scholarly journals Creating, generating and comparing random network models with Network Randomizer

F1000Research ◽  
2016 ◽  
Vol 5 ◽  
pp. 2524 ◽  
Author(s):  
Gabriele Tosadori ◽  
Ivan Bestvina ◽  
Fausto Spoto ◽  
Carlo Laudanna ◽  
Giovanni Scardoni
Keyword(s):  
Experimental Data ◽  
High Throughput ◽  
Biological Networks ◽  
Random Network ◽  
Network Models ◽  
Information Network ◽  
Statistical Tool ◽  
New Techniques ◽  
High Throughput Data ◽  
Randomized Networks

Biological networks are becoming a fundamental tool for the investigation of high-throughput data in several fields of biology and biotechnology. With the increasing amount of information, network-based models are gaining more and more interest and new techniques are required in order to mine the information and to validate the results. We have developed an app for the Cytoscape platform which allows the creation of randomized networks and the randomization of existing, real networks. Since there is a lack of tools for generating and randomizing networks, our app helps researchers to exploit different, well known random network models which could be used as a benchmark for validating real datasets. We also propose a novel methodology for creating random weighted networks starting from experimental data. Finally the app provides a statistical tool which compares real versus random attributes, in order to validate all the numerical findings. In summary, our app aims at creating a standardised methodology for the validation of the results in the context of the Cytoscape platform.

scholarly journals Creating, generating and comparing random network models with NetworkRandomizer

F1000Research ◽  
2017 ◽  
Vol 5 ◽  
pp. 2524 ◽  
Author(s):  
Gabriele Tosadori ◽  
Ivan Bestvina ◽  
Fausto Spoto ◽  
Carlo Laudanna ◽  
Giovanni Scardoni
Keyword(s):  
High Throughput ◽  
Biological Networks ◽  
Quantitative Data ◽  
Random Network ◽  
Network Models ◽  
Information Network ◽  
Statistical Tool ◽  
New Techniques ◽  
High Throughput Data ◽  
Multiplication Algorithm

Biological networks are becoming a fundamental tool for the investigation of high-throughput data in several fields of biology and biotechnology. With the increasing amount of information, network-based models are gaining more and more interest and new techniques are required in order to mine the information and to validate the results. To fill the validation gap we present an app, for the Cytoscape platform, which aims at creating randomised networks and randomising existing, real networks. Since there is a lack of tools that allow performing such operations, our app aims at enabling researchers to exploit different, well known random network models that could be used as a benchmark for validating real, biological datasets. We also propose a novel methodology for creating random weighted networks, i.e. the multiplication algorithm, starting from real, quantitative data. Finally, the app provides a statistical tool that compares real versus randomly computed attributes, in order to validate the numerical findings. In summary, our app aims at creating a standardised methodology for the validation of the results in the context of the Cytoscape platform.

scholarly journals Creating, generating and comparing random network models with NetworkRandomizer

F1000Research ◽  
2017 ◽  
Vol 5 ◽  
pp. 2524 ◽  
Author(s):  
Gabriele Tosadori ◽  
Ivan Bestvina ◽  
Fausto Spoto ◽  
Carlo Laudanna ◽  
Giovanni Scardoni
Keyword(s):  
High Throughput ◽  
Biological Networks ◽  
Quantitative Data ◽  
Random Network ◽  
Network Models ◽  
Information Network ◽  
Statistical Tool ◽  
New Techniques ◽  
High Throughput Data ◽  
Multiplication Algorithm

Biological networks are becoming a fundamental tool for the investigation of high-throughput data in several fields of biology and biotechnology. With the increasing amount of information, network-based models are gaining more and more interest and new techniques are required in order to mine the information and to validate the results. To fill the validation gap we present an app, for the Cytoscape platform, which aims at creating randomised networks and randomising existing, real networks. Since there is a lack of tools that allow performing such operations, our app aims at enabling researchers to exploit different, well known random network models that could be used as a benchmark for validating real, biological datasets. We also propose a novel methodology for creating random weighted networks, i.e. the multiplication algorithm, starting from real, quantitative data. Finally, the app provides a statistical tool that compares real versus randomly computed attributes, in order to validate the numerical findings. In summary, our app aims at creating a standardised methodology for the validation of the results in the context of the Cytoscape platform.

scholarly journals Assessment of network perturbation amplitudes by applying high-throughput data to causal biological networks

2012 ◽  
Vol 6 (1) ◽  
pp. 54 ◽  
Author(s):  
Florian Martin ◽  
Ty M Thomson ◽  
Alain Sewer ◽  
David A Drubin ◽  
Carole Mathis ◽  
...  
Keyword(s):  
High Throughput ◽  
Biological Networks ◽  
High Throughput Data

scholarly journals Convergence of a Belief Propagation Algorithm for Biological Networks

2019 ◽  
Vol 2019 ◽  
pp. 1-16
Author(s):  
Sang-Mok Choo ◽  
Young-Hee Kim
Keyword(s):  
Experimental Data ◽  
Biological Networks ◽  
Belief Propagation ◽  
Iterative Scheme ◽  
Biological Systems ◽  
Network Models ◽  
Stochastic Approach ◽  
Factor Graph ◽  
Process Step ◽  
Whole Process

Constructing network models of biological systems is important for effective understanding and control of the biological systems. For the construction of biological networks, a stochastic approach for link weights has been recently developed by using experimental data and belief propagation on a factor graph. The link weights were variable nodes of the factor graph and determined from their marginal probability mass functions which were approximated by using an iterative scheme. However, there is no convergence analysis of the iterative scheme. In this paper, at first, we present a detailed explanation of the complicated multistep process step by step with a network of small size and artificial experimental data, and then we show a sufficient condition for the convergence of the iterative scheme. Numerical examples are given to illustrate the whole process and to verify our result.

scholarly journals Connection-type-specific biases make uniform random network models consistent with cortical recordings

2014 ◽  
Vol 112 (8) ◽  
pp. 1801-1814 ◽  
Author(s):  
Christian Tomm ◽  
Michael Avermann ◽  
Carl Petersen ◽  
Wulfram Gerstner ◽  
Tim P. Vogels
Keyword(s):  
Experimental Data ◽  
Random Network ◽  
Network Models ◽  
Synaptic Strength ◽  
Good Representation ◽  
Network Architectures ◽  
Dimensional Parameter ◽  
Data Set ◽  
Substantial Impact ◽  
The Impact

Uniform random sparse network architectures are ubiquitous in computational neuroscience, but the implicit hypothesis that they are a good representation of real neuronal networks has been met with skepticism. Here we used two experimental data sets, a study of triplet connectivity statistics and a data set measuring neuronal responses to channelrhodopsin stimuli, to evaluate the fidelity of thousands of model networks. Network architectures comprised three neuron types (excitatory, fast spiking, and nonfast spiking inhibitory) and were created from a set of rules that govern the statistics of the resulting connection types. In a high-dimensional parameter scan, we varied the degree distributions (i.e., how many cells each neuron connects with) and the synaptic weight correlations of synapses from or onto the same neuron. These variations converted initially uniform random and homogeneously connected networks, in which every neuron sent and received equal numbers of synapses with equal synaptic strength distributions, to highly heterogeneous networks in which the number of synapses per neuron, as well as average synaptic strength of synapses from or to a neuron were variable. By evaluating the impact of each variable on the network structure and dynamics, and their similarity to the experimental data, we could falsify the uniform random sparse connectivity hypothesis for 7 of 36 connectivity parameters, but we also confirmed the hypothesis in 8 cases. Twenty-one parameters had no substantial impact on the results of the test protocols we used.

Constructing Simple Biological Networks for Understanding Complex High-Throughput Data in Plants

2015 ◽  
pp. 503-526 ◽  
Author(s):  
Tomás C. Moyano ◽  
Elena A. Vidal ◽  
Orlando Contreras-López ◽  
Rodrigo A. Gutiérrez
Keyword(s):  
High Throughput ◽  
Biological Networks ◽  
High Throughput Data

scholarly journals DeDaL: Cytoscape 3.0 app for producing and morphing data-driven and structure-driven network layouts

2015 ◽  
Author(s):  
Urszula Czerwinska ◽  
Laurence Calzone ◽  
Emmanuel Barillot ◽  
Andrei Zinovyev
Keyword(s):  
High Throughput ◽  
Biological Networks ◽  
Biological Network ◽  
Data Driven ◽  
Multidimensional Data ◽  
High Throughput Data ◽  
Analysis Task ◽  
Structural Network ◽  
Linear Algorithms ◽  
Processing Steps

Visualization and analysis of molecular profiling data together with biological networks are able to provide new mechanistical insights into biological functions. Currently, high-throughput data are usually visualized on top of predefined network layouts which are not always adapted to a given data analysis task. We developed a Cytoscape app which allows to construct biological network layouts based on the data from molecular profiles imported as values of nodes attributes. DeDaL is a Cytoscape 3.0 app which uses linear and non-linear algorithms of dimension reduction to produce data-driven network layouts based on multidimensional data (typically gene expression). DeDaL implements several data pre-processing and layout post-processing steps such as continuous morphing between two arbitrary network layouts and aligning one network layout with respect to another one by rotating and mirroring. Combining these possibilities facilitates creating insightful network layouts representing both structural network features and the correlation patterns in multivariate data. DeDaL is the first method allowing to construct biological network layouts from high-throughput data. DeDaL is freely available for downloading together with step-by-step tutorial at http://bioinfo-out.curie.fr/projects/dedal/.

scholarly journals Automated high-quality reconstruction of metabolic networks from high-throughput data

2018 ◽  
Author(s):  
Daniel Hartleb ◽  
C. Jonathan Fritzemeier ◽  
Martin J. Lercher
Keyword(s):  
Experimental Data ◽  
High Throughput ◽  
Information Value ◽  
High Quality ◽  
Reconstruction Process ◽  
High Throughput Data ◽  
A Genome ◽  
Metabolic Models ◽  
Novel Strategy ◽  
Genome Scale

AbstractWhile new genomes are sequenced at ever increasing rates, their phenotypic analysis remains a major bottleneck of biomedical research. The generation of genome-scale metabolic models capable of accurate phenotypic predictions is a labor-intensive endeavor; accordingly, such models are available for only a small percentage of sequenced species. The standard metabolic reconstruction process starts from a (semi-)automatically generated draft model, which is then refined through extensive manual curation. Here, we present a novel strategy suitable for full automation, which exploits high-throughput gene knockout or nutritional growth data. We test this strategy by reconstructing accurate genome-scale metabolic models for three strains ofStreptococcus, a major human pathogen. The resulting models contain a lower proportion of reactions unsupported by genomic evidence than the most widely usedE. colimodel, but reach the same accuracy in terms of knockout prediction. We confirm the models’ predictive power by analyzing experimental data for auxotrophy, additional nutritional environments, and double gene knockouts, and we generate a list of potential drug targets. Our results demonstrate the feasibility of reconstructing high-quality genome-scale metabolic models from high-throughput data, a strategy that promises to massively accelerate the exploration of metabolic phenotypes.Significance statementReading bacterial genomes has become a cheap, standard laboratory procedure. A genome by itself, however, is of little information value – we need a way to translate its abstract letter sequence into a model that describes the capabilities of its carrier. Until now, this endeavor required months of manual work by experts. Here, we show how this process can be automated by utilizing high-throughput experimental data. We use our novel strategy to generate highly accurate metabolic models for three strains ofStreptococcus, a major threat to human health.

scholarly journals Separating the Drivers from the Driven: Integrative Network and Pathway Approaches Aid Identification of Disease Biomarkers from High-Throughput Data

2010 ◽  
Vol 28 (4) ◽  
pp. 253-266 ◽  
Author(s):  
Jason E. McDermott ◽  
Michelle Costa ◽  
Derek Janszen ◽  
Mudita Singhal ◽  
Susan C. Tilton
Keyword(s):  
High Throughput ◽  
Biological Networks ◽  
Complex Interplay ◽  
Disease Biomarkers ◽  
High Throughput Data ◽  
Disease States ◽  
The Individual ◽  
Shed Light ◽  
Integrative Network

The recent advances in high-throughput data acquisition have driven a revolution in the study of human disease and determination of molecular biomarkers of disease states. It has become increasingly clear that many of the most important human diseases arise as the result of a complex interplay between several factors including environmental factors, such as exposure to toxins or pathogens, diet, lifestyle, and the genetics of the individual patient. Recent research has begun to describe these factors in the context of networks which describe relationships between biological components, such as genes, proteins and metabolites, and have made progress towards the understanding of disease as a dysfunction of the entire system, rather than, for example, mutations in single genes. We provide a summary of some of the recent work in this area, focusing on how the integration of different kinds of complementary data, and analysis of biological networks and pathways can lead to discovery of robust, specific and useful biomarkers of disease and how these methods can help shed light on the mechanisms and etiology of the diseases being studied.

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