ATria: a novel centrality algorithm applied to biological networks

Background The notion of centrality is used to identify ?important? nodes in social networks. Importance of nodes is not well-defined, and many different notions exist in the literature. The challenge of defining centrality in meaningful ways when network edges can be positively or negatively weighted has not been adequately addressed in the literature. Existing centrality algorithms also have a second shortcoming, i.e., the list of the most central nodes are often clustered in a specific region of the network and are not well represented across the network. Methods We address both by proposing Ablatio Triadum (ATria), an iterative centrality algorithm that uses the concept of ?payoffs? from economic theory. Results We compare our algorithm with other known centrality algorithms and demonstrate how ATria overcomes several of their shortcomings. We demonstrate the applicability of our algorithm to synthetic networks as well as biological networks including bacterial co-occurrence networks, sometimes referred to as microbial social networks. Conclusions We show evidence that ATria identifies three different kinds of ?important? nodes in microbial social networks with different potential roles in the community

Inferring directional relationships in microbial communities using signed Bayesian networks

Background: Microbe-microbe and host-microbe interactions in a microbiome play a vital role in both health and disease. However, the structure of the microbial community and the colonization patterns are highly complex to infer even under controlled wet laboratory conditions. In this study, we investigate what information, if any, can be provided by a Bayesian Network (BN) about a microbial community. Unlike the previously proposed Co-occurrence Networks (CoNs), BNs are based on conditional dependencies and can help in revealing complex associations. Results: In this paper, we propose a way of combining a BN and a CoN to construct a signed Bayesian Network (sBN). We report a surprising association between directed edges in signed BNs and known colonization orders. Conclusions: BNs are powerful tools for community analysis and extracting influences and colonization patterns, even though the analysis only uses an abundance matrix with no temporal information. We conclude that directed edges in sBNs when combined with negative correlations are consistent with and strongly suggestive of colonization order

Metagenomics, Metatranscriptomics, and Metabolomics Approaches for Microbiome Analysis

Microbiomes are ubiquitous and are found in the ocean, the soil, and in/on other living organisms. Changes in the microbiome can impact the health of the environmental niche in which they reside. In order to learn more about these communities, different approaches based on data from mul-tiple omics have been pursued. Metagenomics produces a taxonomical profile of the sample, metatranscriptomics helps us to obtain a functional profile, and metabolomics completes the picture by determining which byproducts are being released into the environment. Although each approach provides valuable information separately, we show that, when combined, they paint a more comprehensive picture. We conclude with a review of network-based approaches as applied to integrative studies, which we believe holds the key to in-depth understanding of microbiomes

MATria: a unified centrality algorithm

Background Computing centrality is a foundational concept in social networking that involves finding the most “central” or important nodes. In some biological networks defining importance is difficult, which then creates challenges in finding an appropriate centrality algorithm. Results We instead generalize the results of any k centrality algorithms through our iterative algorithm MATRIA, producing a single ranked and unified set of central nodes. Through tests on three biological networks, we demonstrate evident and balanced correlations with the results of these k algorithms. We also improve its speed through GPU parallelism. Conclusions Our results show iteration to be a powerful technique that can eliminate spatial bias among central nodes, increasing the level of agreement between algorithms with various importance definitions. GPU parallelism improves speed and makes iteration a tractable problem for larger networks

MDL, A Domain-Specific Language for Molecular Dynamics

Molecular Dynamics (MD) involves solving Newton’s equations of motion for a molecular system and propagating the system by time-dependent updates of atomic positions and velocities. As a severe limitation of molecular dynamics is the size of the timestep used for propagation, a key area of research is the development of efficient propagation algorithms which can maintain accuracy and stability with larger timesteps. We present MDL, an MD domain-specific language with the goals of allowing prototyping, testing and debugging of these algorithms. We illustrate the use of parallelism within MDL to implement the Finite Temperature String Method, and interfacing to visualization and graphical tools. 1

Empirical Evaluation of Design Patterns in Scientific Application

Design patterns have been widely adopted for building flexible and extensible applications. This can come at a cost of reduced performance which may not be acceptable for computationally intensive scientific applications. We claim that there are certain design patterns that when used properly can enhance both system performance and flexibility and thus greatly benefit scientific software. Software engineers can use these insights to design flexible systems that also deliver on performance, potentially saving days of runtime on long simulations as well as reducing memory consumption significantly. We investigate the effects of some of the design patterns on performance of scientific applications through a detailed measurement and profiling of CompuCell3D, which is a software framework for three-dimensional (3D) modeling of morphogenesis, a stage in embryonic development where cells cluster into tissues and organs. By examining CompuCell3D subsystems with and without design patterns, we evaluate their impact on application performance and maintainability. We find that as the application is continually refactored to support additional functionality, not using design patterns significantly degrades application performance. We also present a scientific design pattern Dynamic Class Node (DCN), discovered during our experimentation. The pattern showed that contiguous allocation of object attributes offers significant benefits in performance by reducing page faults and cache misses, while still maintaining flexibility. Categories and Subject Descriptors C.4 [Performance of Systems]: design studies, performance attributes