Robert Patro

Global network alignment using multiscale spectral signatures

MOTIVATIONnProtein interaction networks provide an important system-level view of biological processes. One of the fundamental problems in biological network analysis is the global alignment of a pair of networks, which puts the proteins of one network into correspondence with the proteins of another network in a manner that conserves their interactions while respecting other evidence of their homology. By providing a mapping between the networks of different species, alignments can be used to inform hypotheses about the functions of unannotated proteins, the existence of unobserved interactions, the evolutionary divergence between the two species and the evolution of complexes and pathways.nnnRESULTSnWe introduce GHOST, a global pairwise network aligner that uses a novel spectral signature to measure topological similarity between subnetworks. It combines a seed-and-extend global alignment phase with a local search procedure and exceeds state-of-the-art performance on several network alignment tasks. We show that the spectral signature used by GHOST is highly discriminative, whereas the alignments it produces are also robust to experimental noise. When compared with other recent approaches, we find that GHOST is able to recover larger and more biologically significant, shared subnetworks between species.nnnAVAILABILITYnAn efficient and parallelized implementation of GHOST, released under the Apache 2.0 license, is available at http://cbcb.umd.edu/kingsford_group/ghostnnnCONTACTnrob@cs.umd.edu.

Parsimonious reconstruction of network evolution

BackgroundUnderstanding the evolution of biological networks can provide insight into how their modular structure arises and how they are affected by environmental changes. One approach to studying the evolution of these networks is to reconstruct plausible common ancestors of present-day networks, allowing us to analyze how the topological properties change over time and to posit mechanisms that drive the networks’ evolution. Further, putative ancestral networks can be used to help solve other difficult problems in computational biology, such as network alignment.ResultsWe introduce a combinatorial framework for encoding network histories, and we give a fast procedure that, given a set of gene duplication histories, in practice finds network histories with close to the minimum number of interaction gain or loss events to explain the observed present-day networks. In contrast to previous studies, our method does not require knowing the relative ordering of unrelated duplication events. Results on simulated histories and real biological networks both suggest that common ancestral networks can be accurately reconstructed using this parsimony approach. A software package implementing our method is available under the Apache 2.0 license athttp://cbcb.umd.edu/kingsford-group/parana.ConclusionsOur parsimony-based approach to ancestral network reconstruction is both efficient and accurate. We show that considering a larger set of potential ancestral interactions by not assuming a relative ordering of unrelated duplication events can lead to improved ancestral network inference.

A robust and rotationally invariant local surface descriptor with applications to non-local mesh processing

In recent years, we have witnessed a striking increase in research concerning how to describe a meshed surface. These descriptors are commonly used to encode mesh properties or guide mesh processing, not to augment existing computations by replication. In this work, we first define a robust surface descriptor based on a local height field representation, and present a transformation via the extraction of Zernike moments. Unlike previous work, our local surface descriptor is innately rotationally invariant. Second, equipped with this novel descriptor, we present SAMPLE - similarity augmented mesh processing using local exemplars - a method which uses feature neighbourhoods to propagate mesh processing done in one part of the mesh, the local exemplar, to many others. Finally, we show that SAMPLE can be used in a number of applications, such as detail transfer and parameterization.

Modeling and Visualization of Human Activities for Multicamera Networks

Multicamera networks are becoming complex involving larger sensing areas in order to capture activities and behavior that evolve over long spatial and temporal windows. This necessitates novel methods to process the information sensed by the network and visualize it for an end user. In this paper, we describe a system for modeling and on-demand visualization of activities of groups of humans. Using the prior knowledge of the 3D structure of the scene as well as camera calibration, the system localizes humans as they navigate the scene. Activities of interest are detected by matching models of these activities learnt a priori against the multiview observations. The trajectories and the activity index for each individual summarize the dynamic content of the scene. These are used to render the scene with virtual 3D human models that mimic the observed activities of real humans. In particular, the rendering framework is designed to handle large displays with a cluster of GPUs as well as reduce the cognitive dissonance by rendering realistic weather effects and illumination. We envision use of this system for immersive visualization as well as summarization of videos that capture group behavior.

The missing models: a data-driven approach for learning how networks grow

Probabilistic models of network growth have been extensively studied as idealized representations of network evolution. Models, such as the Kronecker model, duplication-based models, and preferential attachment models, have been used for tasks such as representing null models, detecting anomalies, algorithm testing, and developing an understanding of various mechanistic growth processes. However, developing a new growth model to fit observed properties of a network is a difficult task, and as new networks are studied, new models must constantly be developed. Here, we present a framework, called GrowCode, for the automatic discovery of novel growth models that match user-specified topological features in undirected graphs. GrowCode introduces a set of basic commands that are general enough to encode several previously developed models. Coupling this formal representation with an optimization approach, we show that GrowCode is able to discover models for protein interaction networks, autonomous systems networks, and scientific collaboration networks that closely match properties such as the degree distribution, the clustering coefficient, and assortativity that are observed in real networks of these classes. Additional tests on simulated networks show that the models learned by GrowCode generate distributions of graphs with similar variance as existing models for these classes.

Social Snapshot: A System for Temporally Coupled Social Photography

Social Snapshot actively acquires and reconstructs temporally dynamic data. The system enables spatiotemporal 3D photography using commodity devices, assisted by their auxiliary sensors and network functionality. It engages users, making them active rather than passive participants in data acquisition.

MDMap: A system for data-driven layout and exploration of molecular dynamics simulations

Contemporary molecular dynamics simulations result in a glut of simulation data, making analysis and discovery a difficult and burdensome task. We present MDMap, a system designed to summarize long-running molecular dynamics (MD) simulations. We represent a molecular dynamics simulation as a state transition graph over a set of intermediate (stable and semi-stable) states. The transitions amongst the states together with their frequencies represent the flow of a biomolecule through the trajectory space. MDMap automatically determines potential intermediate conformations and the transitions amongst them by analyzing the conformational space explored by the MD simulation. MDMap is an automated system to visualize MD simulations as state-transition diagrams, and can replace the current tedious manual layouts of biomolecular folding landscapes with an automated tool. The layout of the representative states and the corresponding transitions among them is presented to the user as a visual synopsis of the long-running MD simulation. We compare and contrast multiple presentations of the state transition diagrams, such as conformational embedding, and spectral, hierarchical, and force-directed graph layouts. We believe this system could provide a road-map for the visualization of other stochastic time-varying simulations in a variety of different domains.

Resolving spatial inconsistencies in chromosome conformation data

We introduce a new method for filtering noisy 3C interactions that selects subsets of interactions that obey metric constraints of various strictness. We demonstrate that, although the problem is computationally hard, near-optimal results are often attainable in practice using well-designed heuristics and approximation algorithms. Further, we show that, compared with a standard technique, this metric filtering approach leads to (a) subgraphs with higher total statistical significance, (b) lower embedding error, (c) lower sensitivity to initial conditions of the embedding algorithm, and (d) structures with better agreement with light microscopy measurements.

Speeding Up Particle Trajectory Simulations Under Moving Force Fields using Graphic Processing Units

In this paper, we introduce a graphic processing unit (GPU)-based framework for simulating particle trajectories under both static and dynamic force fields. By exploiting the highly parallel nature of the problem and making efficient use of the available hardware, our simulator exhibits a significant speedup over its CPU-based analog. We apply our framework to a specific experimental simulation: the computation of trapping probabilities associated with micron-sized silica beads in optical trapping workbenches. When evaluating large numbers of trajectories (4096), we see approximately a 356 times speedup of the GPU-based simulator over its CPU-based counterpart. [DOI: 10.1115/1.4005718]

Saliency guided summarization of molecular dynamics simulations

We present a novel method to measure saliency in molecular dynamics simulation data. This saliency measure is based on a multiscale center-surround mechanism, which is fast and efficient to compute. We explore the use of the saliency function to guide the selection of representative and anomalous timesteps for summarization of simulations. To this end, we also introduce a multiscale keyframe selection procedure which automatically provides keyframes representing the simulation at varying levels of coarseness. We compare our saliency guided keyframe approach against other methods, and show that it consistently selects superior keyframes as measured by their predictive power in reconstructing the simulation.