An engineering design approach to systems biology

被引：17

作者：

Janes, Kevin A. ^{[1
]}

Chandran, Preethi L. ^{[2
]}

Ford, Roseanne M. ^{[3
]}

Lazzara, Matthew J. ^{[3
]}

Papin, Jason A. ^{[1
]}

Peirce, Shayn M. ^{[1
]}

Saucerman, Jeffrey J. ^{[1
]}

Lauffenburger, Douglas A. ^{[4
]}

机构：

[1] Univ Virginia, Dept Biomed Engn, Charlottesville, VA 22908 USA

[2] Howard Univ, Dept Chem Engn, Washington, DC 20059 USA

[3] Univ Virginia, Dept Chem Engn, Charlottesville, VA 22904 USA

[4] MIT, Dept Biol Engn, Cambridge, MA 02139 USA

来源：

INTEGRATIVE BIOLOGY | 2017年 / 9卷 / 07期

关键词：

RESEARCH-AND-DEVELOPMENT; COMPUTATIONAL MODELS; SIGNALING NETWORKS; CELL BIOLOGY; RESPONSES; PATHWAYS; CANCER; PRODUCTIVITY; ROBUSTNESS; ACTIVATION;

D O I：

10.1039/c7ib00014f

中图分类号：

Q2 [细胞生物学];

学科分类号：

071009 ; 090102 ;

摘要：

Measuring and modeling the integrated behavior of biomolecular-cellular networks is central to systems biology. Over several decades, systems biology has been shaped by quantitative biologists, physicists, mathematicians, and engineers in different ways. However, the basic and applied versions of systems biology are not typically distinguished, which blurs the separate aspirations of the field and its potential for real-world impact. Here, we articulate an engineering approach to systems biology, which applies educational philosophy, engineering design, and predictive models to solve contemporary problems in an age of biomedical Big Data. A concerted effort to train systems bioengineers will provide a versatile workforce capable of tackling the diverse challenges faced by the biotechnological and pharmaceutical sectors in a modern, information-dense economy.

引用

页码：574 / 583

页数：10

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