-resolved data can be effectively incorporated into parameter estimation.
  • The open-source AMICI toolbox provides a user-friendly implementation for broader accessibility.