An Analysis of Modularity as a Design Rule Using Network Theory

2019 ◽  
Vol 141 (3) ◽  
Author(s):  
Hannah S. Walsh ◽  
Andy Dong ◽  
Irem Y. Tumer
Keyword(s):  
Network Theory ◽  
Design Principle ◽  
Network Models ◽  
Design Rule ◽  
Function Parameter ◽  
Engineering Systems ◽  
System Architectures ◽  
System Representation ◽  
And Function ◽  
Random Failures

Increasing the modularity of system architectures is generally accepted as a good design principle in engineering. In this paper, we explore whether modularity comes at the expense of robustness. To that end, we model three engineering systems as networks and measure the relation between modularity and robustness to random failures. We produced four types of network models of systems—component-component, component-function, component-parameter, and function-parameter—to further test the relation of robustness to the type of system representation, architectural or behavioral. The results show that higher modularity is correlated with lower robustness (p < 0.001) and that the estimated modularity of the system can depend on the type of system representation. The implication is that there is a tradeoff between modularity and robustness, meaning that increasing modularity might not be appropriate for systems for which robustness is critical and modularity estimates differ significantly between the types of system representation.

An Analysis of Modularity As a Design Rule Using Network Theory

2018 ◽  
Author(s):  
Hannah S. Walsh ◽  
Andy Dong ◽  
Irem Y. Tumer
Keyword(s):  
Network Theory ◽  
Design Principle ◽  
Network Models ◽  
Design Rule ◽  
Function Parameter ◽  
Engineering Systems ◽  
System Architectures ◽  
System Representation ◽  
And Function ◽  
Random Failures

Increasing the modularity of system architectures is generally accepted as a good design principle in engineering. In this paper, we explore whether modularity comes at the expense of robustness. To that end, we model three engineering systems as networks and measure the relation between modularity and robustness to random failures. We produced four types of network models of systems — component, component-function, component-parameter, and function-parameter — to further test the relation of robustness to the type of system representation, architectural or behavioral. The results show that higher modularity is correlated with lower robustness (p < 0.001) and that the estimated modularity of the system can depend on the type of system representation. The implication is that there is a trade-off between modularity and robustness, meaning that increasing modularity might not be appropriate for systems for which robustness is critical and for those whose modularity estimate differs largely between each type of system representation.

A Weighted Network Modeling Approach for Analyzing Product Competition

2020 ◽  
Author(s):  
Yaxin Cui ◽  
Faez Ahmed ◽  
Zhenghui Sha ◽  
Lijun Wang ◽  
Yan Fu ◽  
...  
Keyword(s):  
Network Theory ◽  
Network Modeling ◽  
Network Models ◽  
Social Aspects ◽  
Exponential Random Graph Models ◽  
Graph Models ◽  
Random Graph Models ◽  
Exponential Random Graph ◽  
The Social ◽  
Product Competition

Abstract Statistical network models allow us to study the co-evolution between the products and the social aspects of a market system, by modeling these components and their interactions as graphs. In this paper, we study competition between different car models using network theory, with a focus on how product attributes (like fuel economy and price) affect which cars are considered together and which cars are finally bought by customers. Unlike past work, where most systems have been studied with the assumption that relationships between competitors are binary (i.e., whether a relationship exists or not), we allow relationships to take strengths (i.e., how strong a relationship is). Specifically, we use valued Exponential Random Graph Models and show that our approach provides a significant improvement over the baselines in predicting product co-considerations as well as in the validation of market share. This is also the first attempt to study aggregated purchase preference and car competition using valued directed networks.

scholarly journals Numerical operations in living cells by programmable RNA devices

2019 ◽  
Vol 5 (8) ◽  
pp. eaax0835 ◽  
Author(s):  
Kei Endo ◽  
Karin Hayashi ◽  
Hirohide Saito
Keyword(s):  
Messenger Rna ◽  
Design Principle ◽  
Logic Gates ◽  
Living Cells ◽  
Multiple Biomarkers ◽  
Intracellular Changes ◽  
And Function ◽  
Single Living Cells ◽  
Single Living ◽  
Numerical Operations

Integrated bioengineering systems can make executable decisions according to the cell state. To sense the state, multiple biomarkers are detected and processed via logic gates with synthetic biological devices. However, numerical operations have not been achieved. Here, we show a design principle for messenger RNA (mRNA) devices that recapitulates intracellular information by multivariate calculations in single living cells. On the basis of this principle and the collected profiles of multiple microRNA activities, we demonstrate that rationally programmed mRNA sets classify living human cells and track their change during differentiation. Our mRNA devices automatically perform multivariate calculation and function as a decision-maker in response to dynamic intracellular changes in living cells.

Sensitivity Analysis of the Networks with Fractional Order Reactance

2013 ◽  
Vol 860-863 ◽  
pp. 2309-2314
Author(s):  
Gui Shu Liang ◽  
Xing Hua Zheng ◽  
Long Ma ◽  
Hua Ying Dong
Keyword(s):  
Sensitivity Analysis ◽  
Fractional Calculus ◽  
Fractional Order ◽  
Network Theory ◽  
Network Models ◽  
Element Model ◽  
Network Sensitivity

Fractional calculus theory has gained more and more applications in numerous fields. In many cases, using fractional reactance element model can describe the properties of objects more accurately and simply. This paper studies the sensitivity of networks with fractional order reactance, puts forward the adjoint network sensitivity formulas and the incremental network models of fractional order capacitor and inductor, which will further develop the adjoint network and Incremental network theory. The simulation verification is also given.

Wavelet transformed Gaussian network model

2014 ◽  
Vol 13 (06) ◽  
pp. 1450053 ◽  
Author(s):  
Meng Zhan ◽  
Suhong Li ◽  
Fan Li
Keyword(s):  
Protein Dynamics ◽  
Network Model ◽  
Protein Structures ◽  
Network Models ◽  
Experimental Results ◽  
Gaussian Network Model ◽  
The Mean ◽  
Dynamics And Function ◽  
And Function ◽  
Gaussian Network

Accurate prediction of the Debye–Waller temperature factor of proteins is of significant importance in the study of protein dynamics and function. This work explores the utility of wavelets for improving the performance of Gaussian network model (GNM). We propose two wavelet transformed Gaussian network models (wtGNM), namely a scale-one wtGNM and a scale-two wtGNM. Based on a set of 113 protein structures, it shows that the mean correlation with experimental results for the scale-one wtGNM is 0.714 and that for the scale-two wtGNM is 0.738. In contrast, the mean correlation for the original GNM is 0.594. Therefore, the wtGNM is a potential algorithm for improving the GNM prediction of protein B-factors.

scholarly journals Barnaba: Software for Analysis of Nucleic Acids Structures and Trajectories

2018 ◽  
Author(s):  
Sandro Bottaro ◽  
Giovanni Bussi ◽  
Giovanni Pinamonti ◽  
Sabine Reißer ◽  
Wouter Boomsma ◽  
...  
Keyword(s):  
Nucleic Acids ◽  
Protein Complexes ◽  
Three Dimensional ◽  
Molecular Simulations ◽  
Network Models ◽  
Molecular Conformations ◽  
Rna Molecules ◽  
Command Line Tool ◽  
Perform Cluster Analysis ◽  
And Function

AbstractRNA molecules are highly dynamic systems characterized by a complex interplay between sequence, structure, dynamics, and function. Molecular simulations can potentially provide powerful insights into the nature of these relationships. The analysis of structures and molecular trajectories of nucleic acids can be non-trivial because it requires processing very high-dimensional data that are not easy to visualize and interpret.Here we introduce Barnaba, a Python library aimed at facilitating the analysis of nucleic acids structures and molecular simulations. The software consists of a variety of analysis tools that allow the user to i) calculate distances between three-dimensional structures using different metrics, ii) back-calculate experimental data from three-dimensional structures, iii) perform cluster analysis and dimensionality reductions, iv) search three-dimensional motifs in PDB structures and trajectories and v) construct elastic network models (ENM) for nucleic acids and nucleic acids-protein complexes.In addition, Barnaba makes it possible to calculate torsion angles, pucker conformations and to detect base-pairing/base-stacking interactions. Barnaba produces graphics that conveniently visualize both extended secondary structure and dynamics for a set of molecular conformations. The software is available as a command-line tool as well as a library, and supports a variety of 1le formats such as PDB, dcd and xtc 1les. Source code, documentation and examples are freely available at https://github.com/srnas/barnaba under GNU GPLv3 license.

scholarly journals Building-network: Concept, generation method and centrality analysis

2019 ◽  
Vol 2 ◽  
pp. 1-8
Author(s):  
Xiao Wang ◽  
Dirk Burghardt
Keyword(s):  
Network Theory ◽  
Network Models ◽  
Pattern Detection ◽  
Centrality Measures ◽  
Concept Generation ◽  
Eigenvector Centrality ◽  
Rural Regions ◽  
Network Concept ◽  
Centrality Analysis ◽  
Refinement Strategy

<p><strong>Abstract.</strong> Buildings are among the most important features of cities. In the suburban or rural regions, buildings are normally constructed along the roads, which forms the smooth and consistent patterns so that the building arrangements also can be described with network models. In previous studies, network theory has achieved good performance in cartography and GIS. In this paper, a study of a building-network is proposed, including the concepts, generation methods and centrality analysis. Firstly, with the constraint Delaunay triangulation and the refinement strategy by facing ratio, the building-network is generated by considering the buildings and the proximal segments as the nodes and segments of the network, respectively. Then, centrality analysis is applied on the building-network, aiming to reveal the crucial relationships among buildings, which is useful for understanding the structural properties of the complex network. Four different centrality measures, i.e. degree, closeness, betweenness, and eigenvector centrality, are calculated based on the building-networks. The buildings show different distribution effects and patterns under the four centrality measures. From the results, the degree centrality reveals the local centre of the region; closeness and eigenvector centrality have the ability to cluster buildings into different groups; while betweenness centrality can detect the linear patterns. Therefore, using network theory to analyse buildings can reveal some inner relationships of buildings and has great potential in the application of building pattern detection, classification, clustering and further generalization.</p>

scholarly journals Edges in brain networks: contributions to models of structure and function

2021 ◽  
pp. 1-63
Author(s):  
Joshua Faskowitz ◽  
Richard F Betzel ◽  
Olaf Sporns
Keyword(s):  
Network Models ◽  
Brain Network ◽  
Higher Order ◽  
Dynamic Information ◽  
Brain Organization ◽  
Network Nodes ◽  
Different Types ◽  
Definition Of ◽  
And Function ◽  
Specific Definition

Abstract Network models describe the brain as sets of nodes and edges that represent its distributed organization. So far, most discoveries in network neuroscience have prioritized insights that highlight distinct groupings and specialized functional contributions of network nodes. Importantly, these functional contributions are determined and expressed by the web of their interrelationships, formed by network edges. Here, we underscore the important contributions made by brain network edges for understanding distributed brain organization. Different types of edges represent different types of relationships, including connectivity and similarity among nodes. Adopting a specific definition of edges can fundamentally alter how we analyze and interpret a brain network. Furthermore, edges can associate into collectives and higher-order arrangements, describe time series, and form edge communities that provide insights into brain network topology complementary to the traditional node-centric perspective. Focusing on the edges, and the higher-order or dynamic information they can provide, discloses previously underappreciated aspects of structural and functional network organization.

Harvesting at the Ecosystem Level

2020 ◽  
pp. 229-250
Author(s):  
Michael J. Fogarty ◽  
Jeremy S. Collie
Keyword(s):  
Chlorophyll Concentration ◽  
Network Models ◽  
Potential Effect ◽  
Management Options ◽  
Management Actions ◽  
Threatened And Endangered Species ◽  
Mass Balance Models ◽  
Ecosystem Level ◽  
Habitat Damage ◽  
And Function

Energy entering at the base of the food web places inherent constraints on total harvest from an ecosystem. Empirical relationships have been established between fishery yield and factors such as chlorophyll concentration, primary production, and other lower trophic level variables to guide management actions. Extension of network models to include harvesting has a long lineage and these models are now being employed worldwide to help guide management decisions. These static mass-balance models have been augmented with a fully dynamic modeling component to explore management options. Biomass spectrum models for exploited ecosystems have also been developed. In addition to direct effects on target species, fisheries can affect the structure and function of ecosystems through habitat damage and incidental catch of non-target organisms, including threatened and endangered species. Increasingly, the effects of climate change are being addressed in ecosystem models through their potential effect on production at all levels of the ecosystem.

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