A Guide to Conquer the Biological Network Era Using Graph Theory

Mikaela Koutrouli; Evangelos Karatzas; David Paez-Espino; Georgios A Pavlopoulos

doi:10.3389/fbioe.2020.00034

A Guide to Conquer the Biological Network Era Using Graph Theory

Front Bioeng Biotechnol. 2020 Jan 31:8:34. doi: 10.3389/fbioe.2020.00034. eCollection 2020.

Authors

Mikaela Koutrouli¹, Evangelos Karatzas^{1

2}, David Paez-Espino³, Georgios A Pavlopoulos¹

Affiliations

¹ Institute for Fundamental Biomedical Research, BSRC "Alexander Fleming", Vari, Greece.
² Department of Informatics and Telecommunications, University of Athens, Athens, Greece.
³ Lawrence Berkeley National Laboratory, Department of Energy, Joint Genome Institute, Walnut Creek, CA, United States.

Abstract

Networks are one of the most common ways to represent biological systems as complex sets of binary interactions or relations between different bioentities. In this article, we discuss the basic graph theory concepts and the various graph types, as well as the available data structures for storing and reading graphs. In addition, we describe several network properties and we highlight some of the widely used network topological features. We briefly mention the network patterns, motifs and models, and we further comment on the types of biological and biomedical networks along with their corresponding computer- and human-readable file formats. Finally, we discuss a variety of algorithms and metrics for network analyses regarding graph drawing, clustering, visualization, link prediction, perturbation, and network alignment as well as the current state-of-the-art tools. We expect this review to reach a very broad spectrum of readers varying from experts to beginners while encouraging them to enhance the field further.

Keywords: biological networks; clustering; graph theory; topology; visualization.

Publication types

Review