From the bottom and up: bridging the single cell with the cell population
speaker: Stefan Engblom (Uppsala University, Sweden)abstract: In this talk I will discuss a computational framework designed speci cally for detailed modeling of populations of living cells 5. My initial examples will be taken from deterministic-stochastic hybrid models of ion channels and synapses in ring neurons 6. I will next consider some of the intricacies of capturing cell-to-cell communication and do so within a non-static population of cells 4,5. I will next review some results towards multiscale convergence in the above setting of spatial stochastic models 1,2,3. The computational challenge here is to bridge the vast scale separation inherent with these types of applications, and to provide for computational eciency enough that the model can either be e ectively parameterized given data, or homogenized using multiscale techniques. References: 1 S. Engblom, Strong convergence for split-step methods in stochastic jump kinetics, SIAM J. Numer. Anal. 53(6):2655{2676 (2015). 2 S. Engblom, Stability and Strong Convergence for Spatial Stochastic Kinetics, Chap. 3.3 in D. Holcman (editor), Stochastic Processes, Multiscale Modeling, and Numerical Methods for Computational Cellular Biology, Springer (2017). 3 A. Chevallier, and S. Engblom, Pathwise error bounds in Multiscale variable splitting methods for spatial stochastic kinetics, SIAM J. Numer. Anal. 56(1):469{498 (2018). 4 S. Engblom, Stochastic simulation of pattern formation in growing tissue: a multilevel approach, Bull. Math. Biol. (2018, to appear). 5 S. Engblom, D. B. Wilson, and R. E. Baker, Scalable population-level modeling of biological cells incorporating mechanics and kinetics in continuous time, Roy. Soc. Open Sci. (2018, to appear). 6 P. Bauer, S. Engblom, S. Mikulovic, and A. Senek, Multiscale modeling via split-step methods in neural ring, Math. Comput. Model. Dyn. Syst. (2018, to appear).
timetable:
Mon 1 Oct, 11:00 - 12:15, Aula Dini
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