Integrating knowledge and omics to decipher mechanisms via large-scale models of signaling networks
Abstract Signal transduction governs cellular behavior, and its dysregulation often leads to human disease. To understand this process, we can use network models based on prior knowledge, where nodes represent biomolecules, usually proteins, and edges indicate interactions between them. Several comp...
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| Hauptverfasser: | , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
31 May 2022
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| In: |
Molecular systems biology
Year: 2022, Jahrgang: 18, Heft: 7, Pages: 1-15 |
| ISSN: | 1744-4292 |
| DOI: | 10.15252/msb.202211036 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.15252/msb.202211036 Verlag, kostenfrei, Volltext: https://www.embopress.org/doi/full/10.15252/msb.202211036 
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| Verfasserangaben: | Martin Garrido-Rodriguez, Katharina Zirngibl, Olga Ivanova, Sebastian Lobentanzer & Julio Saez-Rodriguez |
| Zusammenfassung: | Abstract Signal transduction governs cellular behavior, and its dysregulation often leads to human disease. To understand this process, we can use network models based on prior knowledge, where nodes represent biomolecules, usually proteins, and edges indicate interactions between them. Several computational methods combine untargeted omics data with prior knowledge to estimate the state of signaling networks in specific biological scenarios. Here, we review, compare, and classify recent network approaches according to their characteristics in terms of input omics data, prior knowledge and underlying methodologies. We highlight existing challenges in the field, such as the general lack of ground truth and the limitations of prior knowledge. We also point out new omics developments that may have a profound impact, such as single-cell proteomics or large-scale profiling of protein conformational changes. We provide both an introduction for interested users seeking strategies to study cell signaling on a large scale and an update for seasoned modelers. |
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| Beschreibung: | Gesehen am 25.08.2022 |
| Beschreibung: | Online Resource |
| ISSN: | 1744-4292 |
| DOI: | 10.15252/msb.202211036 |