#6244. Experimental analysis and modeling of single-cell time-course data

September 2026publication date
Proposal available till 18-05-2025
4 total number of authors per manuscript0 $

The title of the journal is available only for the authors who have already paid for
Journal’s subject area:
Applied Mathematics;
Modeling and Simulation;
Computer Science Applications;
Biochemistry, Genetics and Molecular Biology (all);
Drug Discovery;
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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.
Keywords:
Inference; Longitudinal data; Mechanistic models; Single-cell analysis

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