Algorithmic and analytical methods in network biology

Abstract

During the genomic revolution, algorithmic and analytical methods for organizing, integrating, analyzing, and querying biological sequence data proved invaluable. Today, increasing availability of high-throughput data pertaining to functional states of biomolecules, as well as their interactions, enables genome-scale studies of the cell from a systems perspective. The past decade witnessed significant efforts on the development of computational infrastructure for large-scale modeling and analysis of biological systems, commonly using network models. Such efforts lead to novel insights into the complexity of living systems, through development of sophisticated abstractions, algorithms, and analytical techniques that address a broad range of problems, including the following: (1) inference and reconstruction of complex cellular networks; (2) identification of common and coherent patterns in cellular networks, with a view to understanding the organizing principles and building blocks of cellular signaling, regulation, and metabolism; and (3) characterization of cellular mechanisms that underlie the differences between living systems, in terms of evolutionary diversity, development and differentiation, and complex phenotypes, including human disease. These problems pose significant algorithmic and analytical challenges because of the inherent complexity of the systems being studied; limitations of data in terms of availability, scope, and scale; intractability of resulting computational problems; and limitations of reference models for reliable statistical inference. This article provides a broad overview of existing algorithmic and analytical approaches to these problems, highlights key biological insights provided by these approaches, and outlines emerging opportunities and challenges in computational systems biology.

FIGURE 1

Description of omic data sets in the context of central dogma.

FIGURE 2

Illustration of the general principles of common computational methods for predicting protein–protein interactions. In the upper panel, black and white boxes, respectively, indicate existence and absence of a homolog in the corresponding genome. In the lower panel, the red and green shades of boxes, respectively, indicate the degree of up- and down-regulation of the coding gene with respect to the corresponding condition.

FIGURE 3

Inferring domain–domain interactions (DDIs) from protein–protein interactions (PPIs). Given the domain decomposition of proteins and a set of PPIs, DDI inference methods target identification of DDIs that mediate these interactions. Different formulations of the problem optimize different criteria, leading to different solutions for DDI inference problem.

FIGURE 4

Arp 2/3 complex, which plays a significant role in the regulation of actin cytoskeleton, is identified as a conserved subnetwork through mining of protein–protein interaction networks of multiple organisms, using a fast algorithm that relies on contraction of ortholog proteins. The conserved subnetwork is shown on the left with nodes annotated by cluisters of ortholog groups (COG) identifiers. The occurrence of the subnetwork in three eukaryotic organisms is shown on the right. Dashed links indicate indirect interactions. Such knowledge discovery based analyses are likely to lead to the construction of canonical module libraries.

FIGURE 5

Screenshots from a sample computational tool, NARADA, that enables identification and browsing of canonical network patterns in regulatory networks. NARADA takes gene regulatory networks and functional annotation of individual genes as input and processes queries on regulatory pathways that involve specific biological processes (e.g., what are the processes that regulate ciliary or flagellar motility in E. coli? Are these regulatory pathways mediated by other processes?). NARADA is available as an open source at http://www.cs.purdue.edu/~jpandey/narada/. With the availability of such sophisticated tools, browsing basic biological information becomes a visually rich and interactive activity, moving beyond basic text and database searches.

FIGURE 6

Overview of common approaches to network based functional annotation. In each hypothetical example, the proteins with known function are annotated by a symbol that represents their function. Proteins with unknown function are labeled with question marks. As seen on the left, connectivity/modularity based schemes transfer function based on direct interactions. As seen in the middle, proximity based schemes diffuse function through the network. Finally, as seen on the right, pattern based schemes derive templates of functional interactions and interpolate these patterns accordingly to infer novel functions for proteins.

FIGURE 7

Framework for the integration of omic data for the discovery of subnetworks implicated in complex phenotypes. Proteomic screening provides functional data for a limited set of proteins, transcriptomic screening provides genome-scale data on mRNA expression, and curated or high-throughput protein–protein interactions provide a framework for the integration of these two complementary, valuable sources of data. This framework also illustrates how researchers can couple specific data sets generated in their labs with public data to broaden the scope of their analyses.