TY - JOUR
T1 - Protein dynamics in individual human cells
T2 - Experiment and theory
AU - Cohen, Ariel Aharon
AU - Kalisky, Tomer
AU - Mayo, Avi
AU - Geva-Zatorsky, Naama
AU - Danon, Tamar
AU - Issaeva, Irina
AU - Kopito, Ronen Benjamine
AU - Perzov, Natalie
AU - Milo, Ron
AU - Sigal, Alex
AU - Alon, Uri
N1 - Funding Information:
We thank the Kahn Family Foundation and the Israel Science Foundation for support. We thank Michael Elbaum and his lab for discussions and assistance with the calibration of fluorescence levels.
PY - 2009/4/17
Y1 - 2009/4/17
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=65449158009&partnerID=8YFLogxK
U2 - 10.1371/journal.pone.0004901
DO - 10.1371/journal.pone.0004901
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C2 - 19381343
AN - SCOPUS:65449158009
SN - 1932-6203
VL - 4
JO - PLoS ONE
JF - PLoS ONE
IS - 4
M1 - e4901
ER -