Idris Kouadri Boudjelthia
Idris graduated from the University of Science and Technology Houari Boumediene in Algiers with a master degree in theoretical physics, then obtained a diploma in quantitative life sciences from the Abdus Salam International Centre for Theoretical Physics (ICTP).
He is currently working on using neural ordinary differential equations for systems biology with Guido Sanguinetti and Andrea Sottoriva.
Sessions
Reconstructing temporal cellular dynamics from static single-cell transcriptomics remains a major challenge. Methods based on RNA velocity are useful, but interpreting their results to learn new biology remains difficult. Here we show NeuroVelo, a method that couples learning of an optimal linear projection with non-linear Neural Ordinary Differential Equations. Unlike current methods, it uses dynamical systems theory to model biological processes over time, hence NeuroVelo can identify what genes and mechanisms drive the temporal cellular dynamics. We benchmark NeuroVelo against several state-of-the-art methods using single-cell datasets, demonstrating that NeuroVelo has high predictive power but is superior to competing methods in identifying the mechanisms that drive cellular dynamics over time. We also show how we can use this method to infer gene regulatory networks that drive cell fate directly from the data.