Labeling strategies for analysing cellular dynamics

There have been a variety of experimental methods, such as fluorescent dyes and cellular barcodes, to label and track individual cells over time for following their differentiation and proliferation dynamics. However, the choice of the labelling method in combination with the mathematical approaches to analyse these dynamics might affect the ability to correctly infer the desired information. This project addresses this problem by investigating the influence of labelling strategies and experimental limitations on the ability to quantify cell proliferation and differentiation dynamics trying to propose optimal strategies for different problems.

Participants: Michael Gabel; Roland R. Regoes (ETH Zurich)

Publications: Gabel et al., PLoS ONE 2017

FAMoS – A Flexible and dynamic Algorithm for Model Selection

Most biological systems are difficult to analyse due to a multitude of interacting components and the concomitant lack of information about the essential dynamics. Finding appropriate models that provide a systematic description of these biological systems can be challenging given the sheer number of possibilities. In this project, we develop a flexible model selection algorithm that performs a robust and dynamical search of large model spaces without the need for an exhaustive search of the total model space. The algorithm can be applied to a large variety of biological problems and is provided as an R-package.

Participants: Michael Gabel, Tobias Hohl

Publications: Gabel et al., PLoS Comp Biol 2019 

Mathematical methods to quantify viral transmission modes

Mathematical models that describe the changing concentration of virus and cells over time play an important role when analyzing viral infection dynamics. However, previous modeling approaches insufficiently describe viral transmission dynamics in spatially defined settings and can lead to inappropriate estimates. In this project, we develop simple model extensions that provide correct description of cell-to-cell transmission dynamics kinetics among stationary cells in comparison to standard virus dynamics models while still being applicable to standard cell population-based measurements.  

Participants: Peter Kumberger; Susan L. Uprichard, Karina Durso-Cain, Harel Dahari (Loyola Univ. Chicago)

Publications: Kumberger et al., Viruses 2018, Durso-Cain et al., Viruses 2021

Resources: https://github.com/GrawLab/HCVspread