Human

This logical model accounts for the differentiation of monocytes into monocyte-derived dendritic cells (moDCs) and macrophages. It recapitulates the main established facts regarding wild-type differentiation of monocytes and macrophages in the presence of CSF2 and/or IL4, as well as the impact of various documented mutations. This model integrates documented regulatory interactions, together with novel interactions predicted from public transcriptomic and epigenomic data, enabling to validate in silico various novel transcriptional regulatory links presumably involved in this differentiation process.

After the success of the new generation of immune therapies, immune checkpoint receptors have become one important center of attention of molecular oncologists. The initial success and hopes of anti-programmed cell death protein 1 (anti-PD1) and anti-cytotoxic T-lymphocyte-associated protein 4 (anti-CTLA4) therapies have shown some limitations since a majority of patients have continued to show resistance. Other immune checkpoints have raised some interest and are under investigation, such as T cell immunoglobulin and ITIM (immunoreceptor tyrosine-based inhibition motif) domain (TIGIT), inducible T-cell costimulator (ICOS), and T cell immunoglobulin and mucin domain-containing protein 3 (TIM3), which appear as promising targets for immunotherapy. To explore their role and study possible synergetic effects of these different checkpoints, we have built a model of T cell receptor (TCR) regulation including not only PD1 and CTLA4, but also other well studied checkpoints (TIGIT, TIM3, lymphocyte activation gene 3 (LAG3), cluster of differentiation 226 (CD226), ICOS, and tumour necrosis factor receptors (TNFRs)) and simulated different aspects of T cell biology. Our model shows good correspondence with observations from available experimental studies of anti-PD1 and anti-CTLA4 therapies and suggest efficient combinations of immune checkpoint inhibitors (ICI). Among the possible candidates, TIGIT appears to be the most promising drug target in our model. The model predicts that signal transducer and activator of transcription 1 (STAT1)/STAT4-dependent pathways, activated by cytokines such as interleukin 12 (IL12) and interferon gamma (IFNG), could improve the effect of ICI therapy via upregulation of Tbet, suggesting that the effect of the cytokines related to STAT3/STAT1 activity is dependent on the balance between STAT1 and STAT3 downstream signalling.

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

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.

Relationships between genetic alterations, such as co-occurrence or mutual exclusivity, are often observed in cancer, where their understanding may provide new insights into etiology and clinical management. In this study, we combined statistical analyses and computational modelling to explain patterns of genetic alterations seen in 178 patients with bladder tumours (either muscle-invasive or non-muscle-invasive). A statistical analysis on frequently altered genes identified pair associations including co-occurrence or mutual exclusivity. Focusing on genetic alterations of protein-coding genes involved in growth factor receptor signalling, cell cycle and apoptosis entry, we complemented this analysis with a literature search to focus on nine pairs of genetic alterations of our dataset, with subsequent verification in three other datasets available publically. To understand the reasons and contexts of these patterns of associations while accounting for the dynamics of associated signalling pathways, we built a logical model. This model was validated first on published mutant mice data, then used to study patterns and to draw conclusions on counter-intuitive observations, allowing one to formulate predictions about conditions where combining genetic alterations benefits tumorigenesis. For example, while CDKN2A homozygous deletions occur in a context of FGFR3 activating mutations, our model suggests that additional PIK3CA mutation or p21CIP deletion would greatly favour invasiveness. Further, the model sheds light on the temporal orders of gene alterations, for example, showing how mutual exclusivity of FGFR3 and TP53 mutations is interpretable if FGFR3 is mutated first. Overall, our work shows how to predict combinations of the major gene alterations leading to invasiveness.