A network model for the control of the differentiation process in Th cells

Mendoza, Luis

doi:10.1016/j.biosystems.2005.10.004

Cited by 153 publications

(170 citation statements)

References 60 publications

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“…Hence, despite the lack of quantitative data, it has been possible to model this network as a dynamical system at a qualitative level. Specifically, this network has been studied by modeling it as a discrete dynamical system (Mendoza 2006), a continuous dynamical system (Mendoza and Xenarios 2006), a Petri Net (Remy et al 2006), and a binary decision diagram (Garg et al 2007). Also the model has been recently updated to include the description of Th17 and Treg cell types (Mendoza and Pardo 2010).…”

Section: T-helper Modelmentioning

confidence: 99%

Strengths and Weaknesses of Selected Modeling Methods Used in Systems Biology

Kahlem¹,

DiCara²,

Durot³

et al. 2011

Systems and Computational Biology - Bioinformatics and Computational Modeling

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Section: T-helper Modelmentioning

confidence: 99%

Strengths and Weaknesses of Selected Modeling Methods Used in Systems Biology

Kahlem¹,

DiCara²,

Durot³

et al. 2011

Systems and Computational Biology - Bioinformatics and Computational Modeling

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“…The balance between Th1 and Th2 cytokines may determine the outcome of an autoimmune disease [43]. Only a few cytokines, such as IFN-c, IL-12 and IL-18, participate in the commitment of undifferentiated Th0 cells to become Th1 cells [44]. In the IL-12 )/) mice, the incidence and severity of EAN were significantly reduced compared with that in wildtype mice [23].…”

Section: Lymphocyte Proliferation In Responses To P2 53-78mentioning

confidence: 99%

Passive Transfer of Experimental Autoimmune Neuritis by IL‐12 and IL‐18 Synergistically Potentiated Lymphoid Cells is Regulated by NKR‐P1+ Cells

Sun

Wang

et al. 2007

Scand J Immunol

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The aim of this study was to investigate the roles and mechanism of interleukin‐12 (IL‐12) and interleukin‐18 (IL‐18) in potentiating the autoreactivity of lymphoid cells specific for P2 53–78 peptide. P2 53–78‐specific lymphoid cells in the presence of IL‐12 or IL‐18 alone passive transferred only moderate experimental autoimmune neuritis (EAN) into a low percentage of recipients. However, lymphoid cells co‐cultured with both cytokines transferred aggressive clinical and histological EAN into all recipients. NKR‐P1+ cells (including NK and NKT cells) played an immunosuppressive function in passive transfer EAN and depletion of NKR‐P1+ cells by anti‐NKR‐P1 Ab and complement induced a more serious form of EAN. Nevertheless, lymphoid cells co‐cultured with both IL‐12 and IL‐18 induced high levels of interferon‐γ (IFN‐γ) and promoted Th1 differentiation partially through NKR‐P1+ cells and to some extent, NKR‐P1+ cell depletion inhibited the auto‐reactivity of lymphoid cells treated with IL‐12 and IL‐18.

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“…To illustrate this point, let us consider three situations reported in experimental articles (cf. citations in [16]):…”

Section: Stable States and Their Biological Interpretationmentioning

confidence: 99%

“…Depending on the challenge and on the activity of other cell lines, the virgin T lymphocytes may differentiate into different subtypes (Th1 and Th2) characterised by specific gene expression and lymphokine excretion patterns. On the basis of an extensive analysis of the literature, L. Mendoza has recently proposed a logical model encompassing the most crucial regulatory components and the cross-interactions involved in these differentiative decisions [16]. The model of Mendoza is briefly described in the supplementary material provided on the GINsim web site [25], including an XML (GINML) file containing the definition of the logical regulatory graph, interaction intervals and parameter values.…”

Section: Introducing Th-cell Differentiationmentioning

confidence: 99%

Qualitative Petri Net Modelling of Genetic Networks

Chaouiya

Rémy

Thieffry

2006

Transactions on Computational Systems Biology VI

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Abstract. The complexity of biological regulatory networks calls for the development of proper mathematical methods to model their structures and to give insight in their dynamical behaviours. One qualitative approach consists in modelling regulatory networks in terms of logical equations (using either Boolean or multi-level discretisation). Petri Nets (PNs) offer a complementary framework to analyse large systems.In this paper, we propose to articulate the logical approach with PNs. We first revisit the definition of a rigourous and systematic mapping of multi-level logical regulatory models into specific standard PNs, called Multi-level Regulatory Petri Nets (MRPNs). In particular, we consider the case of multiple arcs representing different regulatory effects from the same source. We further propose a mapping of multi-level logical regulatory models into Coloured PNs, called Coloured Regulatory Petri Nets (CRPNs). These CRPNs provide an intuitive graphical representation of regulatory networks, relatively easy to grasp.Finally, we present the PN translation and the analysis of a multi-level logical model of the core regulatory network controlling the differentiation of T-helper lymphocytes into Th1 and Th2 types.

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A network model for the control of the differentiation process in Th cells

Cited by 153 publications

References 60 publications

Strengths and Weaknesses of Selected Modeling Methods Used in Systems Biology

Strengths and Weaknesses of Selected Modeling Methods Used in Systems Biology

Passive Transfer of Experimental Autoimmune Neuritis by IL‐12 and IL‐18 Synergistically Potentiated Lymphoid Cells is Regulated by NKR‐P1+ Cells

Qualitative Petri Net Modelling of Genetic Networks

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