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Center for Nonlinear Studies |
In bioinformatics and systems biology research, mathematical models such as differential equation models are widely used to describe biological systems by bioinformaticians or computational biologists. At the same time, a huge amount of high-throughput experimental data are produced and readily available from the biological systems. It is quite challenging to use the extensive data to estimate the parameters in the complex systems biology models and perform rigorous inference for model structure validation and model selection. I will review the gaps between mathematical modeling and statistics inference. In particular, I will review the recent development of identifiability analysis of nonlinear ordinary differential equation (ODE) models, estimation of time-varying coefficients in ODE models, and high-dimensional ODE models for gene regulatory networks.
Host: Ming Zhang, T-6/CNLS