Differentiation

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.

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

This Boolean model displays the subtle role of miR-9 in the course of neural specification in zebrafish.

Figure 1: Green arrows represent positive regulations, whereas red T arrows represent inhibitory interactions. Dotted lines represent suspected (but not yet molecularly characterized) direct or indirect interactions, which prevent the expression of a gene in the progenitor or neuronal precursor states, such as elavl3/HuC inhibition in progenitors.

The node P denotes a proliferating progenitor state (P=1, N=0). It is defined by the expression of Her6 and/or Zic5. The node N denotes the commitment of a progenitor into a neural precursor (P=0, N=1).
By inhibiting genes with opposite effect on neural differentiation, miR-9 activity generates an ambivalent state (P=0, N=0) poised for responding to both progenitor maintenance and commitment cues.

Model simulations qualitatively recapitulate all the experimental results presented in Coolen et al (submitted), for the wild-type as well as for various mutant situations (including loss-of-functions, ectopic gene expressions, and miR-9 target protection by morpholinos).

The vulva of Caenorhabditis elegans has been long used as an experimental model of cell differentiation and organogenesis. While it is known that the signaling cascades of Wnt, Ras/MAPK, and NOTCH interact to form a molecular network, there is no consensus regarding its precise topology and dynamical properties. We inferred the molecular network, and developed a multivalued synchronous discrete dynamic model to study its behavior. The model reproduces the patterns of activation reported for the following types of cell: vulval precursor, first fate, second fate, second fate with reversed polarity, third fate, and fusion fate. We simulated the fusion of cells, the determination of the first, second, and third fates, as well as the transition from the second to the first fate. We also used the model to simulate all possible single loss- and gain-of-function mutants, as well as some relevant double and triple mutants. Importantly, we associated most of these simulated mutants to multivulva, vulvaless, egg-laying defective, or defective polarity phenotypes. The model shows that it is necessary for RAL-1 to activate NOTCH signaling, since the repression of LIN-45 by RAL-1 would not suffice for a proper second fate determination in an environment lacking DSL ligands. We also found that the model requires the complex formed by LAG-1, LIN-12, and SEL-8 to inhibit the transcription of eff-1 in second fate cells. Our model is the largest reconstruction to date of the molecular network controlling the specification of vulval precursor cells and cell fusion control in C. elegans. According to our model, the process of fate determination in the vulval precursor cells is reversible, at least until either the cells fuse with the ventral hypoderm or divide, and therefore the cell fates must be maintained by the presence of extracellular signals.

Klamt et al. proposed in [1] a Boolean model of the TCR signalling pathway. The model encompasses 40 regulatory components. In this version of the model an auto-regulation has been added on each input.

Analysis

This model has been studied in [2], using novel algorithms for the analysis of feedback circuits and the determination of stable states. The stable state analysis shows seven stable states, listed bellow. Each stable state corresponds to a different input combination, except "111". Indeed, the systems shows an oscilatory behaviour under full activation.

The feedback circuit analysis shows nine circuits, besides the three auto-activation on the inputs. Only one of these circuits is functional: (ZAP70, cCbl). This negative circuit is functional in presence of LCK and TCRphos which can only be maintained in presence of the three inputs. This circuit drives the oscillatory behaviour observed under full activation.

References