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Model checking to assess T-helper cell plasticity.

Abou-Jaoudé W, Monteiro PT, Naldi A, Grandclaudon M, Soumelis V, Chaouiya C, Thieffry D - Front Bioeng Biotechnol (2015)

Bottom Line: To account for novel cellular subtypes, we present an extended version of a published model of Th cell differentiation.We then use symbolic model checking to analyze reachability properties between Th subtypes upon changes of environmental cues.Finally, we explore novel strategies enabling specific Th cell polarizing or reprograming events.

View Article: PubMed Central - PubMed

Affiliation: Institut de Biologie de l'Ecole Normale Supérieure , Paris , France ; UMR CNRS 8197 , Paris , France ; INSERM U1024 , Paris , France ; Laboratoire d'Informatique de l'Ecole Normale Supérieure , Paris , France.

ABSTRACT
Computational modeling constitutes a crucial step toward the functional understanding of complex cellular networks. In particular, logical modeling has proven suitable for the dynamical analysis of large signaling and transcriptional regulatory networks. In this context, signaling input components are generally meant to convey external stimuli, or environmental cues. In response to such external signals, cells acquire specific gene expression patterns modeled in terms of attractors (e.g., stable states). The capacity for cells to alter or reprogram their differentiated states upon changes in environmental conditions is referred to as cell plasticity. In this article, we present a multivalued logical framework along with computational methods recently developed to efficiently analyze large models. We mainly focus on a symbolic model checking approach to investigate switches between attractors subsequent to changes of input conditions. As a case study, we consider the cellular network regulating the differentiation of T-helper (Th) cells, which orchestrate many physiological and pathological immune responses. To account for novel cellular subtypes, we present an extended version of a published model of Th cell differentiation. We then use symbolic model checking to analyze reachability properties between Th subtypes upon changes of environmental cues. This allows for the construction of a synthetic view of Th cell plasticity in terms of a graph connecting subtypes with arcs labeled by input conditions. Finally, we explore novel strategies enabling specific Th cell polarizing or reprograming events.

No MeSH data available.


Related in: MedlinePlus

Typical workflow to tackle a central biological question using logical model construction and analysis. A model is defined, relying on literature and experimental data (box Model Definition). The model is then analyzed (boxes Static analysis and Dynamical analysis). The identification of the attractors is performed either by static methods (see Sections 2.2.1 and 2.2.2) or by inspecting the dynamics (see Sections 2.2.3 and 2.3). Dynamics are represented at different levels of abstraction, from the comprehensive state transition graphs to the reprograming graphs. Resulting properties are confronted with biological observations, leading to predictions and/or to model revision. Ellipsoid boxes relate to the different model versions and behavior representations. Green boxes denote methods that are available in GINsim, whereas gray boxes denote analyses performed with other software tools.
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Figure 1: Typical workflow to tackle a central biological question using logical model construction and analysis. A model is defined, relying on literature and experimental data (box Model Definition). The model is then analyzed (boxes Static analysis and Dynamical analysis). The identification of the attractors is performed either by static methods (see Sections 2.2.1 and 2.2.2) or by inspecting the dynamics (see Sections 2.2.3 and 2.3). Dynamics are represented at different levels of abstraction, from the comprehensive state transition graphs to the reprograming graphs. Resulting properties are confronted with biological observations, leading to predictions and/or to model revision. Ellipsoid boxes relate to the different model versions and behavior representations. Green boxes denote methods that are available in GINsim, whereas gray boxes denote analyses performed with other software tools.

Mentions: In this section, we introduce the logical framework, presenting the rationale underlying the model definition. We further describe model modifications accounting for genetic perturbations (e.g., gene knock-out or knock-in) along with a model reduction method. Next, we briefly present computational strategies to efficiently analyze properties of logical models. Finally, we focus on the assets of model checking to enhance the dynamical analysis of large signaling/regulatory logical networks. Figure 1 illustrates the workflow for logical model definition and analysis, on which we rely to address the question of Th cell plasticity. Most methods presented in this section are implemented in GINsim (Chaouiya et al., 2012)1.


Model checking to assess T-helper cell plasticity.

Abou-Jaoudé W, Monteiro PT, Naldi A, Grandclaudon M, Soumelis V, Chaouiya C, Thieffry D - Front Bioeng Biotechnol (2015)

Typical workflow to tackle a central biological question using logical model construction and analysis. A model is defined, relying on literature and experimental data (box Model Definition). The model is then analyzed (boxes Static analysis and Dynamical analysis). The identification of the attractors is performed either by static methods (see Sections 2.2.1 and 2.2.2) or by inspecting the dynamics (see Sections 2.2.3 and 2.3). Dynamics are represented at different levels of abstraction, from the comprehensive state transition graphs to the reprograming graphs. Resulting properties are confronted with biological observations, leading to predictions and/or to model revision. Ellipsoid boxes relate to the different model versions and behavior representations. Green boxes denote methods that are available in GINsim, whereas gray boxes denote analyses performed with other software tools.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4309205&req=5

Figure 1: Typical workflow to tackle a central biological question using logical model construction and analysis. A model is defined, relying on literature and experimental data (box Model Definition). The model is then analyzed (boxes Static analysis and Dynamical analysis). The identification of the attractors is performed either by static methods (see Sections 2.2.1 and 2.2.2) or by inspecting the dynamics (see Sections 2.2.3 and 2.3). Dynamics are represented at different levels of abstraction, from the comprehensive state transition graphs to the reprograming graphs. Resulting properties are confronted with biological observations, leading to predictions and/or to model revision. Ellipsoid boxes relate to the different model versions and behavior representations. Green boxes denote methods that are available in GINsim, whereas gray boxes denote analyses performed with other software tools.
Mentions: In this section, we introduce the logical framework, presenting the rationale underlying the model definition. We further describe model modifications accounting for genetic perturbations (e.g., gene knock-out or knock-in) along with a model reduction method. Next, we briefly present computational strategies to efficiently analyze properties of logical models. Finally, we focus on the assets of model checking to enhance the dynamical analysis of large signaling/regulatory logical networks. Figure 1 illustrates the workflow for logical model definition and analysis, on which we rely to address the question of Th cell plasticity. Most methods presented in this section are implemented in GINsim (Chaouiya et al., 2012)1.

Bottom Line: To account for novel cellular subtypes, we present an extended version of a published model of Th cell differentiation.We then use symbolic model checking to analyze reachability properties between Th subtypes upon changes of environmental cues.Finally, we explore novel strategies enabling specific Th cell polarizing or reprograming events.

View Article: PubMed Central - PubMed

Affiliation: Institut de Biologie de l'Ecole Normale Supérieure , Paris , France ; UMR CNRS 8197 , Paris , France ; INSERM U1024 , Paris , France ; Laboratoire d'Informatique de l'Ecole Normale Supérieure , Paris , France.

ABSTRACT
Computational modeling constitutes a crucial step toward the functional understanding of complex cellular networks. In particular, logical modeling has proven suitable for the dynamical analysis of large signaling and transcriptional regulatory networks. In this context, signaling input components are generally meant to convey external stimuli, or environmental cues. In response to such external signals, cells acquire specific gene expression patterns modeled in terms of attractors (e.g., stable states). The capacity for cells to alter or reprogram their differentiated states upon changes in environmental conditions is referred to as cell plasticity. In this article, we present a multivalued logical framework along with computational methods recently developed to efficiently analyze large models. We mainly focus on a symbolic model checking approach to investigate switches between attractors subsequent to changes of input conditions. As a case study, we consider the cellular network regulating the differentiation of T-helper (Th) cells, which orchestrate many physiological and pathological immune responses. To account for novel cellular subtypes, we present an extended version of a published model of Th cell differentiation. We then use symbolic model checking to analyze reachability properties between Th subtypes upon changes of environmental cues. This allows for the construction of a synthetic view of Th cell plasticity in terms of a graph connecting subtypes with arcs labeled by input conditions. Finally, we explore novel strategies enabling specific Th cell polarizing or reprograming events.

No MeSH data available.


Related in: MedlinePlus