Research Summary

Our lab aims to determine how the dynamic behavior of biological signals is controlled and how it affects cellular responses. We perform fluorescence microscopy time-lapse movies to quantitatively measure proteins dynamics in single living human cells in response to different stimuli and we use mathematical modeling to integrate this information. In the long term, we hope to be able to understand the overall properties of a network based on its structure and dynamics, and to predict how a network will behave in response to new stimuli, or how a network can be modified or rebuilt to give a desired output.

We focus on the p53 signaling pathway, one of the most important pathway protecting organisms from developing cancer. We recently discovered that p53 levels show highly unexpected undamped oscillatory behavior in response to DNA damage but the number of oscillatory pulses seen in individual cells varies from cell to cell; in previous population studies, the pulses in different cells were averaged together, giving an appearance of damped oscillations. We now aim to identify the molecular mechanism leading to p53 oscillations and the feedbacks that control their amplitude and duration. We perform accurate quantitative measurements of the temporal dynamics of the p53 response and its outcomes in single living cells. We integrate this information into a quantitative dynamical model with the goal of predicting p53 dynamics in response to various stimuli. We also use genetic and pharmacological tools for manipulating the behavior of the p53 network in order to examine the downstream effects of p53 dynamics.

We apply the same single cell method used for studying p53 dynamic behavior to additional proteins in other signaling systems. We select well-defined, basic biological pathways, where much is known about the components and their interactions, and study how each pathway functions as a system to process information in response to different stimuli. Our long term goal is to form a dictionary of functional circuit elements connecting their structure, dynamical behavior and function in human cells.

Legend to the Figure: Human cells expressing p53-CFP (green) and Mdm2-YFP (red) after DNA damage.

Click to enlarge

Selected References

Naama Geva -Zatorsky, Nitzan Rosenfeld, Shalev Itzkovitz, Ron Milo, Alex Sigal, Erez Dekel, Talia Yarnitsky, Paz Pollack, Yuvalal Liron, Zvi Kam, Galit Lahav and Uri Alon. Oscillations and variability in the p53 system. Molecular Systems Biology, 2006; 2: E1-E13.

Alex Sigal, Ron Milo, Ariel Cohen, Naama Geva-Zatorsky, Yael Klein, Inbal Alaluf, Naamah Swerdlin, Natalie Pertsov, Tamar Danon, Tal Raveh, Anne Carpenter, Galit Lahav and Uri Alon. Widespread cell-cycle dependence of nuclear proteins reveals by dynamic proteomics in individual living cells. Nature Methods, 2006: 3(7):525-31.

Alexander Loewer and Galit Lahav. Cellular Conference Call: External Feedback Affects Cell Fate Decisions. Cell, 2006; 124: 1128-1130.

Lahav G, Rosenfeld N, Sigal A, Geva-Zatorsky N, Levine AJ, Elowitz MB and Alon U. Dynamics of the p53-mdm2 feedback loop in individual cells. Nature Genetics 2004; 36 (2):147-50. 

Lahav G. The Strength of Indecisiveness: Oscillatory Behavior for Better Cell Fate Determination. Science STKE. 2004; 264, pe55;

Kassir Y, Adir N, Boger-Nadjar E, Guttmann-Raviv N, Rubin-Bejerano R, Sagee S and Shenhar G. Transcriptional regulation of meiosis in budding yeast. Int Rev Cytol.  2003; 224:111-71  

Shenhar G and Kassir Y. A positive regulator of mitosis, Sok2, functions as a negative regulator of meiosis in Saccharomyces cerevisiae. Mol. Cell. Biol. 2001; 21(5):1603-12.  

Sagee S, Sherman A, Shenhar G, Robzyk K, Ben-Dov N, Simchen G and Kassir Y. Multiple and distinct Activation and Repression Sequences mediate the regulate transcription of IME1, a transcriptional activator of meiosis specific genes in Saccharomyces cerevisiae. (S.S, A.S and G.S contributed equally to this article). Mol. Cell. Biol. 1998; 18 (4):1985-95.