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Networks' characteristics are important for systems biology

Published online by Cambridge University Press:  03 September 2014

ANDREW K. RIDER ,
TIJANA MILENKOVIĆ ,
GEOFFREY H. SIWO ,
RICHARD S. PINAPATI ,
SCOTT J. EMRICH ,
MICHAEL T. FERDIG  and
NITESH V. CHAWLA
ANDREW K. RIDER
Affiliation:
Department of Computer Science and Engineering, ECK Institute for Global Health, Interdisciplinary Center for Network Science and Applications (iCeNSA), University of Notre Dame, Notre Dame, IN 46556, USA
TIJANA MILENKOVIĆ
Affiliation:
Department of Computer Science and Engineering, ECK Institute for Global Health, Interdisciplinary Center for Network Science and Applications (iCeNSA), University of Notre Dame, Notre Dame, IN 46556, USA
GEOFFREY H. SIWO
Affiliation:
Department of Biological Sciences, ECK Institute for Global Health, Interdisciplinary Center for Network Science and Applications (iCeNSA), University of Notre Dame, Notre Dame, IN 46556, USA
RICHARD S. PINAPATI
Affiliation:
Department of Biological Sciences, ECK Institute for Global Health, Interdisciplinary Center for Network Science and Applications (iCeNSA), University of Notre Dame, Notre Dame, IN 46556, USA
SCOTT J. EMRICH
Affiliation:
Department of Computer Science and Engineering, ECK Institute for Global Health, University of Notre Dame, Notre Dame, IN 46556, USA
MICHAEL T. FERDIG
Affiliation:
Department of Biological Sciences, ECK Institute for Global Health, Interdisciplinary Center for Network Science and Applications (iCeNSA), University of Notre Dame, Notre Dame, IN 46556, USA
NITESH V. CHAWLA
Affiliation:
Department of Computer Science and Engineering, ECK Institute for Global Health, Interdisciplinary Center for Network Science and Applications (iCeNSA), University of Notre Dame, Notre Dame, IN 46556, USA (e-mail: [email protected])
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Abstract

A fundamental goal of systems biology is to create models that describe relationships between biological components. Networks are an increasingly popular approach to this problem. However, a scientist interested in modeling biological (e.g., gene expression) data as a network is quickly confounded by the fundamental problem: how to construct the network? It is fairly easy to construct a network, but is it the network for the problem being considered? This is an important problem with three fundamental issues: How to weight edges in the network in order to capture actual biological interactions? What is the effect of the type of biological experiment used to collect the data from which the network is constructed? How to prune the weighted edges (or what cut-off to apply)? Differences in the construction of networks could lead to different biological interpretations.

Indeed, we find that there are statistically significant dissimilarities in the functional content and topology between gene co-expression networks constructed using different edge weighting methods, data types, and edge cut-offs. We show that different types of known interactions, such as those found through Affinity Capture-Luminescence or Synthetic Lethality experiments, appear in significantly varying amounts in networks constructed in different ways. Hence, we demonstrate that different biological questions may be answered by the different networks. Consequently, we posit that the approach taken to build a network can be matched to biological questions to get targeted answers. More study is required to understand the implications of different network inference approaches and to draw reliable conclusions from networks used in the field of systems biology.

Keywords

systems biologynetwork inference
Type
Research Article
Information
Network Science , Volume 2 , Issue 2 , August 2014 , pp. 139 - 161
Copyright
Copyright © Cambridge University Press 2014 

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