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) to medical records of patients (Cabeli et al 2020). These
machine learning methods combine multivariate information analysis with
interpretable graphical models (Li et al 2019) and outperform other methods on
a broad range of benchmarks, achieving better results with only ten to hundred
times fewer samples.
The 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. These novel 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 cellular systems (Marinkovic et al
2019) from the Hersen lab (Institut Curie) to analyze
cell cycle progression and apoptosis in tumor-on-chip devices, in collaboration
with the Martinelli lab (University of Rome Tor Vergata, Italy), and 2-
single-cell multi-omics data on a cellular therapy against multiple sclerosis
in collaboration with the Fillatreau lab (Institut Necker).