Table of Contents
Computational Biology Journal
Volume 2016, Article ID 4106329, 12 pages
http://dx.doi.org/10.1155/2016/4106329
Review Article

Survey of Engineering Models for Systems Biology

1Department of Chemical and Biomolecular Engineering, North Carolina State University, Raleigh, NC 27606, USA
2ACM, Morrisville, NC 27709, USA

Received 31 August 2015; Revised 18 December 2015; Accepted 20 December 2015

Academic Editor: Rituraj Purohit

Copyright © 2016 Gregory T. Reeves and Curtis E. Hrischuk. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

In recent years, the field of systems biology has emerged from a confluence of an increase both in molecular biotechnology and in computing storage and power. As a discipline, systems biology shares many characteristics with engineering. However, before the benefits of engineering-based modeling formalisms and analysis tools can be applied to systems biology, the engineering discipline(s) most related to systems biology must be identified. In this paper, we identify the cell as an embedded computing system and, as such, demonstrate that systems biology shares many aspects in common with computer systems engineering, electrical engineering, and chemical engineering. This realization solidifies the grounds for using modeling formalisms from these engineering subdisciplines to be applied to biological systems. While we document several examples where this is already happening, our goal is that identifying the cell as an embedded computing system would motivate and facilitate further discovery through more widespread use of the modeling formalisms described here.