Experimental design and model reduction in systems biology

作     者：Jenny E.Jeong Qinwei Zhuang Mark K.Transtrum Enlu Zhou Peng Qiu 

作者机构：School of Electrical and Computer EngineeringGeorgia Institute of TechnologyAtlantaGA 30318USA School of Biological SciencesGeorgia Institute of TechnologyAtlantaGA 30318USA Department of Physics and AstronomyBrigham Young UniversityProvoUT 84602USA School of Industrial and Systems EngineeringGeorgia Institute of TechnologyAtlantaGA 30318USA Department of Biomedical EngineeringGeorgia Institute of TechnologyAtlantaGA 30318USA、Emory UniversityAtlanta GA 30322USA 

基　　金：funding from the National Science Foundation (CCF1552784) 

出 版 物：《Frontiers of Electrical and Electronic Engineering in China》 (中国电气与电子工程前沿（英文版）)

年 卷 期：2018年第6卷第4期

页      码：287-306页

摘      要：In systems biology, the dynamics of biological networks are often modeled with ordinary differential equations (ODEs) that encode interacting components in the systems, resulting in highly complex models. In contrast, the amount of experimentally available data is almost always limited, and insufficient to constrain the parameters. In this situation, parameter estimation is a very challenging problem. To address this challenge, two intuitive approaches are to perform experimental design to generate more data, and to perform model reduction to simplify the model. Experimental design and model reduction have been traditionally viewed as two distinct areas, and an extensive literature and excellent reviews exist on each of the two areas. Intriguingly, however, the intrinsic connections between the two areas have not been recognized. Experimental design and model reduction are deeply related, and can be considered as one unified framework. There are two recent methods that can tackle both areas, one based on model manifold and the other based on profile likelihood. We use a simple sum-of-two-exponentials example to discuss the concepts and algorithmic details of both methods, and provide Matlab-based code and implementation which are useful resources for the dissemination and adoption of experimental design and model reduction in the biology community. From a geometric perspective, we consider the experimental data as a point in a high-dimensional data space and the mathematical model as a manifold living in this space. Parameter estimation can be viewed as a projection of the data point onto the manifold. By examining the singularity around the projected point on the manifold, we can perform both experimental design and model reduction. Experimental design identifies new experiments that expand the manifold and remove the singularity, whereas model reduction identifies the nearest boundary, which is the nearest singularity that suggests an appropriate form of a reduced

主 题 词：experimental design model reduction model manifold profile likelihood 

学科分类：0710[理学-生物科学类] 07[理学] 09[农学] 

核心收录：

D　O　I：10.1007/S40484-018-0150-9

馆 藏 号：203415534...