Physics and data-based modelling of cellular decision making

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We use a multidisciplinary approach to understand the fundamental biological processes behind cellular decision making. Cellular decision making is a crucial process in the development of multicellular organisms, during which cells differentiate from a single type to the multitude of cell types that compose the adult organism. In this context, complex regulatory events occur both at the single-cell level and at the level of groups of interacting cells.

Recently developed experimental techniques have made it possible to generate a vast amount of large-scale biological data at the single-cell level. By using state-of-the-art and newly developed computational methods, we combine information obtained from the analysis of these data with the insights offered by physical models that can guide interpretation.

Our goal is to decode the molecular mechanisms underlying cellular decision making at

1. the single-cell level, by looking at the interaction between gene expression and chromatin spatial organization during decision making

2. the inter-cellular level, by dissecting the role of cellular communication in collective cellular decision making

One of the model systems we work with is early mouse embryos, where a complex array of cell differentiation and migration takes place. In close collaboration with experimental groups, we combine methods from statistics, information theory and physics to fully exploit the data and construct general, quantitative models.

  • Single-cell ‘omics techniques allow the identification of the molecular fingerprints of cells as they go
    through decision making processes, for example during mouse embryonic development
    (Scialdone et al, Nature, 2016).
  • Models from Statistical Physics can explain how a cell can mark specific copies of a gene for expression
    during fate decision and induce different chromatin spatial arrangement on identical DNA sequences
    (Scialdone et al, PlosCompBio, 2011)