Issue |
EPJ Nonlinear Biomed Phys
Volume 4, Number 1, December 2016
The Physics Behind Systems Biology
|
|
---|---|---|
Article Number | 7 | |
Number of page(s) | 19 | |
DOI | https://doi.org/10.1140/epjnbp/s40366-016-0034-8 | |
Published online | 12 August 2016 |
https://doi.org/10.1140/epjnbp/s40366-016-0034-8
Review
The Physics behind Systems Biology
1
Institute for Systems Theory and Automatic Control, University of Stuttgart, Pfaffenwaldring 9, Stuttgart, 70569, Germany
2
School of Engineering and Science, Jacobs University Bremen, Campus Ring 1, Bremen, 28759, Germany
* e-mail: nicole.radde@ist.uni-stuttgart.de
Received:
6
June
2016
Accepted:
14
July
2016
Published online:
12
August
2016
Systems Biology is a young and rapidly evolving research field, which combines experimental techniques and mathematical modeling in order to achieve a mechanistic understanding of processes underlying the regulation and evolution of living systems.
Systems Biology is often associated with an Engineering approach: The purpose is to formulate a data-rich, detailed simulation model that allows to perform numerical (‘in silico’) experiments and then draw conclusions about the biological system. While methods from Engineering may be an appropriate approach to extending the scope of biological investigations to experimentally inaccessible realms and to supporting data-rich experimental work, it may not be the best strategy in a search for design principles of biological systems and the fundamental laws underlying Biology.
Physics has a long tradition of characterizing and understanding emergent collective behaviors in systems of interacting units and searching for universal laws. Therefore, it is natural that many concepts used in Systems Biology have their roots in Physics. With an emphasis on Theoretical Physics, we will here review the ‘Physics core’ of Systems Biology, show how some success stories in Systems Biology can be traced back to concepts developed in Physics, and discuss how Systems Biology can further benefit from its Theoretical Physics foundation.
Key words: Complex systems / Statistical physics of networks / Nonlinear dynamics / Mathematical models / Robustness / Model inference
© The Author(s), 2016