Using graph theory to analyze biological networks

By Georgios A. Pavlopoulos, Maria Secrier, Charalampos N. Moschopoulos, Theodoros G. Soldatos, S. Kossida, Jan Aerts, Reinhard Schneider, Pantelis G. Bagos. BioData Mining, 2011

792 citations recorded by the source corpus.

Understanding complex systems often requires a bottom-up analysis towards a systems biology approach. The need to investigate a system, not only as individual components but as a whole, emerges. This can be done by examining the elementary constituents individually and then how these are connected. The myriad components of a system and their interactions are best characterized as networks and they are mainly represented as graphs where thousands of nodes are connected with thousands of vertices. In this article we demonstrate approaches, models and methods from the graph theory universe and we discuss ways in which they can be used to reveal hidden properties and features of a network. This network profiling combined with knowledge extraction will help us to better understand the biological significance of the system.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/a51a630f1088baff85282993ebf14fb910ccf126

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In Molecular Biology

Citations in the corpus

Adverse outcome pathway (AOP) development I: strategies and principles. (2014, background): “In a graph theory context, KEs represent nodes and KERs represent edges (Pavlopoulos et al., 2011).”
Artificial Intelligence in Cardiovascular Imaging (2019, background): “For example, network graphs are structures that describe the relationship between objects and have played an important role in social networking, engineering, and molecular and population biology (17).”
Overcoming Catastrophic Forgetting in Graph Neural Networks (2020, background): “Examples include but are not limited to such as citation networks, social networks (Newman and Girvan 2004), and biological reaction networks (Pavlopoulos et al. 2011).”
InfoGraph: Unsupervised and Semi-supervised Graph-Level Representation Learning via Mutual Information Maximization (2019, background): “Graphs have proven to be an effective way to represent very diverse types of data including social networks [38], biological reaction networks[46], protein-protein interactions [29], the quantum mechanical properties of individual molecules [59, 20], and many more.”