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Mammal

Immunogenic Cell Death

Summary: 

This Boolean model covers the major cell types that intervene in immunogenic cell death (ICD), namely cancer cells, DCs, CD8+ and CD4+ T cells. This model integrates intracellular mechanisms within each individual cell entity, and further incorporates intercellular communications between them. The resulting cell population model recapitulates key features of the dynamics of ICD after an initial treatment, in particular the time-dependent size of the different cell populations.

Model dynamics has been simulated by means of a software tool, UPMaBoSS, which performs stochastic simulations with continuous time, considering the dynamics of the system at the cell population level with appropriate timing of events, and accounting for death and division of each cell type.

With this model, the time scales of some of the processes involved in ICD, which are challenging to measure experimentally, have been predicted. In addition, model analysis led to the identification of actionable targets for boosting ICD-induced antitumor response.

All computational analyses and results are compiled in interactive notebooks which cover the presentation of the network structure, model simulations, and calculations of parameter sensitivity analyses.

Curation
Submitter: 
Aurelien Naldi

Control of proliferation by oncogenes and tumor suppressors

Summary: 

This model is an illustrative example of a signal transduction network
relevant to a cancer hallmark phenotype, uncontrolled proliferation. In
the normal context cell proliferation is driven by growth factors that
bind to receptor tyrosine kinases (RTKs); yet it can also be an outcome
of alterations in signal transduction proteins. Six separate pathways
are typically pointed out in biological literature. This model includes
all of these pathways in a single network. The unperturbed system has
two possible steady states, a non-proliferative one and one with
controlled proliferation (Proliferation = 1), among which it may select
depending on environmental signals. Alterations in certain oncogenes or
tumor suppressor genes yield a single outcome: uncontrolled
proliferation (Proliferation = 2). Targeted inhibition of an oncogene
(here, PI3K) may not eliminate the proliferating phenotype.

Curation
Submitter: 
Aurelien Naldi

T cells response to CTLA4 and PD-1 checkpoint inhibitors

Summary: 

This comprehensive model integrates the available data on T cell activation, taking into account CTLA4 and PD-1 checkpoint inhibitory pathways. It encompasses 216 components and 451 regulatory arcs.

To ease the verification of the behaviour of this large logical model, we have designed a modular approach based on a unit testing framework used in software development. Furthermore, to compare the respective impact of the activation of the two checkpoints, we have designed a value propagation technique enabling the analytical computation of all the nodes frozen following the persisting activation or inhibition of any model component. The model verification approach greatly eased the delineation of logical rules complying with predefined dynamical specifications, while the use of the value propagation technique provided interesting insights into the differential potential of CTLA4 and PD-1 immunotherapies.

All our analyses have been implemented into two python notebooks, enabling their reproduction or extension with the most recent version of the CoLoMoTo Docker image (http://www.colomoto.org/notebook).

Preview the unit testing notebook

Preview the value propagation analysis notebook

Curation
Submitter: 
Aurelien Naldi

T-lymphocyte specification

Summary: 

We have applied the logical modelling framework to the regulatory network controlling T-lymphocyte specification. This process involves cross-regulations between specific T-cell regulatory factors with factors driving alternative differentiation pathways, which remain accessible during the early steps of thymocyte development. Many transcription factors needed for T-cell specification are required in other hematopoietic differentiation pathways, and are combined in a fine-tuned, time-dependent fashion to achieve T-cell commitment.
Using the software GINsim, we integrated current knowledge into a dynamical model, which recapitulates the main developmental steps from early progenitors entering the thymus up to T-cell commitment, as well as the impact of various documented environmental and genetic perturbations. Our model analysis further enabled the identification of several knowledge gaps.

The associated notebook can be loaded using the CoLoMoTo notebook docker image (see http://www.colomoto.org/notebook).

Jupyter Notebook: Tdev_notebook_2nov2019.ipynb

Curation
Submitter: 
Pedro Monteiro

Microenvironment control of hybrid Epithelial-Mesenchymal phenotypes

Summary: 

Epithelial to Mesenchymal Transition (EMT) has been associated with cancer cell heterogeneity, plasticity and metastasis. However, the extrinsic signals supervising these phenotypic transitions remain elusive. To identify microenvironmental signals controlling cancer-associated phenotypes amid the EMT continuum, we defined a logical model of the EMT cellular network that access the qualitative degrees of cell adhesions by adherent junctions and focal adhesions, two features affected during EMT. Model attractors could recover epithelial, mesenchymal and hybrid phenotypes. In silico simulations provided evidences that hybrid phenotypes may arise through independent molecular paths, involving stringent extrinsic signals. Of particular interest, model predictions and their experimental validations indicated that: 1) ECM stiffening is a prerequisite for cells overactivating FAK-SRC to upregulate SNAIL1 and acquire a mesenchymal phenotype, and 2) FAK-SRC inhibition of cell-cell contacts through the Receptor Protein Tyrosine Phosphates kappa leads to the acquisition of a full mesenchymal rather than a hybrid phenotype. Altogether, our computational and experimental approaches permitted to identify critical microenvironmental signals controlling hybrid EMT phenotypes, and indicated that EMT involves multiple molecular programs.

Curation
Submitter: 
Chaouiya

Contribution of ROS and metabolic status to neonatal and adult CD8+ T cell activation

Summary: 

The low response to infection in neonatal T cells contributes to a high incidence of morbidity and mortality. Here we evaluated the effect of the cytoplasmic and mitochondrial levels of Reactive Oxygen Species (ROS) of neonatal CD8+T cells on their low activation. This model captures the interplay between antigen recognition with ROS and metabolic status in T cell responses. This model displays alternative stable states, which corresponds to different cell fates, i.e. quiescent, activated and anergic, depending on ROS status.

The associated notebook can be loaded using the CoLoMoTo notebook docker image (see http://www.colomoto.org/notebook).

Curation
Submitter: 
Aurelien Naldi

Cell-Fate Decision in Response to Death Receptor Engagement

Summary: 

This model provides a generic high-level view of the interplays between NFκB pro-survival pathway, RIP1-dependent necrosis, and the apoptosis pathway in response to death receptor-mediated signals.

Wild type simulations demonstrate robust segregation of cellular responses to receptor engagement. Model simulations recapitulate documented phenotypes of protein knockdowns and enable the prediction of the effects of novel knockdowns. In silico experiments simulate the outcomes following ligand removal at different stages, and suggest experimental approaches to further validate and specialise the model for particular cell types.

This analysis gives specific predictions regarding cross-talks between the three pathways, as well as the transient role of RIP1 protein in necrosis, and confirms the phenotypes of novel perturbations. Our wild type and mutant simulations provide novel insights to restore apoptosis in defective cells. The model analysis expands our understanding of how cell fate decision is made.

The original model focuses on the interplay between three pathways activated in response to the same signal [1].

This model has then been adapted for multiscale analysis [2].


References

Curation
Submitter: 
Laurence Calzone
Related references

TCR and TLR5 merged Boolean model

Summary: 

CD4+ T cells recognize antigens through their T cell receptors TCR). However, additional signals involving co-stimulatory receptors, for example CD28, are required for proper T cell activation. Alternative co-stimulatory receptors have been proposed, including members of the Toll-like receptor family, such as TLR5 and TLR2.

We report here three Boolean models for:
- the T cell receptor (TCR) signalling pathway;
- the Toll-like receptor (TLR5) signalling pathway;
- the combination of TCR and TLR5 pathway, taking into accounting cross-interactions.

These models were validated by analysing the responses of T cells to the activation of these pathways alone or in combination, in terms of CREB, c-Jun and p65 activation.
The resulting merged model accurately reproduces the experimental results, showing that the activation of TLR5 can play a similar role to that of CD28, regarding AP-1, CREB and NF-кB activation, thereby, providing insights regarding the cross-regulation of these pathways in CD4+ T cells.

Curation
Submitter: 
Pedro Monteiro

p53-Mdm2 network involved in DNA repair

Summary: 

This model is a refined version of the logical model of the p53-mdm2 network described in Fig. 5a of Abou-Jaoudé et al. [1].

The regulatory graph describes the interactions between protein p53, the ubiquitin ligase Mdm2 in its nuclear and cytoplasmic forms, and DNA damage. It relies on biological data taken from literature.

In short, the nuclear component of Mdm2 down-regulates the level of active p53. This occurs both by accelerating p53 degradation through ubiquitination and by blocking the transcriptional activity of p53.

Protein p53 plays a dual role. It activates the expression of Mdm2 thereby up-regulating the level of cytoplasmic Mdm2, and down-regulates the level of nuclear Mdm2 by inhibiting Mdm2 nuclear translocation through inactivation of the kinase Akt.

DNA damage has a negative influence on the level of nuclear Mdm2, by accelerating its degradation through ATM-mediated phosphorylation and auto-ubiquitination.

Damage-induced Mdm2 destabilization enables p53 to accumulate and remain active.

Finally, high levels of p53 promote damage repair by inducing the synthesis of DNA repair proteins.


model network


References

Curation
Submitter: 
Pedro T. Monteiro

Primary sex determination of chicken gonads

Summary: 

This logical model assembles the current knowledge on the regulation of primary sex determination in chicken. Relying on experimental data, a gene network was constructed, leading to a logical model that integrates both the Z-dosage and dominant W hypotheses. The model showed that the sexual fate of chicken gonads results from the resolution of the mutual inhibition between DMRT1 and FOXL2; the initial amount of DMRT1 product determines the development of the gonads. In this respect, the W-factor functions at the initiation step as a second device, by reducing the amount of DMRT1 in ZW gonads when the sexual fate of the gonad is settled; i.e. when SOX9 functional state is determined. Developmental constraints that are instrumental in this resolution were identified. These constraints correspond to qualitative restrictions regarding the relative transcription rates of the genes DMRT1, FOXL2 and HEMGN. The model further clarified the role of oestrogen in maintaining FOXL2 function during ovary development.

Curation
Submitter: 
C. Chaouiya
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