报告题目：Ensemble Modeling of Biological Circuits
报告时间：2018年6月21日（周四）上午10：00
报告地点：西汉姆联必威登录南校区计算机大楼A521讲学厅
报告人：Heinz-Bernd Schüttler Department of Physics and Astronomy
Center for Simulational Physics University of Georgia Athens, Georgia, USA
报告人简介：
Heinz-Bernd Schüttler教授于1984年在美国洛杉矶加利福尼亚大学获得博士学位。1984年德国雷根斯堡大学访问研究员，1987年美国佐治亚大学物理学博士后研究助理教授，1992年佐治亚大学物理和天文学系副教授，1997年佐治亚大学物理和天文学系教授，2000年-2006年任佐治亚大学物理和天文学系博士后研究主任。2011年-2013年任佐治亚大学计算机科学系主任。现在，他在佐治亚大学物理和天文学系担任教授。他的研究重点是凝聚态理论、量子多体系统的数值模拟研究、统计力学在计算生物学和系统生物学中的应用。
报告摘要
Systems biology faces the challenge of extracting meaningful biological insights from ever more detailed quantitative data which elucidate the collective inner workings of the dynamics and steady-state properties of cells, tissues, organisms and populations at the systems level. Kinetic rate equation network models, formulated in terms of systems of coupled ordinary differential equations (ODEs) or partial differential equations (PDEs), provide us, in principle, with a powerful mathematical “microscope” that allows us to “see” the new biology that is hidden in such quantitative data.
Yet, despite massive data set sizes, the available biological data are still noisy and incomplete, while unknown ODE or PDE model parameters are plentiful and hence often poorly constrained by the data. As a consequence, we are presented with the profound difficulty of making reliable quantitative model predictions in the face of substantial parametric uncertainties. The ensemble network simulation (ENS) methodology was developed to deal with this parametric uncertainty problem by way of a Monte Carlo-based probabilistic exploration of the entire high-dimensional model parameter space.
In this talk, I will give an introduction to the ENS approach in the context of ODE models describing the time evolution and steady-state properties of biological circuits. Applications of the ENS approach to gene regulatory and metabolic systems will be explored. Time permitting, I will also discuss extensions of the ENS method to extract information about biological circuit topologies, i.e., about the connectivity of unknown biological pathways, from biological time series data on metabolic and gene regulatory circuits.
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