PhD opening in causal inference for live cell imaging and single-cell multi-omics data

Institut Curie is a non-profit organization bringing together France's largest center in cancer research and a model research Center and hospital group dedicated entirely to the care of cancer patients. Total personnel amount up to 1200 persons in the Research Center (2 sites) and 2000 persons in Hospitals (3 sites). The Research Center gathers about 150 staff scientist, 520 PhD students (55% from abroad), 232 postdoctoral fellows (from 64 nationalities), 350 engineers, technicians and administrative officers. It is composed of 85 groups, across 13 research units (departments) located in Paris (9 research units) and Orsay (4 research units) and a translational research department. The present project will be hosted in PCC department (CNRS-UMR168) located in downtown Paris.

Causal inference for live cell imaging and single-cell multi-omics data

Live cell imaging microscopy and next generation sequencing technologies, now routinely used in cell biology labs, produce massive amounts of time-lapse images and gene expression data at single cell resolution. However, this wealth of state-of-the-art biological data remain largely under-explored due to the lack of unsupervised methods and tools to analyze them without preconceived hypothesis. This highlights the need to develop new Machine Learning and Artificial Intelligence strategies to better exploit the richness and complexity of the information contained in time-resolved cell biology data.

The Isambert lab recently developed novel causal inference methods and tools (https://miic.curie.fr ) to learn cause-effect relationships in a variety of biological or clinical datasets, from single-cell transcriptomic and genomic alteration data (Verny et al 2017, Sella et al 2018, Desterke et al 2020) to medical records of patients (Cabeli et al 2020, Sella et al 2022, Ribeiro Dantas et al 2022) These machine learning methods combine multivariate information analysis with interpretable graphical models (Li et al 2019, Cabeli et al 2021, Ribeiro Dantas et al 2022) and outperform other methods on a broad range of benchmarks, achieving better results with only ten to hundred times fewer samples.

The first objective of the present PhD project is to extend these causal inference methods to analyze time-resolved cell biology data, for which the information about cellular dynamics can facilitate the discovery of novel cause-effect functional processes. The second objective, in collaboration with Barbara Bravi's team at Imperial College London, will be to parametrize reconstructed causal networks in order 1- to predict the course of a disease from early temporal information and 2- to generate synthetic temporal data, which will then be used to improve causal inference methods through an iterative 'adversarial' model training approach. 1These novel advanced causal inference methods for time series data will then be applied to analyze two types of high-through put time-resolved cell biology data: 1- time lapse images of i) tumour-on-chip cellular ecosystems in collaboration with Maria Carla Parrini (Inst Curie) and ii) differentiating hematopoietic stem cells in collaboration with Leïla Perié (Inst Curie) and 2- single-cell transcriptomic data of i) breast cancer under treatment in vitro in collaboration with Luca Magnani (Imperial College) and ii) differentiating hematopoietic stem cells from the Perié lab (Inst Curie).