PURPOSE: In this project, we will use a combination of computational and experimental techniques to characterize p53 dynamics in healthy and cancerous cells in response to important cellular stresses. To measure the dynamics of circuit components, we will use long-term time-lapse fluorescence microscopy of living cells. We will use chemical and genetic perturbations to alter p53 dynamics and determine the effect on p53 target gene expression and cell fate. Using computational modeling, we will integrate these data with measurements of cellular outcomes to predict pathway behavior in response to specific perturbations. By allowing us to study emergent properties that are not evident at the level of smaller-scale interactions, this type of approach will provide novel strategies for manipulating circuit functions, as well as new ways to combat cancers in which p53 dynamics are dysregulated. MATERIALS AND METHODS: 1. Using synthetic biology approaches to control p53 dynamics: We will use synthetic biology approaches to perturb various characteristics of p53 dynamics (for example, p53 pulse amplitude, duration, and frequency), and determine the effect that such perturbations have on p53's downstream functions. 2. Identifying target gene expression patterns based on p53 dynamics: We will probe the function of p53 dynamics in the regulation of the over 100 p53 target genes at the single cell level. Validation by more detailed studies of important target genes will be performed using single-cell level analysis with fluorescent transcriptional reporters. PROGRESS IN FY2016: We have developed a novel synthetic method to control p53 localization, and as a consequence specific features of p53 dynamics. Using this system, we have observed differential regulation of specific p53 target genes. We are continuing our study in this direction, expanding our analysis to include the measurement of several other important targets of p53 regulation. We have also quantified the expression of large sets of p53 regulated genes in single cells, uncovering novel modes of regulation related to mRNA stability and co-regulatory subnetworks. Results from these studies have been published in Cell Systems this past year.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Investigator-Initiated Intramural Research Projects (ZIA)
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Batchelor, Eric; Loewer, Alexander (2017) Recent progress and open challenges in modeling p53 dynamics in single cells. Curr Opin Syst Biol 3:54-59
Porter, Joshua R; Telford, William G; Batchelor, Eric (2017) Single-cell Gene Expression Profiling Using FACS and qPCR with Internal Standards. J Vis Exp :
Harton, Marie D; Batchelor, Eric (2017) Determining the Limitations and Benefits of Noise in Gene Regulation and Signal Transduction through Single Cell, Microscopy-Based Analysis. J Mol Biol 429:1143-1154
Porter, Joshua R; Fisher, Brian E; Batchelor, Eric (2016) p53 Pulses Diversify Target Gene Expression Dynamics in an mRNA Half-Life-Dependent Manner and Delineate Co-regulated Target Gene Subnetworks. Cell Syst 2:272-82
Porter, Joshua R; Batchelor, Eric (2015) Using computational modeling and experimental synthetic perturbations to probe biological circuits. Methods Mol Biol 1244:259-76
Batchelor, Eric; Kann, Maricel G; Przytycka, Teresa M et al. (2013) Modeling cell heterogeneity: from single-cell variations to mixed cells. Pac Symp Biocomput :445-50
Moody, Amie D; Batchelor, Eric (2013) Promoter decoding of transcription factor dynamics. Mol Syst Biol 9:703
Purvis, Jeremy E; Karhohs, Kyle W; Mock, Caroline et al. (2012) p53 dynamics control cell fate. Science 336:1440-4
Batchelor, Eric; Loewer, Alexander; Mock, Caroline et al. (2011) Stimulus-dependent dynamics of p53 in single cells. Mol Syst Biol 7:488
Geva-Zatorsky, Naama; Dekel, Erez; Batchelor, Eric et al. (2010) Fourier analysis and systems identification of the p53 feedback loop. Proc Natl Acad Sci U S A 107:13550-5

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