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Logical modelling of myelofibrotic microenvironment predicts dysregulated progenitor stem cell crosstalk


Idiopathic primary myelofibrosis is an age-related clonal neoplastic disorder of
haematopoiesis characterised by a myeloproliferation and myelofibrosis. Recent
evidence suggests that disease onset results from an altered bone marrow
microenvironment, leading to disrupted crosstalk between progenitor haematopoietic
and mesenchymal stem cells populations. 90% of myelofibrosis cases exhibit ectopic
mutations of JAK2, CALR and, or MPL genes which all converge on the activation of
JAK and STAT signaling pathways. Treatments aiming to target STAT overactivity
have been developed; however, disease management is conducted at advanced
stages of the disease and treatments are not effective. A computational description
of how altered microenvironments can lead to dysregulated crosstalk between
haematopoietic and mesenchymal stem cells populations following STAT activation
would increase our knowledge of disease pathology and influence future treatment
protocols. To meet this aim, we have constructed a logical model that accounts for
the myelofibrotic microenvironment following TPO and lTLR signalling, integrated
with JAK-STAT signalling. The model primarily aims to provide a mechanistic
understanding of the dysregulated crosstalk between progenitor HSC’s, MSC’s and
the microenvironment to predict the onset of PMF with, and without the JAK
activation. Wildtype simulations result in 4 cyclic attractors being obtained, all
depending on combination of inputs being modelled. The model predicted that
presence of TPO and lTLR signalling are both required to facilitate disease onset for
wildtype simulations. For simulations involving JAK knock-in mutated scenarios, the
model resulted in 4 fixed point attractors, with the presence of lTLR alone being
sufficient to drive disease progression.

Pedro Monteiro

Signalling in prostate cancer


Prostate cancer is the second most occurring cancer in men worldwide, and with the
advances made with screening for prostate-specific antigen, it has been prone to early
diagnosis and over-treatment. To better understand the mechanisms of tumorigenesis and
possible treatment responses, we developed a mathematical model of prostate cancer which
considers the major signalling pathways known to be deregulated.
The model includes pathways such as androgen receptor, MAPK, Wnt, NFkB, PI3K/AKT,
MAPK, mTOR, SHH, the cell cycle, the epithelial-mesenchymal transition (EMT), apoptosis
and DNA damage pathways. The final model accounts for 133 nodes and 449 edges.
We applied a methodology to personalise this Boolean model to molecular data to reflect the
heterogeneity and specific response to perturbations of cancer patients, using TCGA and
GDSC datasets.

Aurelien Naldi

Differentiation of Monocytes to Dendritic Cells


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.

Aurelien Naldi

Response to BRAF treatment in melanoma and colorectal cancer


The study of response to cancer treatments has benefited greatly from the contribution of different
omics data but their interpretation is sometimes difficult. Some mathematical models based on
prior biological knowledge of signaling pathways, facilitate this interpretation but often require
fitting of their parameters using perturbation data. We propose a more qualitative mechanistic
approach, based on logical formalism and on the sole mapping and interpretation of omics data,
and able to recover differences in sensitivity to gene inhibition without model training. This
approach is showcased by the study of BRAF inhibition in patients with melanomas and colorectal
cancers who experience significant differences in sensitivity despite similar omics profiles.

We first gather information from literature and build a logical model summarizing the regulatory
network of the mitogen-activated protein kinase (MAPK) pathway surrounding BRAF, with factors
involved in the BRAF inhibition resistance mechanisms. The relevance of this model is verified by
automatically assessing that it qualitatively reproduces response or resistance behaviors identified
in the literature. Data from over 100 melanoma and colorectal cancer cell lines are then used to
validate the model’s ability to explain differences in sensitivity. This generic model is transformed
into personalized cell line-specific logical models by integrating the omics information of the cell
lines as constraints of the model. The use of mutations alone allows personalized models to
correlate significantly with experimental sensitivities to BRAF inhibition, both from drug and
CRISPR targeting, and even better with the joint use of mutations and RNA, supporting
multi-omics mechanistic models. A comparison of these untrained models with learning approaches
highlights similarities in interpretation and complementarity depending on the size of the datasets.


Immune checkpoints


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.

Aurelien Naldi

Differential expression of IL17 isoforms A and F in helper T Lymphocytes


IL-17A and F are critical cytokines in anti-microbial immunity but also contribute to auto-immune pathologies. Recent evidence suggests that they may be differentially produced by T-helper (Th) cells but the underlying mechanisms remain unknown. To address this question, a logical model containing 82 components and 136 regulatory links was developed and calibrated with original flow cytometry data using naive CD4+ T cells in conditions inducing either IL-17A or F. Model analyses led to the identification of the transcription factors NFAT2A, STAT5A and Smad2 as key components explaining the differential expression of IL-17A and IL-17F, with STAT5A controlling IL-17F expression, and an interplay of NFAT2A, STAT5A and Smad2 controlling IL-17A expression.

The analysis notebook is available on github:

Aurélien Naldi

Immunogenic Cell Death


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.

Aurelien Naldi

Control of proliferation by oncogenes and tumor suppressors


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.

Aurelien Naldi

T cells response to CTLA4 and PD-1 checkpoint inhibitors


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 (

Preview the unit testing notebook

Preview the value propagation analysis notebook

Aurelien Naldi

T-lymphocyte specification


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

Jupyter Notebook: Tdev_notebook_2nov2019.ipynb

Pedro Monteiro
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