The ability of signaling networks to detect, process, and react specifically to various stress signals is a key property of living cells. To characterize these oftentimes overwhelmingly complex signal transduction pathways, one promising approach is to use quantitative experiments in synergy with modeling and simulations. We focus on the network underlying the stress response of p53, a pivotal player in cancer initiation and prevention aside from its contribution to numerous other aspects of disease and normal life. Lying at the heart of intricate regulations is the autoregulatory feedback of p53 by MDM2, a critical negative regulator of p53. As most human malignancies shut down the p53 tumor-suppressing responses to survive, p53 and MDM2 are of the most promising targets for drug intervention in cancer therapy. Meanwhile, microRNAs (miRNAs) have emerged as a key regulatory player in nearly every cellular process and intriguingly recent studies show that miRNAs have direct interactions with the p53-MDM2 core. It is safe to assume that the coupled pathways formed by miRNAs and p53-MDM2 play a fundamental role in cellular health. We propose to develop novel methods to theoretically assess the interactions between p53-MDM2 and miRNAs, and experimentally rewire the miRNA-p53-MDM2 network. We propose the experimental implementation of novel molecular circuits engineered to respond to microRNAs introducing closed-loop feedback control to revive the p53 function. We believe that our proposal can provide new insight to medical strategies in the treatment of p53-dependent human cancers.

Public Health Relevance

We propose novel theoretical methods to examine the relationship between p53-MDM2 and microRNAs, and experimentally rewire the miRNA-p53-MDM2 core. Advances in understanding of the interplay between p53 response and miRNAs regulation can provide new insight to existing treatment modalities. Importantly, our work will aim to redefine medical strategies, introducing closed-loop molecular interventions in the treatment of p53-dependent human cancers.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Exploratory/Developmental Grants (R21)
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Modeling and Analysis of Biological Systems Study Section (MABS)
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Li, Jerry
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University of Texas-Dallas
Engineering (All Types)
Schools of Engineering
United States
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Li, Yi; Mendiratta, Saurabh; Ehrhardt, Kristina et al. (2016) Exploiting the CRISPR/Cas9 PAM Constraint for Single-Nucleotide Resolution Interventions. PLoS One 11:e0144970
Moore, Richard; Ooi, Hsu Kiang; Kang, Taek et al. (2015) MiR-192-Mediated Positive Feedback Loop Controls the Robustness of Stress-Induced p53 Oscillations in Breast Cancer Cells. PLoS Comput Biol 11:e1004653
Ehrhardt, Kristina; Guinn, Michael T; Quarton, Tyler et al. (2015) Reconfigurable hybrid interface for molecular marker diagnostics and in-situ reporting. Biosens Bioelectron 74:744-50
Shimoga, Vinay; White, Jacob T; Li, Yi et al. (2013) Synthetic mammalian transgene negative autoregulation. Mol Syst Biol 9:670
Li, Yi; Moore, Richard; Guinn, Michael et al. (2012) Transcription activator-like effector hybrids for conditional control and rewiring of chromosomal transgene expression. Sci Rep 2:897