This event is part of an intensive research period:
Advanced asymptotics of PDEs, modeling and extreme statistics and their applications to data analysis in cell biology.
Latest developments in DNA sequencing and microscopy require mathematical methods to analyze biological dynamics and structures at atomic and single cell levels. As highlighted by the Human Cell Atlas Project or the GTEx consortium, exciting challenges are to provide a quantitative description of the cell and its dynamics beyond the optical resolution, requiring new mathematical and statistical tools.
The aim of this workshop will be to present reconstruction methods applied to these datasets.
The workshop will be divided into two complementary parts: first, methods of reconstruction of geometrical organization and patterns within a single cell. Analysis of trajectories from SPT, spatial reconstruction of cellular organization from RNA-seq data, and methods in machine learning for single cell image analysis.
The second part will be dedicated to the simulations, analysis and inference of dynamics associated with gene expression: transcriptional bursting models, TASEP models of mRNA translation and their application to biological complex data (such as RNA-seq and smFISH).
During the meeting one hour teaching class per day will introduce the topic to students.
The talks will also be accompanied by introductory lectures to manifold learning algorithms for dimensionality reduction and stochastic particle processes applied to gene molecular dynamics.
Please view Planned activities for more.