Session C-3
3:45 - 4:45

3:45 - 4:15 : Wei, Guowei (Mathematics, Michigan State University)
-Title: Differential geometry based multiscale models for biomolecular systems
This talk focuses on a new multiscale paradigm developed at MSU --- the differential geometry based multiscale models of biomolecules. Under the physiological condition, most biological processes, such as signal transduction, ion channel transport and protein folding, occur in water, which consists of 65-90 percent human cell weight. Therefore, solvent and synergy of solvent-solute are important to the understanding of biomolecular structure, function, dynamics and transport. I will discuss the use of differential geometry theory of surfaces for coupling microscopic and macroscopic scales at an equal footing. The biomolcule of interest is described by discrete atomic and quantum mechanical variables. While the aquatic invironment is described by continuum hydrodynamical variables. We derive the coupled geometric flow equation, Navier-Stokes equation, and generalized Poisson-Boltzmann equation (PBE) to describe the dynamics of the biomolecular systems. Applications will be discussed to protein folding, ion channels, micro/nanofluidics, and nano-bio sensors.
Acknowledgment: This work was supported by NSF and NIH grants.

4:15 - 4:45 : Yamada, Richard (Mathematics, University of Michigan-Ann Arbor)
-Title: Molecular Noise Enhances Oscillations in the Supra-Chiasmatic Nuclei Network
-Abstract: In this talk, we will discuss a detailed mathematical model for circadian timekeeping within the SCN. Our proposed model consists of a large population of SCN neurons, with each neuron containing a network of biochemical reactions involving the core circadian components. Using mathematical modeling, our results show that both intracellular molecular noise and intercellular coupling (nonlinear in nature) are required to sustain stochastic oscillations in the SCN oscillator network. Our work focuses on the problem of overcoming noise in oscillator systems, and our results highlight the importance of transcriptional noise in enhancing oscillations rather than dampening them. Surprisingly, our predictions from our model have been confirmed experimentally; we conclude with a short discussion of these results.