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.
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1.1 Modeling and Parameter Estimation for Single-Cell Data |
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1.2 Contribution of this Thesis |
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2.2 Modeling Chemical Kinetics |
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3 ODE Constrained Mixture Modeling for Multivariate Data |
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3.1 Introduction and Problem Statement |
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3.2 Assessment of ODE-MMs Using Novel Data for NGF-Induced Erk Signaling |
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3.3 Modeling Variability within a Subpopulation |
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3.4 Simultaneous Analysis of Multivariate Measurements |
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3.5 Application Example: NGF-Induced Erk Signaling |
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3.0 Discussion and Outlook |
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4 Approximate Bayesian Computation Using Multivariate Statistics |
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4.1 Introduction and Problem Statement |
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4.2 Extended Introduction to Approximate Bayesian Computation |
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4.3 Approximate Bayesian Computation with Multivariate Test Statistics |
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4.4 Simulation Example: Single-Cell Time-Series of a One-Stage Model of Gene Expression |
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4.5 Discussion and Outlook |
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| Bibliography |
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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.
Lisainfo e-raamatute kohta