Experimental analysis and modeling of single-cell time-course data. Issue 28 (December 2021)

• Record Type: Journal Article
• Authors: Bijman, Eline Yafelé; Kaltenbach, Hans-Michael; Stelling, Jörg
• Is Part Of: Current opinion in systems biology. Issue 28(2021)
• Publication Date: 2021-12
• Subjects: Single-cell analysis -- Longitudinal data -- Mechanistic models -- Inference; Systems biology -- Periodicals; 570
• DOI: 10.1016/j.coisb.2021.100359
• Languages: English
• ISSNs: 2452-3100

Abstract

Abstract: Contemporary single-cell experiments produce vast amounts of data, but the interpretation of these data is far from straightforward. In particular, understanding mechanisms and sources of cell-to-cell variability, given highly complex and nonlinear cellular networks, precludes intuitive interpretation. It requires careful computational and mathematical analysis instead. Here, we discuss different types of single-cell data and computational, model-based methods currently used to analyze them. We argue that mechanistic models incorporating subpopulation or cell-specific parameters can help to identify sources of variation and to understand experimentally observed behaviors. We highlight how data types and qualities, together with the nonlinearity of single-cell dynamics, make it challenging to identify the correct underlying biological mechanisms and we outline avenues to address these challenges. Graphical abstract: Image 1 Highlights: Nonlinear single-cell dynamics and parameter heterogeneity can lead to nonintuitive population behavior. Single-cell biology implies inference problems that depend on data types and assumed models. Mechanistic models incorporating subpopulation or cell-specific parameters allow to identify sources of cellular variability. Handling incomplete cell trajectories and incorporating lineage information pose substantial challenges.