Survey of local and global biological network alignment: the need to reconcile the two sides of the same coin

Abstract

Analogous to genomic sequence alignment that allows for across-species transfer of biological knowledge between conserved sequence regions, biological network alignment can be used to guide the knowledge transfer between conserved regions of molecular networks of different species. Hence, biological network alignment can be used to redefine the traditional notion of a sequence-based homology to a new notion of network-based homology. Analogous to genomic sequence alignment, there exist local and global biological network alignments. Here, we survey prominent and recent computational approaches of each network alignment type and discuss their (dis)advantages. Then, as it was recently shown that the two approach types are complementary, in the sense that they capture different slices of cellular functioning, we discuss the need to reconcile the two network alignment types and present a recent first step in this direction. We conclude with some open research problems on this topic and comment on the usefulness of network alignment in other domains besides computational biology.

An Introductory Guide to Aligning Networks Using SANA, the Simulated Annealing Network Aligner

“…Since network alignment is an NP-complete problem 3 , all such algorithms must use heuristics to navigate this enormous search space. Search methods abound; several good review papers exist (Clark and Kalita, 2014;Faisal et al, 2015b;Milano et al, 2017;Guzzi and Milenković, 2017); for an extensive comparison specifically showing that SANA outperforms about a dozen of the best existing algorithms, see Mamano and Hayes (2017). SANA is virtually unique in that it was designed from the start to be able to optimize any objective function, including the objective functions introduced by other researchers; a preliminary report shows that SANA outperforms over a dozen other algorithms at optimizing their own objective functions (Kanne and Hayes, 2017).…”

Challenges and Opportunities for Visualization and Analysis of Graph-Modeled Medical Data

“…The comparison of biological and biomedical networks over such a framework enables researcher to distinguish recurrent patterns that represent biological functions that are conserved during the evolution [19]. In this way the search for conserved motifs in two networks can be formulated as the search for similar subgraphs in two or more networks [15,20,21].…”

Pairwise Versus Multiple Global Network Alignment

“…Contact: tmilenko@nd.edu * To whom correspondence should be addressed aligned networks. Since maximizing edge conservation is NP-hard (Kuchaiev and Pržulj, 2011), heuristic methods are needed for NA.Like genomic sequence alignment, NA can be local or global (Meng et al, 2016b;Guzzi and Milenković, 2017). Initial research was on local NA, which typically finds highly conserved but consequently small regions between compared networks.…”

“…More recent efforts have focused on global NA, which typically finds large but consequently suboptimally conserved network regions. Each of local NA and global NA has its (dis)advantages (Meng et al, 2016b,a;Guzzi and Milenković, 2017). In this paper, we focus on global NA, and henceforth, we refer to global NA as NA.Also, and importantly for our study, NA methods can be pairwise or multiple (Faisal et al, 2015b;Guzzi and Milenković, 2017).…”

“…Each of local NA and global NA has its (dis)advantages (Meng et al, 2016b,a;Guzzi and Milenković, 2017). In this paper, we focus on global NA, and henceforth, we refer to global NA as NA.Also, and importantly for our study, NA methods can be pairwise or multiple (Faisal et al, 2015b;Guzzi and Milenković, 2017). While pairwise NA (PNA) aligns two networks at once, multiple NA (MNA) can align more than two networks at once.…”