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Cell Mol Life Sci. 2019 Nov 25. doi: 10.1007/s00018-019-03378-w. [Epub ahead of print]

Modeling population heterogeneity from microbial communities to immune response in cells.

Author information

1
Genomics and Immunoregulation, Life and Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany.
2
Department of Internal Medicine and Radboud Center for Infectious Diseases (RCI), Radboud University Medical Center, 6525, Nijmegen, The Netherlands.
3
Genomics and Immunoregulation, Life and Medical Sciences (LIMES) Institute, University of Bonn, Bonn, Germany. asuccurro@protonmail.com.
4
West German Genome Center (WGGC), University of Bonn, Bonn, Germany. asuccurro@protonmail.com.

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.

KEYWORDS:

Computational modeling; Immune response; Microbial communities; Population heterogeneity; Systems biology

PMID:
31768606
DOI:
10.1007/s00018-019-03378-w

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