Benjamin J. Hescott

New directions for diffusion-based network prediction of protein function: incorporating pathways with confidence

Motivation: It has long been hypothesized that incorporating models of network noise as well as edge directions and known pathway information into the representation of protein–protein interaction (PPI) networks might improve their utility for functional inference. However, a simple way to do this has not been obvious. We find that diffusion state distance (DSD), our recent diffusion-based metric for measuring dissimilarity in PPI networks, has natural extensions that incorporate confidence, directions and can even express coherent pathways by calculating DSD on an augmented graph. Results: We define three incremental versions of DSD which we term cDSD, caDSD and capDSD, where the capDSD matrix incorporates confidence, known directed edges, and pathways into the measure of how similar each pair of nodes is according to the structure of the PPI network. We test four popular function prediction methods (majority vote, weighted majority vote, multi-way cut and functional flow) using these different matrices on the Baker’s yeast PPI network in cross-validation. The best performing method is weighted majority vote using capDSD. We then test the performance of our augmented DSD methods on an integrated heterogeneous set of protein association edges from the STRING database. The superior performance of capDSD in this context confirms that treating the pathways as probabilistic units is more powerful than simply incorporating pathway edges independently into the network. Availability: All source code for calculating the confidences, for extracting pathway information from KEGG XML files, and for calculating the cDSD, caDSD and capDSD matrices are available from http://dsd.cs.tufts.edu/capdsd Contact: lenore.cowen@tufts.edu or benjamin.hescott@tufts.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Going the distance for protein function prediction: a new distance metric for protein interaction networks

In protein-protein interaction (PPI) networks, functional similarity is often inferred based on the function of directly interacting proteins, or more generally, some notion of interaction network proximity among proteins in a local neighborhood. Prior methods typically measure proximity as the shortest-path distance in the network, but this has only a limited ability to capture fine-grained neighborhood distinctions, because most proteins are close to each other, and there are many ties in proximity. We introduce diffusion state distance (DSD), a new metric based on a graph diffusion property, designed to capture finer-grained distinctions in proximity for transfer of functional annotation in PPI networks. We present a tool that, when input a PPI network, will output the DSD distances between every pair of proteins. We show that replacing the shortest-path metric by DSD improves the performance of classical function prediction methods across the board.

Connectedness of PPI network neighborhoods identifies regulatory hub proteins

MOTIVATIONnWith the growing availability of high-throughput protein-protein interaction (PPI) data, it has become possible to consider how a proteins local or global network characteristics predict its function.nnnRESULTSnWe introduce a graph-theoretic approach that identifies key regulatory proteins in an organism by analyzing proteins local PPI network structure. We apply the method to the yeast genome and describe several properties of the resulting set of regulatory hubs. Finally, we demonstrate how the identified hubs and putative target gene sets can be used to identify causative, functional regulators of differential gene expression linked to human disease.nnnAVAILABILITYnCode is available at http://bcb.cs.tufts.edu/hubcomps.nnnCONTACTnfox.andrew.d@gmail.com; slonim@cs.tufts.edunnnSUPPLEMENTARY INFORMATIONnSupplementary data are available at Bioinformatics online.

Evaluating Between-Pathway Models with Expression Data

Between-pathway models (BPMs) are network motifs consisting of pairs of putative redundant pathways. In this article, we show how adding another source of high-throughput data--microarray gene expression data from knockout experiments--allows us to identify a compensatory functional relationship between genes from the two BPM pathways. We evaluate the quality of the BPMs from four different studies, and we describe how our methods might be extended to refine pathways.

Non-Uniform Reductions

We study properties of non-uniform reductions and related completeness notions. We strengthen several results of Hitchcock and Pavan (ICALP (1), Lecture Notes in Computer Science, vol.xa04051, pp.xa0465–476, Springer, 2006) and give a trade-off between the amount of advice needed for a reduction and its honesty on NEXP. We construct an oracle relative to which this trade-off is optimal. We show, in a more systematic study of non-uniform reductions, among other things that non-uniformity can be removed at the cost of more queries. In line with Post’s program for complexity theory (Buhrman and Torenvliet in Bulletin of the EATCS 85, pp.xa041–51, 2005) we connect such ‘uniformization’ properties to the separation of complexity classes.

Inferring mechanisms of compensation from E-MAP and SGA data using local search algorithms for max cut.

A new method based on a mathematically natural local search framework for max cut is developed to uncover functionally coherent module and BPM motifs in high-throughput genetic interaction data. Unlike previous methods, which also consider physical protein-protein interaction data, our method utilizes genetic interaction data only; this becomes increasingly important as high-throughput genetic interaction data is becoming available in settings where less is known about physical interaction data. We compare modules and BPMs obtained to previous methods and across different datasets. Despite needing no physical interaction information, the BPMs produced by our method are competitive with previous methods. Biological findings include a suggested global role for the prefoldin complex and a SWR subcomplex in pathway buffering in the budding yeast interactome.

Diffuse Reflections in Simple Polygons

Abstract We prove a conjecture of Aanjaneya, Bishnu, and Pal that the maximum number of diffuse reflections needed for a point light source to illuminate the interior of a simple polygon with n walls is ⌊ n / 2 ⌋ − 1 . Light reflecting diffusely leaves a surface in all directions, rather than at an identical angle as with specular reflections.

Pathway centrality in protein interaction networks identifies functional mediators of pulmonary disease

Identification of functional pathways mediating molecular responses may lead to better understanding of disease processes and suggest new therapeutic approaches. We introduce a method to detect such mediating functions using topological properties of protein-protein interaction networks. We introduce the concept of pathway centrality, a measure of communication between disease genes and differentially expressed genes. We find mediating pathways for three pulmonary diseases (asthma; bronchopulmonary dysplasia (BPD); and chronic obstructive pulmonary disease (COPD)) using pathway centrality. Mediating pathways shared by all three pulmonary disorders heavily favor inflammatory or immune responses and include specific pathways such as cytokine production, NF Kappa B, and JAK/STAT signaling. Disease-specific mediators, such as insulin signaling in BPD or homeostasis in COPD, are also highlighted. We support our findings, some of which suggest new treatment approaches, both with anecdotal evidence from the literature and via systematic evaluation using genetic interactions.

Building a Molecular Taxonomy of Disease

The advent of high throughput technologies contributes to the rapid growth of molecular-level knowledge about human disease. However, existing disease taxonomies tend to focus on either physiological characterizations of disease or the organizational and billing needs of hospitals. Most fail to fully incorporate our rapidly increasing knowledge about molecular causes of disease. More modern disease taxonomies would presumably be built based on the combination of clinical, physiological, and molecular data. In this study, we analyzed our ability to infer disease relationships from molecular data alone. This approach may provide insights into how to ultimately build more modern taxonomies of disease

Tight Bounds for Active Self-Assembly Using an Insertion Primitive

We prove two limits on the behavior of a model of self-assembling particles introduced by Dabby and Chen (Proceedings of 24th ACM-SIAM Symposium on Discrete Algorithms (SODA), pp. 1526–1536, 2013), called insertion systems, where monomers insert themselves into the middle of a growing linear polymer. First, we prove that the expressive power of these systems is equal to context-free grammars, answering a question posed by Dabby and Chen. Second, we prove that systems of k monomer types can deterministically construct polymers of length