Name
Title | ||
|---|---|---|
Modeling Dynamics Of Cell-To-Cell Variability In Trail-Induced Apoptosis Explains Fractional Killing And Predicts Reversible Resistance |
Abstract | ||
|---|---|---|
Isogenic cells sensing identical external signals can take markedly different decisions. Such decisions often correlate with pre-existing cell-to-cell differences in protein levels. When not neglected in signal transduction models, these differences are accounted for in a static manner, by assuming randomly distributed initial protein levels. However, this approach ignores the a priori non-trivial interplay between signal transduction and the source of this cell-to-cell variability: temporal fluctuations of protein levels in individual cells, driven by noisy synthesis and degradation. Thus, modeling protein fluctuations, rather than their consequences on the initial population heterogeneity, would set the quantitative analysis of signal transduction on firmer grounds. Adopting this dynamical view on cell-to-cell differences amounts to recast extrinsic variability into intrinsic noise. Here, we propose a generic approach to merge, in a systematic and principled manner, signal transduction models with stochastic protein turnover models. When applied to an established kinetic model of TRAIL-induced apoptosis, our approach markedly increased model prediction capabilities. One obtains a mechanistic explanation of yet-unexplained observations on fractional killing and non-trivial robust predictions of the temporal evolution of cell resistance to TRAIL in HeLa cells. Our results provide an alternative explanation to survival via induction of survival pathways since no TRAIL-induced regulations are needed and suggest that short-lived anti-apoptotic protein Mcl1 exhibit large and rare fluctuations. More generally, our results highlight the importance of accounting for stochastic protein turnover to quantitatively understand signal transduction over extended durations, and imply that fluctuations of short-lived proteins deserve particular attention. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1371/journal.pcbi.1003893 | PLOS COMPUTATIONAL BIOLOGY |
Keywords | Field | DocType |
biomedical research,bioinformatics | Protein turnover,Biology,Protein biosynthesis,MCL1,Cell,Signal transduction,Bioinformatics,Merge (version control),Genetics,Model prediction,Apoptosis | Journal |
Volume | Issue | ISSN |
10 | 10 | 1553-734X |
Citations | PageRank | References |
3 | 0.54 | 5 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| François Bertaux | 1 | 6 | 1.62 |
| Szymon Stoma | 2 | 29 | 3.23 |
| Dirk Drasdo | 3 | 64 | 13.91 |
| Grégory Batt | 4 | 364 | 25.79 |