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Synchronous versus asynchronous modeling of gene regulatory networks.

Garg A, Di Cara A, Xenarios I, Mendoza L, De Micheli G - Bioinformatics (2008)

Bottom Line: In this manuscript, we provide algorithms based on reduced ordered binary decision diagrams (ROBDDs) for Boolean modeling of gene regulatory networks.These algorithms allow users to compute cyclic attractors of large networks that are currently not feasible using existing software.Hereby we provide a framework to analyze the effect of multiple gene perturbation protocols, and their effect on cell differentiation processes.

View Article: PubMed Central - PubMed

Affiliation: Ecole Polytechnique Federale de Lausanne, Station 14, 1015 Lausanne, Switzerland. abhishek.garg@epfl.ch

ABSTRACT

Motivation: In silico modeling of gene regulatory networks has gained some momentum recently due to increased interest in analyzing the dynamics of biological systems. This has been further facilitated by the increasing availability of experimental data on gene-gene, protein-protein and gene-protein interactions. The two dynamical properties that are often experimentally testable are perturbations and stable steady states. Although a lot of work has been done on the identification of steady states, not much work has been reported on in silico modeling of cellular differentiation processes.

Results: In this manuscript, we provide algorithms based on reduced ordered binary decision diagrams (ROBDDs) for Boolean modeling of gene regulatory networks. Algorithms for synchronous and asynchronous transition models have been proposed and their corresponding computational properties have been analyzed. These algorithms allow users to compute cyclic attractors of large networks that are currently not feasible using existing software. Hereby we provide a framework to analyze the effect of multiple gene perturbation protocols, and their effect on cell differentiation processes. These algorithms were validated on the T-helper model showing the correct steady state identification and Th1-Th2 cellular differentiation process.

Availability: The software binaries for Windows and Linux platforms can be downloaded from http://si2.epfl.ch/~garg/genysis.html.

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A small representation of how activator dominated circuits can be represented using Equations (1) and (2). (a) A small gene regulatory network where gene x1a dominates over x2in. (b) Expanded functional representation using Equations (1) and (2), where fxia(t)=(¬x1a∧¬x2in), xi(t+1)=(x1a∨fxia(t)).
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Figure 2: A small representation of how activator dominated circuits can be represented using Equations (1) and (2). (a) A small gene regulatory network where gene x1a dominates over x2in. (b) Expanded functional representation using Equations (1) and (2), where fxia(t)=(¬x1a∧¬x2in), xi(t+1)=(x1a∨fxia(t)).

Mentions: Following the standardized qualitative dynamical systems methodology (Mendoza and Xenarios, 2006), Equation (1) states that inhibitor functions are strong enough to change the state of the output gene from 1 to 0, while activator functions can change the state from 0 to 1 if and only if no inhibitor functions are acting on that gene. Although this might give an impression of a repressor dominated system, in practice, a situation where an activator dominates the repressor can be represented by Equations (1) and (2) as shown in Figure 2.(1)(2)Equations (1) and (2) represent the dynamics of individual genes independent of the dynamics of the other genes in the network. To model the dynamics of the complete network, one has to couple the dynamics of these genes. This can be done by defining the transition function, T(xt,xt+1), of the state of the network. T(xt,xt+1) represents the transition from the present state xt to the next state xt+1.Fig. 2.


Synchronous versus asynchronous modeling of gene regulatory networks.

Garg A, Di Cara A, Xenarios I, Mendoza L, De Micheli G - Bioinformatics (2008)

A small representation of how activator dominated circuits can be represented using Equations (1) and (2). (a) A small gene regulatory network where gene x1a dominates over x2in. (b) Expanded functional representation using Equations (1) and (2), where fxia(t)=(¬x1a∧¬x2in), xi(t+1)=(x1a∨fxia(t)).
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2519162&req=5

Figure 2: A small representation of how activator dominated circuits can be represented using Equations (1) and (2). (a) A small gene regulatory network where gene x1a dominates over x2in. (b) Expanded functional representation using Equations (1) and (2), where fxia(t)=(¬x1a∧¬x2in), xi(t+1)=(x1a∨fxia(t)).
Mentions: Following the standardized qualitative dynamical systems methodology (Mendoza and Xenarios, 2006), Equation (1) states that inhibitor functions are strong enough to change the state of the output gene from 1 to 0, while activator functions can change the state from 0 to 1 if and only if no inhibitor functions are acting on that gene. Although this might give an impression of a repressor dominated system, in practice, a situation where an activator dominates the repressor can be represented by Equations (1) and (2) as shown in Figure 2.(1)(2)Equations (1) and (2) represent the dynamics of individual genes independent of the dynamics of the other genes in the network. To model the dynamics of the complete network, one has to couple the dynamics of these genes. This can be done by defining the transition function, T(xt,xt+1), of the state of the network. T(xt,xt+1) represents the transition from the present state xt to the next state xt+1.Fig. 2.

Bottom Line: In this manuscript, we provide algorithms based on reduced ordered binary decision diagrams (ROBDDs) for Boolean modeling of gene regulatory networks.These algorithms allow users to compute cyclic attractors of large networks that are currently not feasible using existing software.Hereby we provide a framework to analyze the effect of multiple gene perturbation protocols, and their effect on cell differentiation processes.

View Article: PubMed Central - PubMed

Affiliation: Ecole Polytechnique Federale de Lausanne, Station 14, 1015 Lausanne, Switzerland. abhishek.garg@epfl.ch

ABSTRACT

Motivation: In silico modeling of gene regulatory networks has gained some momentum recently due to increased interest in analyzing the dynamics of biological systems. This has been further facilitated by the increasing availability of experimental data on gene-gene, protein-protein and gene-protein interactions. The two dynamical properties that are often experimentally testable are perturbations and stable steady states. Although a lot of work has been done on the identification of steady states, not much work has been reported on in silico modeling of cellular differentiation processes.

Results: In this manuscript, we provide algorithms based on reduced ordered binary decision diagrams (ROBDDs) for Boolean modeling of gene regulatory networks. Algorithms for synchronous and asynchronous transition models have been proposed and their corresponding computational properties have been analyzed. These algorithms allow users to compute cyclic attractors of large networks that are currently not feasible using existing software. Hereby we provide a framework to analyze the effect of multiple gene perturbation protocols, and their effect on cell differentiation processes. These algorithms were validated on the T-helper model showing the correct steady state identification and Th1-Th2 cellular differentiation process.

Availability: The software binaries for Windows and Linux platforms can be downloaded from http://si2.epfl.ch/~garg/genysis.html.

Show MeSH