Network-based dynamic modeling and control strategies in complex diseases
Open Access
- Author:
- Gomez Tejeda Zanudo, Jorge
- Graduate Program:
- Physics
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- February 04, 2016
- Committee Members:
- Reka Z Albert, Dissertation Advisor/Co-Advisor
Reka Z Albert, Committee Chair/Co-Chair
Dezhe Jin, Committee Member
Lu Bai, Committee Member
Timothy Reluga, Committee Member
Richard Wallace Robinett, Special Member - Keywords:
- Systems Biology
Complex Networks
Network models
Cancer - Abstract:
- In order to understand how the interactions of molecular components inside cells give rise to cellular function, creating models that incorporate the current biological knowledge while also making testable predictions that guide experimental work is of utmost importance. Creating such models is a challenging task in complex diseases such as cancer, in which numerous components are known to play an important role. To model the dynamics of the networks underlying complex diseases I use network-based models with discrete dynamics, which have been shown to reproduce the qualitative dynamics of a multitude of cellular systems while requiring only the combinatorial nature of the interactions and qualitative information on the desired/undesired states. I developed analytical and computational tools based on a type of function-dependent subnetwork that stabilizes in a steady state regardless of the state of the rest of the network, and which I termed stable motif. Based on the concept of stable motif, I proposed a method to identify a model's dynamical attractors, which have been found to be identifiable with the cell fates and cell behaviors of modeled organisms. I also proposed a stable-motif-based control method that identifies targets whose manipulation ensures the convergence of the model towards an attractor of interest. The identified control targets can be single or multiple nodes, are proven to always drive any initial condition to the desired attractor, and need to be applied only transiently to be effective. I illustrated the potential of these methods by collaborating with wet-lab cancer biologists to construct and analyze a model for a process involved in the spread of cancer cells (epithelial-mesenchymal transition), and also applied them to several published models for complex diseases, such as a type of white blood cell cancer (T-LGL leukemia). These methods allowed me to find attractors of larger models than what was previously possible, identify the subnetworks responsible for the disease and the healthy cell states, and show that stabilizing the activity of a few select components can drive the cell towards a desired fate or away from an undesired fate, the validity of which is supported by experimental work.