Carolin Loos introduces two novel approaches for the analysis of single-cell data. Both approaches can be used to study cellular heterogeneity and therefore advance a holistic understanding of biological processes. The first method, ODE constrained mixture modeling, enables the identification of subpopulation structures and sources of variability in single-cell snapshot data. The second method estimates parameters of single-cell time-lapse data using approximate Bayesian computation and is able to exploit the temporal cross-correlation of the data as well as lineage information.

Publication in the field of natural sciences Includes supplementary material: sn.pub/extras

Autorentext

Carolin Loos is currently doing her PhD at the Institute of Computational Biology at the Helmholtz Zentrum München. She is member of the junior research group Data-driven Computational Modeling.

Inhalt

Modeling and Parameter Estimation for Single-Cell Data.- ODE Constrained Mixture Modeling for Multivariate Data.- Approximate Bayesian Computation Using Multivariate Statistics.