Protein Dynamics in Individual Human Cells: Experiment and Theory

被引:51
作者
Cohen, Ariel Aharon
Kalisky, Tomer
Mayo, Avi
Geva-Zatorsky, Naama
Danon, Tamar
Issaeva, Irina
Kopito, Ronen Benjamine
Perzov, Natalie
Milo, Ron
Sigal, Alex
Alon, Uri
机构
[1] Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot
[2] Department of Bioengineering, Stanford University and Howard Hughes Medical Institute, Stanford, CA
[3] Department of Materials and Interfaces, Weizmann Institute of Science, Rehovot
[4] Department of Systems Biology, Harvard Medical School, Boston, MA
[5] Division of Biology, California Institute of Technology, Pasadena, CA
来源
PLOS ONE | 2009年 / 4卷 / 04期
基金
以色列科学基金会;
关键词
STOCHASTIC GENE-EXPRESSION; RNA-POLYMERASE-II; TRANSCRIPTION; NOISE; CYCLE; PRC1; FLUCTUATIONS; DEPENDENCE; PROTEOMICS; STABILITY;
D O I
10.1371/journal.pone.0004901
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
A current challenge in biology is to understand the dynamics of protein circuits in living human cells. Can one define and test equations for the dynamics and variability of a protein over time? Here, we address this experimentally and theoretically, by means of accurate time-resolved measurements of endogenously tagged proteins in individual human cells. As a model system, we choose three stable proteins displaying cell-cycle-dependant dynamics. We find that protein accumulation with time per cell is quadratic for proteins with long mRNA life times and approximately linear for a protein with short mRNA lifetime. Both behaviors correspond to a classical model of transcription and translation. A stochastic model, in which genes slowly switch between ON and OFF states, captures measured cell-cell variability. The data suggests, in accordance with the model, that switching to the gene ON state is exponentially distributed and that the cell-cell distribution of protein levels can be approximated by a Gamma distribution throughout the cell cycle. These results suggest that relatively simple models may describe protein dynamics in individual human cells.
引用
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页数:12
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