Systems Biology of Cellular Signaling

To survive, cells must accurately detect intracellular and extracellular conditions and produce appropriate responses. These responses are determined by a complex network of interactions, including protein-protein interactions, transcriptional regulation, and mRNA processing. Over the past several decades, a great deal of progress has been made in identifying the specific biomolecular members of the network, as well as the interactions between the biomolecules. The next challenge is to understand how the complex dynamical interactions of the network are regulated, and how this regulation contributes to proper cellular function. This level of understanding often requires a multi-disciplinary approach, drawing upon techniques from biology, chemistry, mathematics, physics, and computer science to analyze quantitatively the biological “circuits” within the network. Ultimately, tackling this challenge not only is important for understanding the proper functioning of a cell, but also will likely provide novel therapeutic strategies for disease states in which the cellular network is dysregulated.

In our lab, we focus on quantitatively understanding the regulation and function of mammalian stress responses. These responses are important for a wide range of biological processes, including the responses to DNA damage and oncogene activation. We employ a variety of approaches to analyze stress response circuits:

• We use long-term time-lapse fluorescence microscopy to quantitatively measure dynamical changes in the concentration, localization, and activity of biological circuit components. These measurements are made at the single-cell level, which provides a wealth of information not observable from traditional measurement methods that rely on population averaging of data.

• We use synthetic biology approaches to perturb biological circuits, such as interfering with existing network connections using small molecule inhibitors or RNAi, or creating new circuit feedbacks and feed-forwards through genetic perturbations.

• We complement our experimental studies with computational modeling of biological circuits. These models enable us to more formally synthesize our experimental observations, and they serve as a predictive tool to better understand the effects of perturbations.

Our work has focused on understanding the tumor suppressor protein p53. This transcription factor is upregulated in response to numerous cellular stresses, including various forms of DNA damage. When activated, it can regulate the transcription of over a hundred genes, affecting a cell’s ability to repair damage, divide, or undergo programmed cell death if damage is too great. p53 is the most frequently protein known to be mutated in cancers, and mutations in the circuit regulating p53 are believed to occur in almost all cancers.

Single-cell level analysis of the p53 regulatory circuit revealed that p53 exhibits complex dynamical behavior. In response to one form of activating stress, DNA double strand breaks, p53 can undergo a series of repeated pulses. The amplitude (p53 concentration) and the duration of individual pulses within a cell and between cells is independent of the dose of the DNA damaging agent; however, the number of pulses increases with higher doses of damage. Additionally, individual pulses of p53 are excitable – a full p53 pulse can be triggered by either a sustained or a transient damage signal. In response to a different stress, DNA damage generated by UV radiation, p53 undergoes a distinct dynamical response. This response is graded, in that p53 shows a single pulse with amplitude and duration that directly depend on the amount of UV to which the cell is exposed. In contrast with the response to double strand breaks, this graded response to UV is not excitable. Comparison of the circuits responding to these types of damage allowed us to identify and validate , both experimentally and computationally , the molecular mechanisms responsible for the specific features of p53 dynamics we observed.

Several areas of future investigation include:

• identifying and characterizing p53’s dynamical response to other forms of stress
• determining the functional consequences of p53 dynamics on p53’s regulation of target genes
• extending these studies to in vivo contexts, such as tumor environments in living tissue
• developing therapeutic strategies to manipulate p53 dynamics as a novel therapeutic strategy
• analyzing the interactions of the p53 circuit within a larger network context
• exploring other stress response circuits that show complex dynamical behaviors

Recent Publications:

• Batchelor E, Loewer A, Mock CS, and Lahav G. Stimulus-dependent dynamics of p53 in single cells. Mol Syst Biol. 2011 May 10; 7: 488.
• Toettcher J, Mock CS, Batchelor E, Loewer A, and Lahav G. A synthetic-natural hybrid oscillator in human cells. Proc Natl Acad Sci USA. 2010, 107(39) 17047-52.
• Geva-Zatorsky N, Dekel E, Batchelor E, Lahav G, Alon U. Fourier analysis and systems identification of the p53 feedback loop. Proc Natl Acad Sci USA 2010, 107(30) 13550-5.
• Loewer A, Batchelor E, Gaglia G and Lahav G. Basal dynamics of p53 reveal transcriptionally attenuated pulses in cycling cells. Cell 2010, 142(1) 89-100.
• Batchelor E, Loewer A, and Lahav G. The ups and downs of p53: understanding protein dynamics in single cells. Nat Rev Cancer 2009, 9(5) 371-7.
• Batchelor E, Mock CS, Bhan I, Loewer A, and Lahav G. Recurrent initiation: a mechanism for triggering p53 pulses in response to DNA damage. Mol Cell 2008, 30(3) 277-289.
• Batchelor E and Goulian M. Imaging OmpR localization in Escherichia coli. Mol Microbiol 2006, 59(6) 1767-1778.