Xiaolu Guo, PhD Student

I am a visiting Ph.D. student from Peking Univ. My main research interests are landscape construction in gene network (most recently), model of auxin transportation and stem cell dynamics in plant, simulation of traffic based on big data.

My research covers several topics, model of auxin transportation and stem cell populations in plant, simulation of Beijing traffic based on big data of Beijing traffic, construction of landscape quasi-potential in gene networks such as HIV latency gene network Epithelial to Mesenchymal Transition(EMT) gene network. And recently, my research is focused on EMT landscape construction.

The most recent study has shown both in experiment and in model that there are two immediate-states between epithelial phenotype and mesenchymal phenotype when EMT occurs [1]. Furthermore, the two immediate-states have been shown numerical and theoretical existence, and the stability of these two immediate-states and the irreversibility of each transition has been analyzed through bifurcation diagram [1]. A probable potential function has been sketched to unravel the possible mechanism underlying the regulaiton system [1]. However, no strictly quantified model or simulation can give such a potential function yet, not to mention the transition rate and probability between each state and phenotype. Therefore, my research goal is to construct a quasi-potential energy landscape function for the network through Large Deviation Theory [2,3], and then to calculate the transition path, transition rate and transition probability through geometric Minimum Action Method (gMAM)[4,5].

The study will be challenging since the gene network contains so many kinds of molecule which result in a high dimensional system, since only 2D landscape has been finished[3,6]. To effectively calculate quasi-potential energy function, transition path, transition rate and switching probability, the difficulty of convergence of numerical method of high-dimensional system has to be overcome, which lead to complicated analyzing proof and demanding simulation. On the other hand, a proper and effective mathematical model of EMT could provide a thorough understanding of the cancer mechanics, and hopefully will guide the direction of medically therapy for cancer.

Publications

1. Bihai Shi, Xiaolu Guo, Ying Wang, Ken-ichiro Hayashi, Jinzhi Lei, Lei Zhang, Yuling Jiao. Feedback from Leaves Controls Shoot Apical Meristem Growth by an Auxin Transport Switch Submitted.

References

  • 1. Hong T, Watanabe K, Ta CH, Villarreal-Ponce A, Nie Q, Dai X (2015) An Ovol2-Zeb1 Mutual Inhibitory Circuit Governs Bidirectional and Multi-step Transition between Epithelial and Mesenchymal States. PLoS Comput Biol 11(11): e1004569. doi:10.1371/journal.pcbi.1004569
  • 2. Shwartz A, Weiss A (1995) Large deviations for performance analysis: Queues, communications and computing. Chapman and Hall, London.
  • 3. Lv C, Li X, Li F, Li T (2014) Constructing the Energy Landscape for Genetic Switching System Driven by Intrinsic Noise. PLoS ONE 9(2): e88167. doi:10.1371/journal.pone.0088167
  • 4. Weinan E, Ren W, Vanden-Eijnden E (2004) Minimum action method for the study of rare events. Comm Pure Appl Math 57: 637–656. 5. Heymann M, Vanden-Eijnden E (2008) The geometric minimum action method: A least action principle on the space of curves. Comm Pure Appl Math 61: 1052–1117.
  • 6. Li, C., Hong, T., & Nie, Q. (2016). Quantifying the landscape and kinetic paths for epithelial–mesenchymal transition from a core circuit. Physical Chemistry Chemical Physics, 18(27), 17949-17956.