Modeling influenza infection and regulation of autophagy
In this project, I will develop a computational model to integrate the influenza infection and its interference with autophagy and apoptosis processes as a whole system with detail mechanistic information on how influenza proteins interacts with proteins in the signalling pathways of autophagy to interfere and evade the defence system. To be able to integrate these different parts, I will collaborate closely with experimentalists from structural biology, biochemistry, cell biology and bioinformatics to assimilate structural and interaction data, parameter measurements, imaging of temporal dynamics and integrate them with our rule-based modelling method. With this advanced modelling approach, I will incorporate detail interactions between influenza proteins and signalling proteins in autophagy and study the spatiotemporal dynamics of autophagy signalling and autophagosomes. I will use this complete model to simulate the quantitative dynamics and state transformations of all signalling molecule and proteins. It will facilitate us to derive the "signatures" of influenza infection on autophagy modulation from such simulations. The next step is to use the model and discovered "signatures" to guide the design of antibodies and vaccines to specifically target such "signatures".
Modeling immuno-receptor signaling
There are two parts in this project. The first one is a computational model of EGFR aggregation. With both equilibrium and non-equilibrium models of EGFR aggregation where EGFR is normal or mutant, I will apply advanced fitting algorithms and Bayesian inference methods to estimate parameters and select models from among alternative models. The second model is of Syk activation in IgE receptor signaling. Through collaboration with biologists at University of New Mexico and Cornell University, I will apply rule-based modeling approaches to capture the dynamics of IgE receptor signaling events in immune cells.
Ecological and evolutionary dynamics of microbial communities
I will apply two different approaches. One is using theoretical and computational methods to study the eco-evo dynamics of microbial assemblies. The other is using bioinformatic approach to explore the microbiome data then infer the mechanisms and underlying principles from data.
Kinetic modeling of metabolic networks
I will apply kinetic modeling approach to study the dynamics of metabolic networks under different regulatory scenarios.
Information processing and opinion dynamics
I will apply theoretical and computational approaches to study the information diffusion and opinion formation in social groups.
Evolutionary design principles of cellular networks
In this project, I study evolutionary dynamics and genotype(structure)-phenotype(dynamics) mapping of cellular networks using in silico, in vitro and in vivo approaches. The ultimate goal is to better understand evolution as well as cellular networks. Meanwhile, I also develop both computational and experimental techniques to address these questions. Hopefully these models and techniques can be extended and applied to biological systems at different scales.