Abstracts

Gene regulatory network is essential to regulate the biological functions of cells.With the rapid development of omics technologies, the network can be inferred for acertain biological function. However, it still remains a challenge to understand thecomplex network at a systematic level. In this thesis, we utilized quantitative modelingapproaches to study the nonlinear dynamics and the design principles of these biologicalgene regulatory networks. We aim to explain the existing experimental observations withthe model, and further propose reasonable hypothesis for future experimental designs.More importantly, the understanding of the circuits regulatory mechanism would benefitthe design of a de novo gene circuit for a new biological function.We first studied the plasticity of cell migration phenotypes during cancermetastasis, which contains two key cellular plasticity mechanisms - epithelial-tomesenchymaltransition (EMT) and mesenchymal-to-amoeboid transition (MAT). In thisstudy, we quantitatively modeled the core Rac1/RhoA gene regulatory circuit for MATand later connected it with the core regulatory circuit for EMT. We found four differentstable states, consistent with the amoeboid (A), mesenchymal (M), the hybridamoeboid/mesenchymal (A/M), and the hybrid epithelial/mesenchymal (E/M)phenotypes that are observed in the experiment. We also explored the effects ofmicroRNAs and EMT-inducing signals like Hepatocyte Growth Factor (HGF), andprovided a new insight for the transitions among these phenotypes.To improve the traditional modeling approaches, we developed a newcomputational modeling method called Random Circuit Perturbation (RACIPE) toexplore the dynamic behavior of gene regulatory circuits without the requirement ofdetailed kinetic parameters. We applied RACIPE on several gene circuits, and found theexistence of robust gene expression patterns even though the model parameters are wildlyperturbed. We also showed the powerful aspect of RACIPE to decipher the operatingprinciples of the circuits.This kind of quantitative models not only works for gene regulatory network, butalso is capable to be extended to study the cell-cell interactions among cancer andimmune cells. The results shown the co-occurrence of three cancer states: low riskcancer with intermediate immunity (L), intermediate risk cancer with high immunity (I) and high risk cancer with low immunity state (H). We further used the model to assess the different combinations of cancer therapies.Advisors/Committee Members: Onuchic, Jose (advisor).