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Related Concept Videos

Combinatorial Gene Control02:33

Combinatorial Gene Control

Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...
Open and closed-loop control systems01:17

Open and closed-loop control systems

Control systems are foundational elements in automation and engineering. They are broadly categorized into open-loop and closed-loop systems. These classifications hinge on the presence or absence of feedback mechanisms, significantly influencing the system's performance, complexity, and application.
An open-loop control system operates without feedback from the output. It consists of two primary elements: the controller and the controlled process. The controller receives an input signal and...
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.
Time and frequency -Domain Interpretation of PI Control01:27

Time and frequency -Domain Interpretation of PI Control

Proportional-Integral (PI) controllers are essential in many control systems to improve stability and performance. They are commonly used in everyday devices like thermostats to enhance system damping and reduce steady-state error. When the zero in the controller's transfer function is optimally placed, the system benefits significantly in terms of stability and accuracy.
Acting as a low-pass filter, the PI controller slows the system's response and extends settling times. This requires careful...
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Control Systems01:10

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Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
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Related Experiment Videos

Optimal control for probabilistic Boolean networks.

Q Liu1, X Guo, T Zhou

  • 1Zhongshan University, The School of Mathematics and Computational Science, Guangzhou, People's Republic of China.

IET Systems Biology
|March 18, 2010
PubMed
Summary
This summary is machine-generated.

This study introduces a novel gene control strategy using Markov decision processes to minimize undesirable gene states during cell cycles. The method optimizes gene intervention to maximize the probability of reaching desired cellular states.

Related Experiment Videos

Area of Science:

  • Systems Biology
  • Computational Biology
  • Genetics

Background:

  • Aberrant gene functions are linked to disease pathology.
  • Controlling gene regulatory networks aims to prevent undesirable cellular phenotypes.
  • Minimizing the probability of genes entering undesirable states during cell cycles is crucial.

Purpose of the Study:

  • To develop an optimal control strategy for gene intervention models.
  • To address the challenge of controlling gene networks to avoid undesirable states.
  • To maximize the probability of reaching desired gene states within a cell cycle.

Main Methods:

  • Utilizing first passage time theory for discrete-time Markov decision processes.
  • Modeling gene interactions using a probabilistic Boolean network control model.
  • Solving an optimal control problem to maximize the probability of first arrival to desirable states.

Main Results:

  • The proposed method provides a framework for optimal gene network control.
  • Demonstrated the effectiveness of the approach through illustrative examples.
  • Quantified the probability of reaching desired gene states and avoiding undesirable ones.

Conclusions:

  • The developed optimal control strategy effectively minimizes undesirable gene states.
  • The first passage model for Markov decision processes is a valid approach for gene intervention.
  • This research offers a computational method for precise control of gene regulatory networks.