C. Smadi, M. Richard, T. Lepoutre, S.Achard

Programme :

• 10h-11h : Charline Smadi (INRAE, IF, Grenoble): Muller’s ratchet with binary tournament selection: Clickrates and type frequency profile
  • Abstract: Muller’s ratchet, in its prototype version, models a haploid, asexual population whose size is constant over the generations. Slightly deleterious mutations are acquired along the lineages at a constant rate, and individuals carrying less mutations have a selective advantage. In the classical variant, the individual fitness is proportional to the difference between the population average and the individual’s mutation load, whereas in the ‘tournament ratchet’ the individual fitness results as a sum of binary comparisons of the individual mutation loads. We obtain the asymptotic click rates of the tournament ratchet in a large parameter regime. We also analyse the type frequency profile of a sample drawn at a late time.
    This is based on an undergoing work, as well as on :
    – A. Gonzalez Casanova, C. Smadi, and A. Wakolbinger. Quasi-equilibria and click times for a variant of Muller’s ratchet. Electronic Journal of Probability, 2023.
    – J. Igelbrink, A. Gonzalez Casanova, C. Smadi, and A. Wakolbinger. Muller’s ratchet in a near-critical regime: Tournament versus fitness proportional selection. Theoretical Population Biology, 2024.
• 11h-11h15 : Pause
• 11h15- 12h15 : Magali Richard (TIMC, Grenoble) : Statistical and computational methods for the analysis of tumor heterogeneity
  • Abstract: Cancer is a highly heterogeneous disease, with each individual tumor evolving as a multicellular autonomous system. Tumors are composed of cells with different identities and origins, that dynamically interact with each other to form the tumor ecosystem. Heterogeneity in tumor cellular composition is a key factor driving cancer progression yet it remains difficult to observe and quantify. To date, our limited ability to accurately estimate this heterogeneity has hampered our understanding of its role during oncogenic processes.
    Our work lies at the intersection of bioinformatics, biostatistics, and oncology. We develop and apply computational methods to analyze high-dimensional, multimodal molecular data. This includes single-cell and spatial transcriptomic datasets, which we use to address key questions in fundamental cancer research. In this talk, I will present some of the methods we have designed to characterise tumor heterogeneity and explore its functional implications. I will also highlight our efforts to promote collaborative algorithm benchmarking and evaluation within the scientific community through data challenge frameworks.
• 12h15-13h45 : Pause déjeuner
• 13h45-14h45 : Thomas Lepoutre (Inria Lyon) : Modelling relaxation experiments
  • Abstract: We analyze the relaxation dynamics of the CD34 antigen on the surface of TF1-BA cells by conducting two relaxation experiments. We propose that the expression of this gene serves as a reliable marker for cell stemness. In the first experiment, we studied the isolation of the least stem cells, while in the second, the most stem cells. In both cases, it is observed that after approximately 25 days the distribution of stemness returns to the initial stationary state. This highlights the complexity of the stemness process, given its dynamic nature. To model these complex dynamics, we introduced a system of two mechanical equations. We have theoretically derived the asymptotic profile of the solutions of this model. Additionally, utilizing data obtained from the relaxation experiments, we estimated the parameter values. Numerical simulations, based on these parameter values, have shown that the model solutions closely align with the experimental data from the relaxation experiments.
• 14h45-15h : Pause
• 15h-16h : Sophie Achard (LJK, Grenoble) : Statistical comparisons of spatio-temporal networks
  • Abstract: In the scenario where multiple instances of networks with same nodes are available and nodes are attached to spatial features, it is worth combining both information in order to explain the role of the nodes. The explainability of node role in complex networks is very difficult, however crucial in different application scenarios such as social science, neuroscience, computer science. . . Many efforts have been made on the quantification of hubs revealing particular nodes in a network using a given structural property.
    Yet, for spatio-temporal networks, the identification of node role remains largely unexplored. In this talk, I will show limitations of classical methods on a real datasets coming from brain connectivity comparing healthy subjects to coma patients. Then, I will present recent work using equivalence relation of the nodal structural properties. Comparisons of graphs with same nodes set is evaluated with a new similarity score based on graph structural patterns. This score provides a nodal index to determine node role distinctiveness in a graph family. Finally, illustrations on different datasets concerning human brain functional connectivity will be described.