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PLoS Comput Biol. 2017 Mar 31;13(3):e1005456. doi: 10.1371/journal.pcbi.1005456. eCollection 2017 Mar.

Interrogating the topological robustness of gene regulatory circuits by randomization.

Huang B1,2, Lu M1,3, Jia D1,4, Ben-Jacob E1,5, Levine H1,6,7,8, Onuchic JN1,2,7,8.

Author information

Center for Theoretical Biological Physics, Rice University, Houston, TX, United States of America.
Department of Chemistry, Rice University, Houston, TX, United States of America.
The Jackson Laboratory, Bar Harbor, ME, United States of America.
Program in Systems, Synthetic and Physical Biology, Rice University, Houston, TX, United States of America.
School of Physics and Astronomy, and The Sagol School of Neuroscience, Tel-Aviv University, Tel-Aviv, Israel.
Department of Bioengineering, Rice University, Houston, TX, United States of America.
Department of Biosciences, Rice University, Houston, TX, United States of America.
Department of Physics and Astronomy, Rice University, Houston, TX, United States of America.


One of the most important roles of cells is performing their cellular tasks properly for survival. Cells usually achieve robust functionality, for example, cell-fate decision-making and signal transduction, through multiple layers of regulation involving many genes. Despite the combinatorial complexity of gene regulation, its quantitative behavior has been typically studied on the basis of experimentally verified core gene regulatory circuitry, composed of a small set of important elements. It is still unclear how such a core circuit operates in the presence of many other regulatory molecules and in a crowded and noisy cellular environment. Here we report a new computational method, named random circuit perturbation (RACIPE), for interrogating the robust dynamical behavior of a gene regulatory circuit even without accurate measurements of circuit kinetic parameters. RACIPE generates an ensemble of random kinetic models corresponding to a fixed circuit topology, and utilizes statistical tools to identify generic properties of the circuit. By applying RACIPE to simple toggle-switch-like motifs, we observed that the stable states of all models converge to experimentally observed gene state clusters even when the parameters are strongly perturbed. RACIPE was further applied to a proposed 22-gene network of the Epithelial-to-Mesenchymal Transition (EMT), from which we identified four experimentally observed gene states, including the states that are associated with two different types of hybrid Epithelial/Mesenchymal phenotypes. Our results suggest that dynamics of a gene circuit is mainly determined by its topology, not by detailed circuit parameters. Our work provides a theoretical foundation for circuit-based systems biology modeling. We anticipate RACIPE to be a powerful tool to predict and decode circuit design principles in an unbiased manner, and to quantitatively evaluate the robustness and heterogeneity of gene expression.

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