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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

Regulatory graph of Th differentiation logical model. The model encompasses 101 components (among which 21 input nodes) and 221 interactions. The components denoting the inputs are in blue, those denoting the secreted cytokines in olive. Green edges correspond to activations, whereas red blunt ones denote inhibitions. Ellipses denote Boolean components, whereas rectangles denote ternary ones. Gray-out components are those selected for reduction.
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Figure 2: Regulatory graph of Th differentiation logical model. The model encompasses 101 components (among which 21 input nodes) and 221 interactions. The components denoting the inputs are in blue, those denoting the secreted cytokines in olive. Green edges correspond to activations, whereas red blunt ones denote inhibitions. Ellipses denote Boolean components, whereas rectangles denote ternary ones. Gray-out components are those selected for reduction.

Mentions: {s1, …, sn} is the set of nodes, which embody the components of the network; these may correspond to proteins, genes, or phenomenological signals (e.g., the node APC in Figure 2 denotes an Antigen Presenting Cell, present or not).


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)

Regulatory graph of Th differentiation logical model. The model encompasses 101 components (among which 21 input nodes) and 221 interactions. The components denoting the inputs are in blue, those denoting the secreted cytokines in olive. Green edges correspond to activations, whereas red blunt ones denote inhibitions. Ellipses denote Boolean components, whereas rectangles denote ternary ones. Gray-out components are those selected for reduction.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 2: Regulatory graph of Th differentiation logical model. The model encompasses 101 components (among which 21 input nodes) and 221 interactions. The components denoting the inputs are in blue, those denoting the secreted cytokines in olive. Green edges correspond to activations, whereas red blunt ones denote inhibitions. Ellipses denote Boolean components, whereas rectangles denote ternary ones. Gray-out components are those selected for reduction.
Mentions: {s1, …, sn} is the set of nodes, which embody the components of the network; these may correspond to proteins, genes, or phenomenological signals (e.g., the node APC in Figure 2 denotes an Antigen Presenting Cell, present or not).

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