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Axel Hutt
Additive noise tunes the stability of high-dimensional systems
18 janvier 2024 - 09:00Salle de conférences IRMA
Experimental brain activity is known to show oscillations in specific frequency bands, which reflects neural information processing. For instance, strong oscillations at about 2Hz reflect tiredness and sleepiness, strong 40Hz oscillations indicate alertness. Changes of power in frequency bands indicate changes in information processing. For instance, it has been observed that strong activity about 10Hz and 2Hz emerge in electroencephalographic activity (EEG) when a subject loses consciousness in general anaesthesia. Numerical simulations of stochastic neural models have shown that such a change can be reproduced by changing the variance of external additive Gaussian uncorrelated noise. At a first glance, this is surprising since additive noise is not supposed to affect a system’s oscillatory activity or stability.
The presentation shows first how additive noise can affect a nonlinear system’s stability by applying stochastic center manifold analysis in non-delayed low-dimensional systems and delayed systems. Then, an extension to stochastic randomly connected network models shows that the observed effect also emerges. Applying random matrix theory together with mean-field theory demonstrates how additive noise tunes the stability and oscillatory activity in such systems. In sum, the mathematical studies provide an explanation why the brain’s oscillatory activity changes with changing experimental conditions.
The seminar will be also broadcasted via BBB: https://bbb.unistra.fr/b/hum-51d-suf-mzq
Axel Hutt has studied physics at the University of Stuttgart supervised by Prof. Hermann Haken and worked on his PhD at the Max Planck Institute for Cognitive Neuroscience, for which he has received a Schloessmann Fellowship Award of the Max Planck Society in the year 2000. After positions at the Weierstrass Institute for Applied Analysis and Stochastics in Berlin, the Humboldt University Berlin and the University of Ottawa/Canada, he started working at INRIA Nancy Grand Est in 2007 and became Directeur de Recherche at INRIA in 2015. In 2010, Axel received an ERC Starting Grant. After a sabbatical stay at the German Weather Service for 4 years, from 2019 on he is working in the INRIA-team MIMESIS / iCube-team MLMS in Strasbourg on stochastic nonlinear dynamics of brain models in the context mental disorders. -
Nalini Anantharaman
Gaps in the spectrum of large graphs
15 février 2024 - 09:00Salle de conférences IRMA
We discuss questions and results pertaining to the presence of gaps in the spectrum of the adjacency matrix of certain families of finite graphs (in the limit where the size of the graph goes to infinity). We mostly focus on the construction of expanders (i.e. families of graphs with a uniform gap at the bottom of the spectrum), but also describe recent results or Bordenave-Collins or Sarnak-Kollar related to the presence of gaps elsewhere in the spectrum. We will also allude to similar questions for hyperbolic surfaces instead of graphs.
The seminar will be also broadcasted via BBB: https://bbb.unistra.fr/b/hum-51d-suf-mzq
Nalini Anantharaman was a Professor at Université Paris-Sud from 2009 to 2014, at Université de Strasbourg from 2014 to 2022, and has been a professor at Collège de France since October 2022 on the "Spectral Geometry" chair. She directed the LabEx IRMIA and the ITI IRMIA++ from 2018 to early 2023. -
Jérôme Pétri
Neutron star magnetospheres: a challenge for plasma physicists and astrophysicists
21 mars 2024 - 09:00Salle de conférences IRMA
Neutron stars are fascinating astrophysical objects immersed in strong gravitational and electromagnetic fields of the order B~10^5-10^10T. These stars manifest themselves mostly as pulsars, emitting a timely very stable and regular electromagnetic signal with periods around P~ 1ms - 10s. Even though discovered 55 years ago, neutron stars still remain mysterious compact objects. Neutron star electrodynamics remains challenging for performing computer simulations because of the extraordinary large span in space and time scales involved in such stars. A typical ratio between the cyclotron frequency omegaB and the stellar rotation frequency Omega is omegaB/Omega ~ 10^16-10^19. Numerical schemes are far from being able to handle such huge ratio. However a global qualitative picture emerges slowly thanks to recent advances in numerical simulations. In this talk, I summarize the most fundamental theoretical aspects of pulsar magnetospheres and highlight the latest developments in simulations of pulsar magnetospheres, from the basic force-free approximation or from the ideal magnetohydrodynamics regime to more detailed particle-in-cell approaches including radiation reaction.
The seminar will be also broadcasted via BBB: https://bbb.unistra.fr/b/hum-51d-suf-mzq
Jérôme Petri is Maître de Conférences at the Université de Strasbourg, Observatoire astronomique, member of the GALHECOS team. His research focuses on the theory and simulation of neutron star electrodynamics and high-energy radiation processes, linking recent multi-wavelength observations of these stars to state-of-the-art numerical modelling. -
Ivan Tarassov, Joseph Schacherer, Nacho Molina
Special biology session with ITI IMCBio+
18 avril 2024 - 09:00Salle de conférences IRMA
This meeting of the ITI IRMIA++ Interdisciplinary Seminar will focus on interactions with biology. Our guests will be three colleagues from ITI IMCBio+ (Integrative Molecular & Cellular Biology). Ivan Tarassov, director, will give a a brief a presentation of the themes covered by the ITI IMCBio+, followed by two short research talks by Joseph Schacherer and Nacho Molina.
The seminar will be also broadcasted via BBB: https://bbb.unistra.fr/b/hum-51d-suf-mzq
TITLES OF THE TALKS
"What is the ITI IMCBio+" (Ivan Tarassov)
"Species-wide quantitative transcriptomes and proteomes reveal distinct genetic control of gene expression variation in yeast" (Joseph Schacherer)
"Modeling Gene Regulation Using Biophysics-Informed Deep Learning on Single-Cell Multi-Omics Data" (Nacho Molina)
ABSTRACTS OF THE TWO SHORT RESEARCH TALKS
Abstract (Joseph Schacherer): Gene expression varies between individuals and corresponds to a key step linking genotypes to phenotypes. However, our knowledge regarding the species-wide genetic control of protein abundance, including its dependency on transcript levels, is very limited. Here, we have determined quantitative proteomes of a large population of 942 diverse natural Saccharomyces cerevisiae yeast isolates. We found that mRNA and protein abundances are weakly correlated at the population gene level. While the protein co-expression network recapitulates major biological functions, differential expression patterns reveal proteomic signatures related to specific populations. Comprehensive genetic association analyses highlight that genetic variants associated with variation in protein (pQTL) and transcript (eQTL) levels poorly overlap (3%). Our results demonstrate that transcriptome and proteome are governed by distinct genetic bases, likely explained by protein turnover. It also highlights the importance of integrating these different levels of gene expression to better understand the genotype-phenotype relationship.
Abstract (Nacho Molina): Biology is currently undergoing an incredible revolution, enabled by the emergence of single-cell genomics. This advancement allows for the characterization of all cell types in the human body, leading to a systematic understanding of collective cell function in health and disease. However, computational biology faces the challenge of extracting valuable information and generating reliable predictions from this wealth of data. While deep learning methods have proven to be powerful tools for clustering and denoising data, their black-box nature limits interpretability and prediction power.
To address this limitation, we present an innovative approach that combines an interpretable variational autoencoder with biophysical modeling to characterize gene regulation using single-cell sequencing data. Our model, trained on large-scale datasets, identifies key regulators responsible for the gene program of each cell type. Additionally, our approach infers gene-specific non-linear response functions that capture complex combinatorial regulations. Moreover, we applied our model to single-cell multiomics data of mouse embryonic stem cells, enabling a deeper quantitative understanding of gene expression dynamics throughout the cell cycle. Specifically, we estimated chromatin accessibility dynamics during cell cycle progression, cell-cycle dependent transcription and degradation rates for each gene, and identified key transcription factors driving the observed transcriptional dynamics.
In conclusion, our approach provides a powerful tool for analyzing and interpreting single-cell sequencing data, enabling deeper insights into the mechanisms of gene regulation.
ABOUT THE SPEAKERS
About Ivan Tarassov: he obtained his Master degree in Biochemistry & Molecular Biology in 1986 and PhD in 1990, both in Moscow State University. In 1992, he moved as a Postdoc (FEBS & EMBO fellowships) in Strasbourg, in IBMC. In 1996, he was recruited in the CNRS as CR1 and founded his own team in the GMGM unit. Between 2013 and 2023 he was the Director of the GMGM unit. In 2011, he founded the MitoCross labex and in 2022-2024 he is the coordinator of the ITI IMCBio+. His main scientific interests are mitochondrial functions and dysfunctions and mitochondrial diseases.
About Joseph Schacherer: he obtained his PhD in 2005 in molecular and cellular biology from the Louis-Pasteur University in Strasbourg, France. Following the completion of his PhD, he joined the laboratory of Leonid Kruglyak at the Lewis Sigler institute of Integrative genomics at Princeton University (New Jersey, USA), where he began work on genomic approaches to study population genomics and intraspecies phenotypic variation. In 2007, he was appointed as assistant professor of genetics and genomics at the laboratory of Genetics, Genomics and Microbiology (UMR7156, University of Strasbourg - CNRS). In 2013, he became team-leader and brought together an experienced team of researchers with expertise in population genomics, genetics, bioinformatics and data analysis crucial to set up high-throughput sequencing and phenotyping experiments and analyse the data generated. The group’s long-term goal is to use population and functional genomics to have a better insight into the rules that govern the genotype-phenotype relationship within species. Moreover, he was laureate of the National Institutes of Health (NIH) R01 grant program (2012, 2017 and 2023) and was awarded an ERC Consolidator Grant in 2018. He also led the 1002 yeast genomes project (http://1002genomes.u-strasbg.fr/). He was nominated member of the Institut Universitaire de France in 2016. And since September 2017, he is professor of genetics and genomics at the University of Strasbourg.
About Nacho Molina: he is a CNRS researcher and the group leader of the Stochastic Systems Biology Lab at the IGBMC in Strasbourg. With a background in theoretical physics, he pursued a Ph.D. in computational biology at the University of Basel where he received outstanding training in Bayesian statistics, machine learning, and gene regulation. After his PhD, he underwent postdoctoral training at EPFL where he developed a novel method combining stochastic processes with hidden Markov models to analyze transcriptional bursting in individual mammalian cells. Currently at IGBMC, the main research focus of his team lies at the interface between deep learning and biophysics, combining tools from both fields to develop mechanistic and interpretable large-scale models of gene regulation. This approach allows to analyze and integrate single-cell sequencing and imaging data and generate testable predictions based on causal mechanisms. Recently, the team has started a new line of research leveraging the strength in modeling gene expression dynamics, to understand the interplay between cell cycle regulation, pluripotency maintenance, and cell differentiation.