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Related Concept Videos

Mechanistic Models: Overview of Compartment Models01:21

Mechanistic Models: Overview of Compartment Models

385
Mechanistic models, a category encompassing both physiological and compartmental modeling, differ from empirical models' approaches to incorporating known factors about the systems being modeled. Empirical models describe data with minimal assumptions, while mechanistic models aim to provide a robust description of available data by specifying assumptions and integrating known factors about the system. Compartmental analysis is a key example of a mechanistic model in pharmacokinetics and...
385
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
270
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

319
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...
319
Second Order systems II01:18

Second Order systems II

411
In an underdamped second-order system, where the damping ratio ζ is between 0 and 1, a unit-step input results in a transfer function that, when transformed using the inverse Laplace method, reveals the output response. The output exhibits a damped sinusoidal oscillation, and the difference between the input and output is termed the error signal. This error signal also demonstrates damped oscillatory behavior. Eventually, as the system reaches a steady state, the error diminishes to zero.
411
First Order Systems01:21

First Order Systems

433
First-order systems, such as RC circuits, are foundational in understanding dynamic systems due to their straightforward input-output relationship. Analyzing their responses to different input functions under zero initial conditions reveals significant insights into system behavior.
When a first-order system is subjected to a unit-step input, its response is characterized by its transfer function. By applying the Laplace transform of the unit-step input to the transfer function, expanding the...
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Second Order systems I01:20

Second Order systems I

603
A servo system exemplifies a second-order system, featuring a proportional controller and load elements that ensure the output position aligns with the input position. The relationship between these components is described by a second-order differential equation. Applying the Laplace transform under zero initial conditions yields the transfer function, showing how inputs are converted to outputs in the system.
By reinterpreting the system, one can derive the closed-loop transfer function, which...
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Related Experiment Video

Updated: Feb 8, 2026

Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information
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Human-based systems: Mechanistic NASH modelling just around the corner?

Joost Boeckmans1, Alessandra Natale1, Karolien Buyl1

  • 1Department of In VitroToxicology & Dermato-Cosmetology (IVTD) Faculty of Medicine and Pharmacy, Vrije Universiteit Brussel, Laarbeeklaan 103, 1090 Brussels, Belgium.

Pharmacological Research
|July 3, 2018
PubMed
Summary

Non-alcoholic steatohepatitis (NASH) is a prevalent liver disease lacking approved treatments. Human-based models offer a more accurate approach for developing new NASH therapies compared to traditional animal studies.

Keywords:
Disease modellingDrug targetsElafibranor (PubChem CID: 9864881)Fenofibrate (PubChem CID: 3339)Human-based alternative methodsNon-alcoholic fatty liver disease (NAFLD)Non-alcoholic steatophepatitis (NASH)Obeticholic acid (PubChem CID: 447715)Pioglitazone (PubChem CID: 4829)Rosiglitazone (PubChem CID: 77999)Serine (PubChem CID: 5951)

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Area of Science:

  • Hepatology and Drug Development
  • Molecular Pathogenesis of Liver Disease

Background:

  • Non-alcoholic steatohepatitis (NASH) affects up to 5% of the global population, characterized by liver fat accumulation, inflammation, and cell stress.
  • NASH represents a critical progression from non-alcoholic fatty liver disease (NAFLD) towards severe liver conditions.
  • Currently, no drugs are approved for NASH treatment, highlighting an urgent need for effective therapeutic strategies.

Purpose of the Study:

  • To review the molecular underpinnings of NASH pathogenesis.
  • To evaluate the utility of human-based research tools in understanding NASH.
  • To explore the application of these tools in developing novel anti-NASH drugs.

Main Methods:

  • Literature review focusing on NASH molecular components and pathogenesis.
  • Analysis of human-based in vitro models for NASH research.
  • Evaluation of in silico and pathway-based approaches using human datasets.
  • Discussion of limitations of current animal models in NASH drug development.

Main Results:

  • Human-based in vitro models provide a more accurate representation of human NASH pathophysiology than animal models.
  • In silico and pathway-based methods using human data can enhance NASH modeling.
  • These human-centric approaches are crucial for investigating cellular dysregulation in NASH.

Conclusions:

  • Human-based tools, including in vitro models and computational methods, are essential for accurate NASH research and drug development.
  • Developing and applying these human-centric approaches will accelerate the discovery of effective anti-NASH therapies.
  • A roadmap for utilizing human-based approaches in future NASH investigations is proposed.