The Experts below are selected from a list of 19740 Experts worldwide ranked by ideXlab platform
Jorge Gomez Tejeda Zanudo - One of the best experts on this subject based on the ideXlab platform.
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target control in Logical models using the Domain of influence of nodes
Frontiers in Physiology, 2018Co-Authors: Gang Yang, Jorge Gomez Tejeda Zanudo, Reka AlbertAbstract:Dynamical models of biomolecular networks are successfully used to understand the mechanisms underlying complex diseases and to design therapeutic strategies. Network control, and its special case of target control, is a promising avenue toward developing disease therapies. In target control it is assumed that a small subset of nodes is most relevant to the system's state and the goal is to drive the target nodes into their desired states. An example of target control would be driving a cell to commit to apoptosis (programmed cell death). From the experimental perspective, gene knockout, pharmacoLogical inhibition of proteins and providing sustained external signals are among practical intervention techniques. We identify methodologies to use the stabilizing effect of sustained interventions for target control in Boolean network models of biomolecular networks. Specifically, we define the Domain of influence of a node (in a certain state) to be the nodes (and their corresponding states) that will be ultimately stabilized by the sustained state of this node regardless of the initial state of the system. We also define the related concept of the Logical Domain of influence of a node, and develop an algorithm for its identification using an auxiliary network that incorporates the regulatory logic. This way a solution to the target control problem is a set of nodes whose Domain of influence can cover the desired target node states. We perform greedy randomized adaptive search in node state space to find such solutions. We apply our strategy to in silico bioLogical network models of real systems to demonstrate its effectiveness. ffectiveness.
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target control in Logical models using the Domain of influence of nodes
bioRxiv, 2018Co-Authors: Gang Yang, Jorge Gomez Tejeda Zanudo, Reka AlbertAbstract:ABSTRACT Dynamical models of biomolecular networks are successfully used to understand the mechanisms underlying complex diseases and to design therapeutic strategies. Network control, and its special case of target control, is a promising avenue toward developing disease therapies. In target control it is assumed that a small subset of nodes is most relevant to the system’s state and the goal is to drive the target nodes into their desired states. An example of target control would be driving a cell to commit to apoptosis (programmed cell death). From the experimental perspective, gene knockout, pharmacoLogical inhibition of proteins and providing sustained external signals are among practical intervention techniques. We identify methodologies to use the stabilizing effect of sustained interventions for target control in Logical models of biomolecular networks. Specifically, we define the Domain of influence of a node (in a certain state) to be the nodes (and their corresponding states) that will be ultimately stabilized by the sustained state of this node regardless of the initial state of the system. We also define the related concept of the Logical Domain of influence of a node, and develop an algorithm for its identification using an auxiliary network that incorporates the regulatory logic. This way a solution to the target control problem is a set of nodes whose Domain of influence can cover the desired target node states. We perform greedy randomized adaptive search in state space to find such solutions. We apply our strategy to several bioLogical networks to demonstrate its effectiveness.
Reka Albert - One of the best experts on this subject based on the ideXlab platform.
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target control in Logical models using the Domain of influence of nodes
Frontiers in Physiology, 2018Co-Authors: Gang Yang, Jorge Gomez Tejeda Zanudo, Reka AlbertAbstract:Dynamical models of biomolecular networks are successfully used to understand the mechanisms underlying complex diseases and to design therapeutic strategies. Network control, and its special case of target control, is a promising avenue toward developing disease therapies. In target control it is assumed that a small subset of nodes is most relevant to the system's state and the goal is to drive the target nodes into their desired states. An example of target control would be driving a cell to commit to apoptosis (programmed cell death). From the experimental perspective, gene knockout, pharmacoLogical inhibition of proteins and providing sustained external signals are among practical intervention techniques. We identify methodologies to use the stabilizing effect of sustained interventions for target control in Boolean network models of biomolecular networks. Specifically, we define the Domain of influence of a node (in a certain state) to be the nodes (and their corresponding states) that will be ultimately stabilized by the sustained state of this node regardless of the initial state of the system. We also define the related concept of the Logical Domain of influence of a node, and develop an algorithm for its identification using an auxiliary network that incorporates the regulatory logic. This way a solution to the target control problem is a set of nodes whose Domain of influence can cover the desired target node states. We perform greedy randomized adaptive search in node state space to find such solutions. We apply our strategy to in silico bioLogical network models of real systems to demonstrate its effectiveness. ffectiveness.
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target control in Logical models using the Domain of influence of nodes
bioRxiv, 2018Co-Authors: Gang Yang, Jorge Gomez Tejeda Zanudo, Reka AlbertAbstract:ABSTRACT Dynamical models of biomolecular networks are successfully used to understand the mechanisms underlying complex diseases and to design therapeutic strategies. Network control, and its special case of target control, is a promising avenue toward developing disease therapies. In target control it is assumed that a small subset of nodes is most relevant to the system’s state and the goal is to drive the target nodes into their desired states. An example of target control would be driving a cell to commit to apoptosis (programmed cell death). From the experimental perspective, gene knockout, pharmacoLogical inhibition of proteins and providing sustained external signals are among practical intervention techniques. We identify methodologies to use the stabilizing effect of sustained interventions for target control in Logical models of biomolecular networks. Specifically, we define the Domain of influence of a node (in a certain state) to be the nodes (and their corresponding states) that will be ultimately stabilized by the sustained state of this node regardless of the initial state of the system. We also define the related concept of the Logical Domain of influence of a node, and develop an algorithm for its identification using an auxiliary network that incorporates the regulatory logic. This way a solution to the target control problem is a set of nodes whose Domain of influence can cover the desired target node states. We perform greedy randomized adaptive search in state space to find such solutions. We apply our strategy to several bioLogical networks to demonstrate its effectiveness.
Gang Yang - One of the best experts on this subject based on the ideXlab platform.
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target control in Logical models using the Domain of influence of nodes
Frontiers in Physiology, 2018Co-Authors: Gang Yang, Jorge Gomez Tejeda Zanudo, Reka AlbertAbstract:Dynamical models of biomolecular networks are successfully used to understand the mechanisms underlying complex diseases and to design therapeutic strategies. Network control, and its special case of target control, is a promising avenue toward developing disease therapies. In target control it is assumed that a small subset of nodes is most relevant to the system's state and the goal is to drive the target nodes into their desired states. An example of target control would be driving a cell to commit to apoptosis (programmed cell death). From the experimental perspective, gene knockout, pharmacoLogical inhibition of proteins and providing sustained external signals are among practical intervention techniques. We identify methodologies to use the stabilizing effect of sustained interventions for target control in Boolean network models of biomolecular networks. Specifically, we define the Domain of influence of a node (in a certain state) to be the nodes (and their corresponding states) that will be ultimately stabilized by the sustained state of this node regardless of the initial state of the system. We also define the related concept of the Logical Domain of influence of a node, and develop an algorithm for its identification using an auxiliary network that incorporates the regulatory logic. This way a solution to the target control problem is a set of nodes whose Domain of influence can cover the desired target node states. We perform greedy randomized adaptive search in node state space to find such solutions. We apply our strategy to in silico bioLogical network models of real systems to demonstrate its effectiveness. ffectiveness.
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target control in Logical models using the Domain of influence of nodes
bioRxiv, 2018Co-Authors: Gang Yang, Jorge Gomez Tejeda Zanudo, Reka AlbertAbstract:ABSTRACT Dynamical models of biomolecular networks are successfully used to understand the mechanisms underlying complex diseases and to design therapeutic strategies. Network control, and its special case of target control, is a promising avenue toward developing disease therapies. In target control it is assumed that a small subset of nodes is most relevant to the system’s state and the goal is to drive the target nodes into their desired states. An example of target control would be driving a cell to commit to apoptosis (programmed cell death). From the experimental perspective, gene knockout, pharmacoLogical inhibition of proteins and providing sustained external signals are among practical intervention techniques. We identify methodologies to use the stabilizing effect of sustained interventions for target control in Logical models of biomolecular networks. Specifically, we define the Domain of influence of a node (in a certain state) to be the nodes (and their corresponding states) that will be ultimately stabilized by the sustained state of this node regardless of the initial state of the system. We also define the related concept of the Logical Domain of influence of a node, and develop an algorithm for its identification using an auxiliary network that incorporates the regulatory logic. This way a solution to the target control problem is a set of nodes whose Domain of influence can cover the desired target node states. We perform greedy randomized adaptive search in state space to find such solutions. We apply our strategy to several bioLogical networks to demonstrate its effectiveness.
K L Cartwright - One of the best experts on this subject based on the ideXlab platform.
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an arbitrary curvilinear coordinate method for particle in cell modeling
arXiv: Plasma Physics, 2012Co-Authors: C A Fichtl, John M Finn, K L CartwrightAbstract:A new approach to the kinetic simulation of plasmas in complex geometries, based on the Particle-in- Cell (PIC) simulation method, is explored. In the two dimensional (2d) electrostatic version of our method, called the Arbitrary Curvilinear Coordinate PIC (ACC-PIC) method, all essential PIC operations are carried out in 2d on a uniform grid on the unit square Logical Domain, and mapped to a nonuniform boundary-fitted grid on the physical Domain. As the resulting Logical grid equations of motion are not separable, we have developed an extension of the semi-implicit Modified Leapfrog (ML) integration technique to preserve the symplectic nature of the Logical grid particle mover. A generalized, curvilinear coordinate formulation of Poisson's equations to solve for the electrostatic fields on the uniform Logical grid is also developed. By our formulation, we compute the plasma charge density on the Logical grid based on the particles' positions on the Logical Domain. That is, the plasma particles are weighted to the uniform Logical grid and the self-consistent mean electrostatic fields obtained from the solution of the Logical grid Poisson equation are interpolated to the particle positions on the Logical grid. This process eliminates the complexity associated with the weighting and interpolation processes on the nonuniform physical grid and allows us to run the PIC method on arbitrary boundary-fitted meshes.
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an arbitrary curvilinear coordinate method for particle in cell modeling
Computational Science & Discovery, 2012Co-Authors: C A Fichtl, John M Finn, K L CartwrightAbstract:A new approach to kinetic simulation of plasmas in complex geometries, based on the particle-in-cell (PIC) simulation method, is explored. In the two-dimensional (2D) electrostatic version of our method, called the arbitrary curvilinear-coordinate PIC method, all essential PIC operations are carried out in 2D on a uniform grid on the unit square Logical Domain, and mapped to a nonuniform boundary-fitted grid on the physical Domain. As the resulting Logical grid equations of motion are not separable, we have developed an extension of the semi-implicit modified leapfrog integration technique to preserve the symplectic nature of the Logical grid particle mover. A generalized, curvilinear-coordinate formulation of Poisson's equations to solve for the electrostatic fields on the uniform Logical grid is also developed. By our formulation, we compute the plasma charge density on the Logical grid based on the particles' positions on the Logical Domain. That is, the plasma particles are weighted to the uniform Logical grid and the self-consistent mean electrostatic fields obtained from the solution of the Logical grid Poisson equation are interpolated to the particle positions on the Logical grid. This process eliminates the complexity associated with the weighting and interpolation processes on the nonuniform physical grid and allows us to run the PIC method on arbitrary boundary-fitted meshes.
C A Fichtl - One of the best experts on this subject based on the ideXlab platform.
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an arbitrary curvilinear coordinate method for particle in cell modeling
arXiv: Plasma Physics, 2012Co-Authors: C A Fichtl, John M Finn, K L CartwrightAbstract:A new approach to the kinetic simulation of plasmas in complex geometries, based on the Particle-in- Cell (PIC) simulation method, is explored. In the two dimensional (2d) electrostatic version of our method, called the Arbitrary Curvilinear Coordinate PIC (ACC-PIC) method, all essential PIC operations are carried out in 2d on a uniform grid on the unit square Logical Domain, and mapped to a nonuniform boundary-fitted grid on the physical Domain. As the resulting Logical grid equations of motion are not separable, we have developed an extension of the semi-implicit Modified Leapfrog (ML) integration technique to preserve the symplectic nature of the Logical grid particle mover. A generalized, curvilinear coordinate formulation of Poisson's equations to solve for the electrostatic fields on the uniform Logical grid is also developed. By our formulation, we compute the plasma charge density on the Logical grid based on the particles' positions on the Logical Domain. That is, the plasma particles are weighted to the uniform Logical grid and the self-consistent mean electrostatic fields obtained from the solution of the Logical grid Poisson equation are interpolated to the particle positions on the Logical grid. This process eliminates the complexity associated with the weighting and interpolation processes on the nonuniform physical grid and allows us to run the PIC method on arbitrary boundary-fitted meshes.
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an arbitrary curvilinear coordinate method for particle in cell modeling
Computational Science & Discovery, 2012Co-Authors: C A Fichtl, John M Finn, K L CartwrightAbstract:A new approach to kinetic simulation of plasmas in complex geometries, based on the particle-in-cell (PIC) simulation method, is explored. In the two-dimensional (2D) electrostatic version of our method, called the arbitrary curvilinear-coordinate PIC method, all essential PIC operations are carried out in 2D on a uniform grid on the unit square Logical Domain, and mapped to a nonuniform boundary-fitted grid on the physical Domain. As the resulting Logical grid equations of motion are not separable, we have developed an extension of the semi-implicit modified leapfrog integration technique to preserve the symplectic nature of the Logical grid particle mover. A generalized, curvilinear-coordinate formulation of Poisson's equations to solve for the electrostatic fields on the uniform Logical grid is also developed. By our formulation, we compute the plasma charge density on the Logical grid based on the particles' positions on the Logical Domain. That is, the plasma particles are weighted to the uniform Logical grid and the self-consistent mean electrostatic fields obtained from the solution of the Logical grid Poisson equation are interpolated to the particle positions on the Logical grid. This process eliminates the complexity associated with the weighting and interpolation processes on the nonuniform physical grid and allows us to run the PIC method on arbitrary boundary-fitted meshes.
