Model calibration and uncertainty analysis in signaling networks
- For a long time the biggest challenges in modeling cellular signal transduction networks has been the inference of crucial pathway components and the qualitative description of their interactions. As a result of the emergence of powerful high-throughput experiments, it is now possible to measure data of high temporal and spatial resolution and to analyze signaling dynamics quantitatively. In addition, this increase of high-quality data is the basis for a better understanding of model limitations and their influence on the predictive power of models. We review established approaches in signal transduction network modeling with a focus on ordinary differential equation models as well as related developments in model calibration. As central aspects of the calibration process we discuss possibilities of model adaptation based on data-driven parameter optimization and the concomitant objective of reducing model uncertainties.
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| Author: | Tim Heinemann, Andreas RaueORCiDGND |
|---|---|
| URN: | urn:nbn:de:bvb:384-opus4-1132030 |
| Frontdoor URL | https://opus.bibliothek.uni-augsburg.de/opus4/113203 |
| ISSN: | 0958-1669OPAC |
| Parent Title (English): | Current Opinion in Biotechnology |
| Publisher: | Elsevier BV |
| Place of publication: | Amsterdam |
| Type: | Article |
| Language: | English |
| Year of first Publication: | 2016 |
| Publishing Institution: | Universität Augsburg |
| Release Date: | 2024/06/03 |
| Volume: | 39 |
| First Page: | 143 |
| Last Page: | 149 |
| DOI: | https://doi.org/10.1016/j.copbio.2016.04.004 |
| Institutes: | Fakultät für Angewandte Informatik |
| Fakultät für Angewandte Informatik / Institut für Informatik | |
| Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Modellierung und Simulation biologischer Prozesse | |
| Dewey Decimal Classification: | 6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit |
| Licence (German): |  CC-BY-NC-ND 4.0: Creative Commons: Namensnennung - Nicht kommerziell - Keine Bearbeitung (mit Print on Demand) |
