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Related Concept Videos

Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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

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.
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
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Operon Model01:23

Operon Model

The operon model represents a fundamental mechanism of gene regulation in prokaryotes, enabling coordinated expression of genes involved in related metabolic or functional pathways. Operons consist of structural genes, a promoter, and an operator, with transcription regulated by repressors, activators, and small effector molecules.Structure and Function of OperonsAn operon is a cluster of structural genes transcribed together under the control of a single promoter. The promoter region...
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Mechanistic Models: Overview of Compartment Models

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...

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Updated: May 15, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Published on: December 7, 2021

A Realistic Control Approach for Set Stabilization of Complex Biological Networks With Logical Models.

Jung-Min Yang, Namhee Kim, Kwang-Hyun Cho

    IEEE Transactions on Computational Biology and Bioinformatics
    |May 13, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel control method for stabilizing complex biological networks modeled as Boolean networks (BNs). The approach uses feedback vertex set (FVS) control to guide systems toward desired long-term behaviors, including fixed-point and cyclic attractors.

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    A Multilayer Microfluidic Platform for the Conduction of Prolonged Cell-Free Gene Expression

    Published on: October 6, 2019

    Area of Science:

    • Systems Biology
    • Control Theory
    • Computational Biology

    Background:

    • Controlling cellular biological systems towards desired long-term behaviors is a key challenge in systems biology, often framed as the set stabilization problem.
    • Boolean networks (BNs) are widely used to model complex biological regulatory networks, but controlling their dynamics remains difficult.

    Purpose of the Study:

    • To present a realistic open-loop control approach for set stabilization of complex biological networks modeled by Boolean networks (BNs).
    • To develop a recursive method using feedback vertex set (FVS) control to guide BNs towards desired attractors, accommodating both fixed-point and cyclic behaviors.

    Main Methods:

    • Transformation of the original BN into an auxiliary BN with redundant state variables.
    • Construction of reduced BNs from the auxiliary BN, leveraging the relationship between attractors and Boolean algebra.
    • Application of the feedback vertex set (FVS) control law recursively to achieve open-loop control inputs for state variable stabilization.

    Main Results:

    • The proposed scheme enables set stabilization of complex BNs using only open-loop control.
    • The method successfully handles both fixed-point and cyclic attractors within the BN model.
    • Demonstrated applicability through extensive numerical experiments on random BNs and real biological systems, showing moderate computational requirements.

    Conclusions:

    • The developed control strategy offers an effective and computationally feasible approach for steering complex biological systems towards desired stable states.
    • This method advances the field of systems biology by providing a robust tool for analyzing and manipulating biological network dynamics.