Computational systems biology of cellular processes in Arabidopsis thaliana: an overview
Systems biology strives for gaining an understanding of biological phenomena by studying the interactions of different parts of a system and integrating the knowledge obtained into the current view of the underlying processes. This is achieved by a tight combination of quantitative experimentation a...
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| Main Authors: | , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
2020
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| In: |
Cellular and molecular life sciences
Year: 2019, Volume: 77, Issue: 3, Pages: 433-440 |
| ISSN: | 1420-9071 |
| DOI: | 10.1007/s00018-019-03379-9 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1007/s00018-019-03379-9
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| Author Notes: | Pascal Holzheu, Ursula Kummer |
| Summary: | Systems biology strives for gaining an understanding of biological phenomena by studying the interactions of different parts of a system and integrating the knowledge obtained into the current view of the underlying processes. This is achieved by a tight combination of quantitative experimentation and computational modeling. While there is already a large quantity of systems biology studies describing human, animal and especially microbial cell biological systems, plant biology has been lagging behind for many years. However, in the case of the model plant Arabidopsis thaliana, the steadily increasing amount of information on the levels of its genome, proteome and on a variety of its metabolic and signalling pathways is progressively enabling more researchers to construct models for cellular processes for the plant, which in turn encourages more experimental data to be generated, showing also for plant sciences how fruitful systems biology research can be. In this review, we provide an overview over some of these recent studies which use different systems biological approaches to get a better understanding of the cell biology of A. thaliana. The approaches used in these are genome-scale metabolic modeling, as well as kinetic modeling of metabolic and signalling pathways. Furthermore, we selected several cases to exemplify how the modeling approaches have led to significant advances or new perspectives in the field. |
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| Item Description: | Published online: 25 November 2019 Gesehen am 01.04.2020 |
| Physical Description: | Online Resource |
| ISSN: | 1420-9071 |
| DOI: | 10.1007/s00018-019-03379-9 |