- Meeting ID: 867 6409 6440
- passcode: 149120
13h00 - 13h30 – Jérémy Grignard (Servier)
Mathematical modeling of the microtubule detyrosination/tyrosination cycle for cell-based drug screening design
Microtubules and their post-translational modifications are involved in major cellular processes. In severe diseases such as neurodegenerative disorders, tyrosinated microtubules are in lower concentration. To date, no activator of the Tubulin Tyrosine Ligase enzyme has been reported in the literature. Through a first cell-free high-throughput screen, using a proprietary chemical library, we identified compounds that increase the tyrosination status of tubulin C-terminals. Nevertheless, a second screen in hTERT RPE1, MEF and hiPSC derived neurons revealed no activity in those cellular models. We present here a mechanistic mathematical model of the microtubule detyrosination/tyrosination cycle combining computational modeling and high-content image analyses. This model calibrated both for neurons and for proliferating cells by changing two parameter values, explains in both cases the lack of activity of the screened compounds in cell-based screening assays, and identifies key kinetic parameters to modulate the tyrosination status. The tyrosinated tubulin is indeed the product of a chain of two reactions in the cycle: the detyrosinated microtubule depolymerization followed by its tyrosination. The level of tyrosinated microtubule at equilibrium is thus limited by both reaction rates, and activating the tyrosination reaction, as by TTL alone, is not effective. Our computational model also predicts the effect of inhibiting the Tubulin Carboxy Peptidase enzyme which we have validated in MEF cellular models. Furthermore, the model predicts that the synergistic targeting of the two kinetic parameters, the tyrosination and detyrosinated depolymerization rate constants, should suffice to enable an increase of the tyrosination status of microtubules in living cells.
13h30 - 14h00 – Clémence Frioux (Inria Bordeaux)
Discrete modelling of metabolism in individual organisms and large communities
Genome-scale metabolic networks gather the functional potential associated to the genome of an organism. Associated to mathematical modelling, they permit predicting the metabolic response of a species in its environment. There has been many developments in tools and software dedicated to the reconstruction and analyses of such models. Here, I will focus on a discrete modelling of metabolic producibility with a Boolean approach. I will illustrate how it complements existing methods by providing insights into the metabolism of organisms, even from automatically-reconstructed metabolic networks. I will show how such approaches can scale up to large communities of organisms, and use metagenomic data to screen the metabolic potential of the microbiota and identify key members among them.
Dernière modification le 07/01/2022