The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Mathematical modeling reveals threshold mechanism in CD95-induced apoptosis
Open accessAbstract
Mathematical modeling is required for understanding the complex behavior of large signal transduction networks. Previous attempts to model signal transduction pathways were often limited to small systems or based on qualitative data only. Here, we developed a mathematical modeling framework for understanding the complex signaling behavior of CD95(APO-1/Fas)-mediated apoptosis. Defects in the regulation of apoptosis result in serious diseases such as cancer, autoimmunity, and neurodegeneration. During the last decade many of the molecular mechanisms of apoptosis signaling have been examined and elucidated. A systemic understanding of apoptosis is, however, still missing. To address the complexity of apoptotic signaling we subdivided this system into subsystems of different information qualities. A new approach for sensitivity analysis within the mathematical model was key for the identification of critical system parameters and two essential system properties: modularity and robustness. Our model describes the regulation of apoptosis on a systems level and resolves the important question of a threshold mechanism for the regulation of apoptosis.
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Citations
Cites
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Parameter uncertainty in biochemical models described by ordinary differential equations
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“When the true parameters are highly uncertain, it is sensible to compute a weighted average of local sensitivities based on the probability of the chosen parameter values [57].”
BackgroundComparative study of parameter sensitivity analyses of the TCR-activated Erk-MAPK signalling pathway
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An error model for protein quantification
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Self-citationA new strategy for assessing sensitivities in biochemical models
Sahle, S., Mendes, P., Hoops, S., & Kummer, U. (2008) · Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rsta.2008.0108 · Cited by 44
Systems biology: a way to make complex problems more understandable
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Self-citation
Metadata
- Authors
- Bentele, M., Lavrik, I., Ulrich, M., Stößer, S., Heermann, D. W., Kalthoff, H., Krammer, P. H., & Eils, R.
- Venue
- The Journal of Cell Biology
- Publisher
- Rockefeller University Press
- Published
- 2004-09-13
- Volume
- 166
- Issue
- 6
- Pages
- 839-851
- ISSN
- 0021-9525 (ISSN-L)
- 1540-8140
- OpenAlex
- W1964037688
- Cited by
- 319
- References
- 2
- FWCI
- 6.89
- Type
- journal article
- Language
- en
- Open access
- bronze
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