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Related Concept Videos

Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
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...
Constraints and Statical Determinacy01:26

Constraints and Statical Determinacy

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Mathematical Modeling: Problem Solving01:29

Mathematical Modeling: Problem Solving

Mathematical modeling transforms real-world scenarios into mathematical expressions, allowing for structured problem-solving and analysis. This process involves defining the situation, assigning variables to measurable quantities, selecting an appropriate model, and solving the resulting equation. Such models are invaluable in finance, providing precise methods to evaluate investments, loans, and repayment structures.A widely used example is the calculation of fixed monthly payments on a loan,...
Application of Nonlinear Inequalities01:29

Application of Nonlinear Inequalities

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Decision Making: P-value Method

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Related Experiment Videos

Finding optimal control policy in probabilistic Boolean Networks with hard constraints by using integer programming

Xi Chen1, Tatsuya Akutsu, Takeyuki Tamura

  • 1Advanced Modelling and Applied Computing Laboratory, Department of Mathematics, The University of Hong Kong, Pokfulam Road, Hong Kong. dlkcissy@hku.hk

International Journal of Data Mining and Bioinformatics
|July 4, 2013
PubMed
Summary
This summary is machine-generated.

This study explores control strategies for Boolean Networks (BNs) and Probabilistic Boolean Networks (PBNs). We developed methods to steer BNs to desired states quickly and PBNs to desired states with high probability, while minimizing costs.

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Control Theory

Background:

  • Boolean Networks (BNs) and Probabilistic Boolean Networks (PBNs) are widely used models for complex biological systems.
  • Control problems in these networks involve manipulating their dynamics to achieve specific outcomes.

Purpose of the Study:

  • To investigate control strategies for Boolean Networks (BNs) and Probabilistic Boolean Networks (PBNs).
  • To develop methods for driving BNs to desired states efficiently.
  • To devise techniques for maximizing the probability of reaching desired states in PBNs and minimizing associated costs.

Main Methods:

  • External control application for BN state derivation.
  • Control sequence design for PBNs to achieve probabilistic state termination.
  • Cost minimization for PBN terminal states.

Main Results:

  • A control method for BNs to reach target states in minimal time steps.
  • A strategy for PBN control to maximize the probability of reaching a desired state.
  • An approach to minimize the maximum cost of PBN terminal states.
  • A hardness result indicating PBN control is more complex than BN control.

Conclusions:

  • Effective control strategies can be designed for both BNs and PBNs.
  • PBN control presents greater computational challenges compared to BN control.
  • These findings contribute to the understanding and application of control theory in biological network modeling.