Personalized signaling models for personalized treatments
Dynamic mechanistic models, that is, those that can simulate behavior over time courses, are a cornerstone of molecular systems biology. They are being used to model molecular mechanisms with varying degrees of granularity?from elementary reactions to causal links?and to describe these systems by va...
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| Main Authors: | , |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
9 January 2020
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| In: |
Molecular systems biology
Year: 2020, Volume: 16, Issue: 1 |
| ISSN: | 1744-4292 |
| DOI: | 10.15252/msb.20199042 |
| Online Access: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.15252/msb.20199042 Verlag, lizenzpflichtig, Volltext: https://www.embopress.org/doi/full/10.15252/msb.20199042 
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| Author Notes: | Julio Saez-Rodriguez, & Nils Blüthgen |
| Summary: | Dynamic mechanistic models, that is, those that can simulate behavior over time courses, are a cornerstone of molecular systems biology. They are being used to model molecular mechanisms with varying degrees of granularity?from elementary reactions to causal links?and to describe these systems by various dynamic mathematical frameworks, such as Boolean networks or systems of differential equations. The models can be based exclusively on experimental data, or on prior knowledge of the underlying biological processes. The latter are typically generic, but can be adapted to a certain context, such as a particular cell type, after training with context-specific data. Dynamic mechanistic models that are based on biological knowledge have great potential for modeling specific systems, because they require less data for training to provide biological insight in particular into causal mechanisms, and to extrapolate to scenarios that are outside the conditions they have been trained on. |
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| Item Description: | Gesehen am 12.05.2020 |
| Physical Description: | Online Resource |
| ISSN: | 1744-4292 |
| DOI: | 10.15252/msb.20199042 |