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Analysis of Single-Cell Data: ODE Constrained Mixture Modeling and Approximate Bayesian Computation 1st ed. 2016 [Mīkstie vāki]

  • Formāts: Paperback / softback, 92 pages, height x width: 210x148 mm, weight: 1607 g, 26 Illustrations, black and white; XXI, 92 p. 26 illus., 1 Paperback / softback
  • Sērija : BestMasters
  • Izdošanas datums: 23-Mar-2016
  • Izdevniecība: Springer Spektrum
  • ISBN-10: 3658132337
  • ISBN-13: 9783658132330
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Analysis of Single-Cell Data: ODE Constrained Mixture Modeling and Approximate Bayesian Computation 1st ed. 2016Palielināt
  • Formāts: Paperback / softback, 92 pages, height x width: 210x148 mm, weight: 1607 g, 26 Illustrations, black and white; XXI, 92 p. 26 illus., 1 Paperback / softback
  • Sērija : BestMasters
  • Izdošanas datums: 23-Mar-2016
  • Izdevniecība: Springer Spektrum
  • ISBN-10: 3658132337
  • ISBN-13: 9783658132330
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9783658132330.html
Keywords:

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. 

List of Figures
xiii
List of Tables
xv
List of Abbreviations
xvii
List of Symbols
xix
1 Introduction
1(4)
1.1 Modeling and Parameter Estimation for Single-Cell Data
1(1)
1.2 Contribution of this Thesis
2(3)
2 Background
5(10)
2.1 Experimental Data
5(1)
2.2 Modeling Chemical Kinetics
6(5)
2.3 Parameter Inference
11(4)
3 ODE Constrained Mixture Modeling for Multivariate Data
15(42)
3.1 Introduction and Problem Statement
15(2)
3.2 Assessment of ODE-MMs Using Novel Data for NGF-Induced Erk Signaling
17(8)
3.3 Modeling Variability within a Subpopulation
25(15)
3.4 Simultaneous Analysis of Multivariate Measurements
40(10)
3.5 Application Example: NGF-Induced Erk Signaling
50(4)
3.0 Discussion and Outlook
54(3)
4 Approximate Bayesian Computation Using Multivariate Statistics
57(28)
4.1 Introduction and Problem Statement
57(2)
4.2 Extended Introduction to Approximate Bayesian Computation
59(6)
4.3 Approximate Bayesian Computation with Multivariate Test Statistics
65(9)
4.4 Simulation Example: Single-Cell Time-Series of a One-Stage Model of Gene Expression
74(8)
4.5 Discussion and Outlook
82(3)
5 Summary and Discussion
85(2)
Bibliography 87
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.