Optimally orienting physical networks

Integrative Approaches for Inference of Genome-Scale Gene Regulatory Networks

Transcriptional regulatory networks specify the regulatory proteins of target genes that control the context-specific expression levels of genes. With our ability to profile the different types of molecular components of cells under different conditions, we are now uniquely positioned to infer regulatory networks in diverse biological contexts such as different cell types, tissues, and time points. In this chapter, we cover two main classes of computational methods to integrate different types of information to infer genome-scale transcriptional regulatory networks. The first class of methods focuses on integrative methods for specifically inferring connections between transcription factors and target genes by combining gene expression data with regulatory edge-specific knowledge. The second class of methods integrates upstream signaling networks with transcriptional regulatory networks by combining gene expression data with protein-protein interaction networks and proteomic datasets. We conclude with a section on practical applications of a network inference algorithm to infer a genome-scale regulatory network.

Analyzing of Molecular Networks for Human Diseases and Drug Discovery

Molecular networks represent the interactions and relations of genes/proteins, and also encode molecular mechanisms of biological processes, development and diseases. Among the molecular networks, protein-protein Interaction Networks (PINs) have become effective platforms for uncovering the molecular mechanisms of diseases and drug discovery. PINs have been constructed for various organisms and utilized to solve many biological problems. In human, most proteins present their complex functions by interactions with other proteins, and the sum of these interactions represents the human protein interactome. Especially in the research on human disease and drugs, as an emerging tool, the PIN provides a platform to systematically explore the molecular complexities of specific diseases and the references for drug design. In this review, we summarized the commonly used approaches to aid disease research and drug discovery with PINs, including the network topological analysis, identification of novel pathways, drug targets and sub-network biomarkers for diseases. With the development of bioinformatic techniques and biological networks, PINs will play an increasingly important role in human disease research and drug discovery.

An optimization framework for network annotation

Motivation

A chief goal of systems biology is the reconstruction of large-scale executable models of cellular processes of interest. While accurate continuous models are still beyond reach, a powerful alternative is to learn a logical model of the processes under study, which predicts the logical state of any node of the model as a Boolean function of its incoming nodes. Key to learning such models is the functional annotation of the underlying physical interactions with activation/repression (sign) effects. Such annotations are pretty common for a few well-studied biological pathways.

Results

Here we present a novel optimization framework for large-scale sign annotation that employs different plausible models of signaling and combines them in a rigorous manner. We apply our framework to two large-scale knockout datasets in yeast and evaluate its different components as well as the combined model to predict signs of different subsets of physical interactions. Overall, we obtain an accurate predictor that outperforms previous work by a considerable margin.

Availability and implementation

The code is publicly available at https://github.com/spatkar94/NetworkAnnotation.git.

An overview of bioinformatics methods for modeling biological pathways in yeast

The advent of high-throughput genomics techniques, along with the completion of genome sequencing projects, identification of protein-protein interactions and reconstruction of genome-scale pathways, has accelerated the development of systems biology research in the yeast organism Saccharomyces cerevisiae In particular, discovery of biological pathways in yeast has become an important forefront in systems biology, which aims to understand the interactions among molecules within a cell leading to certain cellular processes in response to a specific environment. While the existing theoretical and experimental approaches enable the investigation of well-known pathways involved in metabolism, gene regulation and signal transduction, bioinformatics methods offer new insights into computational modeling of biological pathways. A wide range of computational approaches has been proposed in the past for reconstructing biological pathways from high-throughput datasets. Here we review selected bioinformatics approaches for modeling biological pathways inS. cerevisiae, including metabolic pathways, gene-regulatory pathways and signaling pathways. We start with reviewing the research on biological pathways followed by discussing key biological databases. In addition, several representative computational approaches for modeling biological pathways in yeast are discussed.

Pathway connectivity and signaling coordination in the yeast stress-activated signaling network

Stressed cells coordinate a multi-faceted response spanning many levels of physiology. Yet knowledge of the complete stress-activated regulatory network as well as design principles for signal integration remains incomplete. We developed an experimental and computational approach to integrate available protein interaction data with gene fitness contributions, mutant transcriptome profiles, and phospho-proteome changes in cells responding to salt stress, to infer the salt-responsive signaling network in yeast. The inferred subnetwork presented many novel predictions by implicating new regulators, uncovering unrecognized crosstalk between known pathways, and pointing to previously unknown ‘hubs’ of signal integration. We exploited these predictions to show that Cdc14 phosphatase is a central hub in the network and that modification of RNA polymerase II coordinates induction of stress-defense genes with reduction of growth-related transcripts. We find that the orthologous human network is enriched for cancer-causing genes, underscoring the importance of the subnetwork's predictions in understanding stress biology.

Network orientation via shortest paths

The graph orientation problem calls for orienting the edges of a graph so as to maximize the number of pre-specified source-target vertex pairs that admit a directed path from the source to the target. Most algorithmic approaches to this problem share a common preprocessing step, in which the input graph is reduced to a tree by repeatedly contracting its cycles. Although this reduction is valid from an algorithmic perspective, the assignment of directions to the edges of the contracted cycles becomes arbitrary, and the connecting source-target paths may be arbitrarily long. In the context of biological networks, the connection of vertex pairs via shortest paths is highly motivated, leading to the following problem variant: given a graph and a collection of source-target vertex pairs, assign directions to the edges so as to maximize the number of pairs that are connected by a shortest (in the original graph) directed path. This problem is NP-complete and hard to approximate to within sub-polynomial factors. Here we provide a first polynomial-size integer linear program formulation for this problem, which allows its exact solution in seconds on current networks. We apply our algorithm to orient protein-protein interaction networks in yeast and compare it with two state-of-the-art algorithms. We find that our algorithm outperforms previous approaches and can orient considerable parts of the network, thus revealing its structure and function.

AVAILABILITY AND IMPLEMENTATION

The source code is available at www.cs.tau.ac.il/∼roded/shortest.zip.

CONTACT

roded@post.tau.ac.il.

Prediction of regulation relationship between protein interactions in signaling networks

The discovery of regulation relationship of protein interactions is crucial for the mechanism research in signaling network. Bioinformatics methods can be used to accelerate the discovery of regulation relationship between protein interactions, to distinguish the activation relations from inhibition relations. In this paper, we describe a novel method to predict the regulation relations of protein interactions in the signaling network. We detected 4,417 domain pairs that were significantly enriched in the activation or inhibition dataset. Three machine learning methods, logistic regression, support vector machines(SVMs), and naïve bayes, were explored in the classifier models. The prediction power of three different models was evaluated by 5-fold cross-validation and the independent test dataset. The area under the receiver operating characteristic curve for logistic regression, SVM, and naïve bayes models was 0.946, 0.905 and 0.809, respectively. Finally, the logistic regression classifier was applied to the human proteome-wide interaction dataset, and 2,591 interactions were predicted with their regulation relations, with 2,048 in activation and 543 in inhibition. This model based on domains can be used to identify the regulation relations between protein interactions and furthermore reconstruct signaling pathways.

The approximability of shortest path-based graph orientations of protein-protein interaction networks

The graph orientation problem calls for orienting the edges of an undirected graph so as to maximize the number of prespecified source-target vertex pairs that admit a directed path from the source to the target. Most algorithmic approaches to this problem share a common preprocessing step, in which the input graph is reduced to a tree by repeatedly contracting its cycles. Although this reduction is valid from an algorithmic perspective, the assignment of directions to the edges of the contracted cycles becomes arbitrary and, consequently, the connecting source-target paths may be arbitrarily long. In the context of biological networks, the connection of vertex pairs via shortest paths is highly motivated, leading to the following variant: Given an undirected graph and a collection of source-target vertex pairs, assign directions to the edges so as to maximize the number of pairs that are connected by a shortest (in the original graph) directed path. Here we study this variant, provide strong inapproximability results for it, and propose approximation algorithms for the problem, as well as for relaxations where the connecting paths need only be approximately shortest.

Reconstruction of signaling network from protein interactions based on function annotations

The directionality of protein interactions is the prerequisite of forming various signaling networks, and the construction of signaling networks is a critical issue in the discovering the mechanism of the life process. In this paper, we proposed a novel method to infer the directionality in protein-protein interaction networks and furthermore construct signaling networks. Based on the functional annotations of proteins, we proposed a novel parameter GODS and established the prediction model. This method shows high sensitivity and specificity to predict the directionality of protein interactions, evaluated by fivefold cross validation. By taking the threshold value of GODS as 2, we achieved accuracy 95.56 percent and coverage 74.69 percent in the human test set. Also, this method was successfully applied to reconstruct the classical signaling pathways in human. This study not only provided an effective method to unravel the unknown signaling pathways, but also the deeper understanding for the signaling networks, from the aspect of protein function.