Model-based extension of high-throughput to high-content data
High-quality quantitative data is a major limitation in systems biology. The experimental data used in systems biology can be assigned to one of the following categories: assays yielding average data of a cell population, high-content single cell measurements and high-throughput techniques generatin...
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| Main Authors: | , , , , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
05 August 2010
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| In: |
BMC systems biology
Year: 2010, Volume: 4, Pages: 1-13 |
| ISSN: | 1752-0509 |
| DOI: | 10.1186/1752-0509-4-106 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1186/1752-0509-4-106
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| Author Notes: | Andrea C. Pfeifer, Daniel Kaschek, Julie Bachmann, Ursula Klingmüller, Jens Timmer |
| Summary: | High-quality quantitative data is a major limitation in systems biology. The experimental data used in systems biology can be assigned to one of the following categories: assays yielding average data of a cell population, high-content single cell measurements and high-throughput techniques generating single cell data for large cell populations. For modeling purposes, a combination of data from different categories is highly desirable in order to increase the number of observable species and processes and thereby maximize the identifiability of parameters. |
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| Item Description: | Gesehen am 02.05.2023 |
| Physical Description: | Online Resource |
| ISSN: | 1752-0509 |
| DOI: | 10.1186/1752-0509-4-106 |