| Chapter title |
Automated Analysis of Intracellular Dynamic Processes
|
|---|---|
| Chapter number | 14 |
| Book title |
Light Microscopy
|
| Published in |
Methods in molecular biology, March 2017
|
| DOI | 10.1007/978-1-4939-6810-7_14 |
| Pubmed ID | |
| Book ISBNs |
978-1-4939-6808-4, 978-1-4939-6810-7
|
| Authors |
Yao Yao, Ihor Smal, Ilya Grigoriev, Maud Martin, Anna Akhmanova, Erik Meijering |
| Editors |
Yolanda Markaki, Hartmann Harz |
| Abstract |
The study of intracellular dynamic processes is of fundamental importance for understanding a wide variety of diseases and developing effective drugs and therapies. Advanced fluorescence microscopy imaging systems nowadays allow the recording of virtually any type of process in space and time with super-resolved detail and with high sensitivity and specificity. The large volume and high information content of the resulting image data, and the desire to obtain objective, quantitative descriptions and biophysical models of the processes of interest, require a high level of automation in data analysis. Two key tasks in extracting biologically meaningful information about intracellular dynamics from image data are particle tracking and particle trajectory analysis. Here we present state-of-the-art software tools for these tasks and describe how to use them. |
Mendeley readers
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 6 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 1 | 17% |
| Student > Ph. D. Student | 1 | 17% |
| Student > Bachelor | 1 | 17% |
| Unknown | 3 | 50% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 3 | 50% |
| Unknown | 3 | 50% |