arXiv subjects

Eivind Almaas

Publications and source records attributed to Eivind Almaas.

5 recordsLinked to original sources

arXiv2018

wTO: an R package for computing weighted topological overlap and consensus networks with an integrated visualization tool

Network analyses, such as of gene co-expression networks, metabolic networks and ecological networks have become a central approach for the systems-level study of biological data. Several software packages exist for generating and analyzing such networks, either from correlation scores or the absolute value of a transformed score called weighted topological overlap (wTO). However, since gene regulatory processes can up- or down-regulate genes, it is of great interest to explicitly consider both positive and negative correlations when constructing a gene co-expression network. Here, we present an R package for calculating the wTO, that, in contrast to existing packages, explicitly addresses the sign of the wTO values, and is thus especially valuable for the analysis of gene regulatory networks. The package includes the calculation of p-values (raw and adjusted) for each pairwise gene score. Our package also allows the calculation of networks from time series (without replicates). Since networks from independent datasets (biological repeats or related studies) are not the same due to technical and biological noise in the data, we additionally, incorporated a novel method for calculating a consensus network (CN) from two or more networks into our R package. We compare our new wTO package to state of art packages and demonstrate the application of the wTO and CN functions using 3 independently derived datasets from healthy human pre-frontal cortex samples. To showcase an example for the time series application we utilized a metagenomics data set. In this work, we developed a software package that allows the computation of wTO networks, CNs and a visualization tool in the R statistical environment. It is publicly available on CRAN repositories under the GPL-2 Open Source License (https://cran.r-project.org/web/packages/wTO/).

q-bio.MN↗

arXiv2018

Comparing multiple networks using the Co-expression Differential Network Analysis (CoDiNA)

Biomedical sciences are increasingly recognising the relevance of gene co-expression-networks for analysing complex-systems, phenotypes or diseases. When the goal is investigating complex-phenotypes under varying conditions, it comes naturally to employ comparative network methods. While approaches for comparing two networks exist, this is not the case for multiple networks. Here we present a method for the systematic comparison of an unlimited number of networks: Co-expression Differential Network Analysis (CoDiNA) for detecting links and nodes that are common, specific or different to the networks. Applying CoDiNA to a neurogenesis study identified genes for neuron differentiation. Experimentally overexpressing one candidate resulted in significant disturbance in the underlying neurogenesis' gene regulatory network. We compared data from adults and children with active tuberculosis to test for signatures of HIV. We also identified common and distinct network features for particular cancer types with CoDiNA. These studies show that CoDiNA successfully detects genes associated with the diseases.

stat.CO↗

arXiv2013

Possible sorting mechanism for microparticles in an evanescent field

Mie scattering theory is used to calculate radiation forces on a dielectric microsphere illuminated by evanescent waves, produced by laser light transmitted obliquely through a flat horizontal dielectric surface. The incident field is identified with the evanescent field, and both $p$ and $s$ polarizations are considered. Our investigation consists of three parts. First, after highlighting the basic formalism, we report results for the radiation force published in an earlier paper [J. Opt. Soc. Am. B {\bf 12}, 2429 (1995)], correcting a few trivial calculational errors. Second - the main objective of our paper - is to show how the vertical (lifting) force on microspheres, typically via a proper adjustment of the laser frequency, can be used to separate spheres differing by a slight amount in their refractive index. This is caused by an oscillatory behavior in the force with respect to the nondimensional wave number $α$ in the surrounding medium. Fine-tuning the wave number $α$, relative to the given refractive indices in the system, may lead to particle expulsion. The sorting mechanism turns out to be feasible when $α$ is about 18-20 or larger, which actually is in the region of practical interest. Finally, we investigate how variations in the angle of incidence $θ_1$ for the laser beam influences the resulting radiation force.

physics.optics↗

arXiv2009

Genome-scale reconstruction of the metabolic network in Yersinia pestis, strain 91001

The gram-negative bacterium Yersinia pestis, the aetiological agent of bubonic plague, is one the deadliest pathogens known to man. Despite its historical reputation, plague is a modern disease which annually afflicts thousands of people. Public safety considerations greatly limit clinical experimentation on this organism and thus development of theoretical tools to analyze the capabilities of this pathogen is of utmost importance. Here, we report the first genome-scale metabolic model of Yersinia pestis biovar Mediaevalis based both on its recently annotated genome, and physiological and biochemical data from literature. Our model demonstrates excellent agreement with Y. pestis known metabolic needs and capabilities. Since Y. pestis is a meiotrophic organism, we have developed CryptFind, a systematic approach to identify all candidate cryptic genes responsible for known and theoretical meiotrophic phenomena. In addition to uncovering every known cryptic gene for Y. pestis, our analysis of the rhamnose fermentation pathway suggests that betB is the responsible cryptic gene. Despite all of our medical advances, we still do not have a vaccine for bubonic plague. Recent discoveries of antibiotic resistant strains of Yersinia pestis coupled with the threat of plague being used as a bioterrorism weapon compel us to develop new tools for studying the physiology of this deadly pathogen. Using our theoretical model, we can study the cells phenotypic behavior under different circumstances and identify metabolic weaknesses which may be harnessed for the development of therapeutics. Additionally, the automatic identification of cryptic genes expands the usage of genomic data for pharmaceutical purposes.

q-bio.MN↗

arXiv2008

Predicting synthetic rescues in metabolic networks

An important goal of medical research is to develop methods to recover the loss of cellular function due to mutations and other defects. Many approaches based on gene therapy aim to repair the defective gene or to insert genes with compensatory function. Here, we propose an alternative, network-based strategy that aims to restore biological function by forcing the cell to either bypass the functions affected by the defective gene, or to compensate for the lost function. Focusing on the metabolism of single-cell organisms, we computationally study mutants that lack an essential enzyme, and thus are unable to grow or have a significantly reduced growth rate. We show that several of these mutants can be turned into viable organisms through additional gene deletions that restore their growth rate. In a rather counterintuitive fashion, this is achieved via additional damage to the metabolic network. Using flux balance-based approaches, we identify a number of synthetically viable gene pairs, in which the removal of one enzyme-encoding gene results in a nonviable phenotype, while the deletion of a second enzyme-encoding gene rescues the organism. The systematic network-based identification of compensatory rescue effects may open new avenues for genetic interventions.

q-bio.MN↗