Attractor Landscape Analysis Reveals Feedback Loops in the p53 Network That Control the Cellular Response to DNA Damage

Abstract

The protein p53 functions as a tumor suppressor and can trigger either cell cycle arrest or apoptosis in response to DNA damage. We used Boolean network modeling and attractor landscape analysis to analyze the state transition dynamics of a simplified p53 network for which particular combinations of activation states of the molecules corresponded to specific cellular outcomes. Our results identified five critical interactions in the network that determined the cellular response to DNA damage, and simulations lacking any of these interactions produced states associated with sustained p53 activity, which corresponded to a cell death response. Attractor landscape analysis of the cellular response to DNA damage of the breast cancer cell line MCF7 and the effect of the Mdm2 (murine double minute 2) inhibitor nutlin-3 indicated that nutlin-3 would exhibit limited efficacy in triggering cell death, because the cell death state was not induced to a large extent by simulations with nutlin-3 and instead produced a state consistent with oscillatory p53 dynamics and cell cycle arrest. Attractor landscape analysis also suggested that combining nutlin-3 with inhibition of Wip1 would synergize to stimulate a sustained increase in p53 activity and promote p53-mediated cell death. We validated this synergistic effect in stimulating p53 activity and triggering cell death with single-cell imaging of a fluorescent p53 reporter in MCF7 cells. Thus, attractor landscape analysis of p53 network dynamics and its regulation can identify potential therapeutic strategies for treating cancer.

Cancer as a disorder of patterning information: computational and biophysical perspectives on the cancer problem

“…Part of the power of information-based approaches is that they are agnostic to the details of the problem to which they are applied. Thus, information-based approaches could provide a unified framework for addressing the onset of and progression of cancer as it occurs both within a cell through changes to the attractor landscapes of the gene regulatory networks of cancer cells [435] and to the neural-like pattern memories encoded as attractors in the state space of bioelectric networks. Analyzing multiple levels of information dynamics in tandem could provide greater insights into the complexity of the cancer disease.…”

Quantifying Waddington landscapes and paths of non-adiabatic cell fate decisions for differentiation, reprogramming and transdifferentiation

“…As a result of the statistical analysis, we obtained the steady-state probability distribution, and further the potential landscape, according to U ¼ 2lnP ss [13,21,23,[25][26][27][34][35][36][37][38][39][40]. Here, P ss is the steady-state probability distribution in the state space of relative gene expression levels.…”

Population heterogeneity in the epithelial to mesenchymal transition is controlled by NFAT and phosphorylated Sp1

“…Data-driven approaches [16], boolean [60] or other logical model formulations [17,61] are emerging paradigms that constrain model complexity by the availability of the training and validation data. Other techniques such as constraints based modeling, which is commonly used to model metabolic networks, have also been applied to model transcriptional networks, although primarily in lower eukaryotes and prokaryotes [62].…”