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Modeling population heterogeneity from microbial communities to immune response in cells

Authors
  • Pecht, Tal1
  • Aschenbrenner, Anna C.1, 2
  • Ulas, Thomas1
  • Succurro, Antonella1, 1
  • 1 University of Bonn,
  • 2 Radboud University Medical Center,
Type
Published Article
Journal
Cellular and Molecular Life Sciences
Publisher
Springer-Verlag
Publication Date
Nov 25, 2019
Volume
77
Issue
3
Pages
415–432
Identifiers
DOI: 10.1007/s00018-019-03378-w
PMID: 31768606
PMCID: PMC7010691
Source
PubMed Central
Keywords
License
Unknown

Abstract

Heterogeneity is universally observed in all natural systems and across multiple scales. Understanding population heterogeneity is an intriguing and attractive topic of research in different disciplines, including microbiology and immunology. Microbes and mammalian immune cells present obviously rather different system-specific biological features. Nevertheless, as typically occurs in science, similar methods can be used to study both types of cells. This is particularly true for mathematical modeling, in which key features of a system are translated into algorithms to challenge our mechanistic understanding of the underlying biology. In this review, we first present a broad overview of the experimental developments that allowed observing heterogeneity at the single cell level. We then highlight how this “data revolution” requires the parallel advancement of algorithms and computing infrastructure for data processing and analysis, and finally present representative examples of computational models of population heterogeneity, from microbial communities to immune response in cells.

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