Current progress in network research: toward reference networks for key model organisms

Abstract

The collection of multiple genome-scale datasets is now routine, and the frontier of research in systems biology has shifted accordingly. Rather than clustering a single dataset to produce a static map of functional modules, the focus today is on data integration, network alignment, interactive visualization and ontological markup. Because of the intrinsic noisiness of high-throughput measurements, statistical methods have been central to this effort. In this review, we briefly survey available datasets in functional genomics, review methods for data integration and network alignment, and describe recent work on using network models to guide experimental validation. We explain how the integration and validation steps spring from a Bayesian description of network uncertainty, and conclude by describing an important near-term milestone for systems biology: the construction of a set of rich reference networks for key model organisms.

Automatic Parameter Learning for Multiple Local Network Alignment

“…As more experimental data gathers and network integration algorithms improve, network datasets with multiple data types will appear (Srinivasan et al, 2007), such as networks with interactions between proteins and DNA (Zhang et al, 2005;Tan et al, 2007), networks with physical as well as genetic interactions (Kelley and Ideker, 2005;Ulitsky et al, 2008), expression networks with boolean edges (Sahoo et al, 2007), and metabolic networks with chemical compounds (Kuhn et al, 2007). With future work to redefine Graemlin 2.0's feature functions, its scoring function and parameter learning algorithm will apply to these kinds of networks.…”

“…Graemlin 2.0 can in fact use any clustering algorithm in its disjoint local alignment stage. However, while clustering algorithms can use simple distance metrics for pairwise alignment of networks with single node and edge types, it becomes hard to define robust distance metrics for complex networks with multiple node or edge types (Srinivasan et al, 2007;Kuhn et al, 2007;Sahoo et al, 2007). Below, we present an algorithm that can use arbitrary features of a set of local alignments and generalizes to align complex networks.…”

Dividing Protein Interaction Networks by Growing Orthologous Articulations

“…Recent overviews of approaches and issues in comparative biological networks analysis are presented in [4,5]. Based on the general formulation of network alignment proposed in [3], a number of techniques for (local and global) network alignment have been introduced [6,7,8,9,10,13].…”

“…Recent studies consider a comparative approach for the analysis of PPI networks from different species in order to discover common protein groups which are likely to be related to shared relevant functional modules [3,4,5].…”

PTOMSM: A modified version of Topological Overlap Measure used for predicting Protein-Protein Interaction Network

“…Specifically, when calculating the total score for a link between protein A and protein B, all of the confidence scores of the link between A and B in each subset are provided as input to an integrating function. For example, the Bayesian method has been widely used in the integration of different data sources, and has been shown as an effective approach [3][4][5][6]. Several integrative databases of protein-protein interactions have been generated with this integration approach(e.g.…”