|
 |
|
Chapter 3
Comparison of Algorithms for Analyzing Fluorescence Microscopy Images and Computation of Transcription Factor Profiles
| 3.1. |
|
Introduction |
| 3.2. |
Preliminaries |
| 3.2.1 |
Principles of GF Reporter Systems |
| 3.2.2. |
Wavelets |
| 3.2.3 |
K-Means Clustering |
| 3.2.4 |
Principle Component Analysis |
| 3.2.5 |
Mathematical Description of Digital Images and Image Analysis |
| 3.3. |
Methods |
| 3.3.1 |
Image Analysis Based on Wavelets and a bi-Directional Search |
| 3.3.2 |
Image Analysis Based on K-Means Clustering and PCA |
| 3.3.3 |
Determining Fluorescence Intensity of an Image |
| 3.3.4 |
Comparison of the Two Image Analysis Procedures |
| 3.4. |
Data Acquisition, Anticipated Results, and Interpretation |
| 3.4.1 |
Developing a model describing the relationship between the transcription factor concentration and the observed fluorescent intensity |
| 3.4.2 |
Solution of an inverse problem for determining transcription factor concentrations |
| 3.5. |
Application Notes |
| 3.6. |
Summary and Conclusions |
| |
Acknowledgements |
| |
References |
Chapter 4
Data-driven, Mechanistic Modeling of Biochemical Reaction Networks
| 4.1 |
|
Introduction |
| 4.2 |
Principles of Data-driven Modeling |
| 4.2.1 |
Types of experimental data |
| 4.2.2 |
Data processing and normalization |
| 4.2.3. |
Suitability of models used in conjunction with quantitative data |
| 4.2.4 |
Issues related to parameter specification and estimation |
| 4.3 |
Examples of Data-Driven Modeling |
| 4.3.1 |
Example 1: Systematic analysis of crosstalk in the PDGF receptor signaling network |
| 4.3.2 |
Example 2: Computational analysis of signal specificity in yeast |
| 4.4 |
Acknowledgements |
| 4.5 |
References |
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