scholarly journals Robust design of complex socio-technical systems against seasonal effects: a network motif-based approach

2022 ◽  
Vol 8 ◽  
Author(s):  
Yinshuang Xiao ◽  
Zhenghui Sha
Keyword(s):  
Robust Design ◽  
Seasonal Changes ◽  
Capacity Planning ◽  
Network Motif ◽  
Transportation Systems ◽  
Seasonal Effects ◽  
Network Motifs ◽  
Planning Decision ◽  
Demand Fluctuations ◽  
Technical Systems

Abstract Seasonal effects can significantly impact the robustness of socio-technical systems (STS) to demand fluctuations. There is an increasing need to develop novel design approaches that can support capacity planning decisions for enhancing the robustness of STS against seasonal effects. This paper proposes a new network motif-based approach to supporting capacity planning in STS for an improved seasonal robustness. Network motifs are underlying nonrandom subgraphs within a complex network. In this approach, we introduce three motif-based metrics for system performance evaluation and capacity planning decision-making. The first one is the imbalance score of a motif (e.g., a local service network), the second one is the measurement of a motif’s seasonal robustness, and the third one is a capacity planning decision criterion. Based on these three metrics, we validate that the sensitivity of STS performance against seasonal effects is highly correlated with the imbalanced capacity between service nodes in an STS. Correspondingly, we formulate a design optimisation problem to improve the robustness of STS by rebalancing the resources at critical service nodes. To demonstrate the utility of the approach, a case study on Divvy bike-sharing system in Chicago is conducted. With a focus on the size-3 motifs (a subgraph consisting three docked stations), we find that there is a significant correlation between the difference of the number of docks among the stations in a motif and the return/rental performance of such a motif against seasonal changes. Guided by this finding, our design approach can successfully balance out the number of docks between those stations that have caused the most severe seasonal perturbations. The results also imply that the network motifs can be an effective local structural representation in support of STS robust design. Our approach can be generally applied in other STS where the system performances are significantly impacted by seasonal changes, for example, supply chain networks, transportation systems and power grids.

NMDB: NETWORK MOTIF DATABASE ENVISAGED AND EXPLICATED FROM HUMAN DISEASE SPECIFIC PATHWAYS

2014 ◽  
Vol 22 (01) ◽  
pp. 89-100 ◽  
Author(s):  
ABHAY PRATAP ◽  
SETU TALIYAN ◽  
TIRATHA RAJ SINGH
Keyword(s):  
Biological Networks ◽  
Human Disease ◽  
Biological Network ◽  
Metabolic Diseases ◽  
Network Motif ◽  
Network Motifs ◽  
Efficient Detection ◽  
Single Input ◽  
Motif Database ◽  
Disease Specific

The study of network motifs for large number of networks can aid us to resolve the functions of complex biological networks. In biology, network motifs that reappear within a network more often than expected in random networks include negative autoregulation, positive autoregulation, single-input modules, feedforward loops, dense overlapping regulons and feedback loops. These network motifs have their different dynamical functions. In this study, our main objective is to examine the enrichment of network motifs in different biological networks of human disease specific pathways. We characterize biological network motifs as biologically significant sub-graphs. We used computational and statistical criteria for efficient detection of biological network motifs, and introduced several estimation measures. Pathways of cardiovascular, cancer, infectious, repair, endocrine and metabolic diseases, were used for identifying and interlinking the relation between nodes. 3–8 sub-graph size network motifs were generated. Network Motif Database was then developed using PHP and MySQL. Results showed that there is an abundance of autoregulation, feedforward loops, single-input modules, dense overlapping regulons and other putative regulatory motifs in all the diseases included in this study. It is believed that the database will assist molecular and system biologists, biotechnologists, and other scientific community to encounter biologically meaningful information. Network Motif Database is freely available for academic and research purpose at: http://www.bioinfoindia.org/nmdb .

Motif discovery in biological network using expansion tree

2018 ◽  
Vol 16 (06) ◽  
pp. 1850024 ◽  
Author(s):  
Sabyasachi Patra ◽  
Anjali Mohapatra
Keyword(s):  
Complex Networks ◽  
Protein Interaction ◽  
Motif Discovery ◽  
Statistical Significance ◽  
Network Motif ◽  
Graph Isomorphism ◽  
Building Blocks ◽  
Network Motifs ◽  
Topological Features ◽  
Scalable Network

Networks are powerful representation of topological features in biological systems like protein interaction and gene regulation. In order to understand the design principles of such complex networks, the concept of network motifs emerged. Network motifs are recurrent patterns with statistical significance that can be seen as basic building blocks of complex networks. Identification of network motifs leads to many important applications, such as understanding the modularity and the large-scale structure of biological networks, classification of networks into super-families, protein function annotation, etc. However, identification of network motifs is challenging as it involves graph isomorphism which is computationally hard. Though this problem has been studied extensively in the literature using different computational approaches, we are far from satisfactory results. Motivated by the challenges involved in this field, an efficient and scalable network Motif Discovery algorithm based on Expansion Tree (MODET) is proposed. Pattern growth approach is used in this proposed motif-centric algorithm. Each node of the expansion tree represents a non-isomorphic pattern. The embeddings corresponding to a child node of the expansion tree are obtained from the embeddings of the parent node through vertex addition and edge addition. Further, the proposed algorithm does not involve any graph isomorphism check and the time complexities of these processes are [Formula: see text] and [Formula: see text], respectively. The proposed algorithm has been tested on Protein–Protein Interaction (PPI) network obtained from the MINT database. The computational efficiency of the proposed algorithm outperforms most of the existing network motif discovery algorithms.

scholarly journals ANALYSIS OF PREFERENTIAL NETWORK MOTIF GENERATION IN AN ARTIFICIAL REGULATORY NETWORK MODEL CREATED BY DUPLICATION AND DIVERGENCE

2007 ◽  
Vol 10 (02) ◽  
pp. 155-172 ◽  
Author(s):  
ANDRÉ LEIER ◽  
P. DWIGHT KUO ◽  
WOLFGANG BANZHAF
Keyword(s):  
Gene Regulatory Networks ◽  
Network Topology ◽  
Regulatory Networks ◽  
Genome Duplication ◽  
Network Motif ◽  
Network Motifs ◽  
Gene Regulatory ◽  
Artificial Gene ◽  
Artificial Model ◽  
Regulatory Sites

Previous studies on network topology of artificial gene regulatory networks created by whole genome duplication and divergence processes show subgraph distributions similar to gene regulatory networks found in nature. In particular, certain network motifs are prominent in both types of networks. In this contribution, we analyze how duplication and divergence processes influence network topology and preferential generation of network motifs. We show that in the artificial model such preference originates from a stronger preservation of protein than regulatory sites by duplication and divergence. If these results can be transferred to regulatory networks in nature, we can infer that after duplication the paralogous transcription factor binding site is less likely to be preserved than the corresponding paralogous protein.

scholarly journals Bistability in network motifs of Duffing oscillators

2020 ◽  
pp. 31-40
Author(s):  
R. Jaimes-Reátegui ◽  
J. M. Castillo-Cruz ◽  
J. H. Garcıa-Lopez ◽  
G. Huerta-Cuellar ◽  
L. A. Gallegos-Infante ◽  
...  
Keyword(s):  
Fixed Point ◽  
Bifurcation Diagram ◽  
Limit Cycle ◽  
Coupling Strength ◽  
Complex Dynamics ◽  
Initial Conditions ◽  
Network Motif ◽  
Equation Of Motion ◽  
Network Motifs

We study the emergence of synchronization in the network motif of three bistable Duffing oscillators coupled in all possible configurations. The equation of motion is derived for every configuration. For each motif, we vary initial conditions of every oscillator and calculate the bifurcation diagram as a function of the coupling strength. We find transitions of the whole system to a monostable regime with either a fixed point or a limit cycle depending on the motif’s configuration, as the coupling strength is increased. The most complex dynamics is observed the nidirectional chain, where a transition to quasiperiodicity occurs.

scholarly journals Demographic response of the Gambian Gerbil to seasonal changes in Savannah fallow fields

2019 ◽  
Vol 46 (1) ◽  
pp. 1-9
Author(s):  
Safianu Rabiu ◽  
Robert K. Rose
Keyword(s):  
Seasonal Changes ◽  
Large Population ◽  
Survival Rates ◽  
Seasonal Effects ◽  
Northern Nigeria ◽  
Crop Pest ◽  
Pellet Counts ◽  
Demographic Response ◽  
Ecological Relations ◽  
The Impact

AbstractThe Savannah gerbil, Gerbilliscus gambianus (Muridae: Gerbillinae) is important to the ecological relations of the dry grassland ecosystem of West Africa, as well as, being a zoonotic agent of human diseases and potential crop pest. We examined the impact of seasonal changes on the population dynamics of G. gambianus in northern Nigeria, by completing population estimates using capture–mark–recapture (CMR) and indirect population density indices (PDI) methods. The latter included fecal pellet counts and limited spotlightening. During 1990–1992 we collected both CMR and PDI data, and established their relationship by regression, thus calibrating the PDI values to CMR estimator. We also completed a separate, PDI only, study during 2015–2017, and estimated monthly densities indirectly by toning the PDI values to population sizes in the CMR estimator. The lowest declines (<20 gerbils ha−1) were in mid rains (July–August), and highest increases (>90 gerbils ha−1) were after the rains (October–January). Seasonal effects on densities were significant during 1990–1992 but not during 2015–2017. There were improved survival rates for both adults (0.95) and young (0.83), adult capture probability (0.56), and mean monthly recruitment of young (23) after the rains. There was no significant change in the overall population dynamic pattern of G. gambianus over a 25-year period. Because G. gambianus did not maintain colonies inside farmlands cultivated by rain or irrigation, and its tendency for large population drops in mid-rains, we are in doubt of its potential as crop pest in northern Nigeria.

scholarly journals Application of dynamic expansion tree for finding large network motifs in biological networks

PeerJ ◽  
2019 ◽  
Vol 7 ◽  
pp. e6917 ◽  
Author(s):  
Sabyasachi Patra ◽  
Anjali Mohapatra
Keyword(s):  
Biological Networks ◽  
Protein Function ◽  
Large Scale ◽  
Network Motif ◽  
Graph Isomorphism ◽  
Interaction Network ◽  
Motif Finding ◽  
Network Motifs ◽  
Large Network ◽  
Scalable Network

Network motifs play an important role in the structural analysis of biological networks. Identification of such network motifs leads to many important applications such as understanding the modularity and the large-scale structure of biological networks, classification of networks into super-families, and protein function annotation. However, identification of large network motifs is a challenging task as it involves the graph isomorphism problem. Although this problem has been studied extensively in the literature using different computational approaches, still there is a lot of scope for improvement. Motivated by the challenges involved in this field, an efficient and scalable network motif finding algorithm using a dynamic expansion tree is proposed. The novelty of the proposed algorithm is that it avoids computationally expensive graph isomorphism tests and overcomes the space limitation of the static expansion tree (SET) which makes it enable to find large motifs. In this algorithm, the embeddings corresponding to a child node of the expansion tree are obtained from the embeddings of a parent node, either by adding a vertex or by adding an edge. This process does not involve any graph isomorphism check. The time complexity of vertex addition and edge addition are O(n) and O(1), respectively. The growth of a dynamic expansion tree (DET) depends on the availability of patterns in the target network. Pruning of branches in the DET significantly reduces the space requirement of the SET. The proposed algorithm has been tested on a protein–protein interaction network obtained from the MINT database. The proposed algorithm is able to identify large network motifs faster than most of the existing motif finding algorithms.

scholarly journals A spatial model of COVID-19 transmission in England and Wales: early spread, peak timing and the impact of seasonality

2021 ◽  
Vol 376 (1829) ◽  
pp. 20200272
Author(s):  
Leon Danon ◽  
Ellen Brooks-Pollock ◽  
Mick Bailey ◽  
Matt Keeling
Keyword(s):  
Seasonal Changes ◽  
Capacity Planning ◽  
Census Data ◽  
Transmission Rate ◽  
Parameter Estimates ◽  
Peak Time ◽  
England And Wales ◽  
Minimal Impact ◽  
Starting Location ◽  
The Impact

An outbreak of a novel coronavirus was first reported in China on 31 December 2019. As of 9 February 2020, cases have been reported in 25 countries, including probable human-to-human transmission in England. We adapted an existing national-scale metapopulation model to capture the spread of COVID-19 in England and Wales. We used 2011 census data to inform population sizes and movements, together with parameter estimates from the outbreak in China. We predict that the epidemic will peak 126 to 147 days (approx. 4 months) after the start of person-to-person transmission in the absence of controls. Assuming biological parameters remain unchanged and transmission persists from February, we expect the peak to occur in June. Starting location and model stochasticity have a minimal impact on peak timing. However, realistic parameter uncertainty leads to peak time estimates ranging from 78 to 241 days following sustained transmission. Seasonal changes in transmission rate can substantially impact the timing and size of the epidemic. We provide initial estimates of the epidemic potential of COVID-19. These results can be refined with more precise parameters. Seasonal changes in transmission could shift the timing of the peak into winter, with important implications for healthcare capacity planning. This article is part of the theme issue ‘Modelling that shaped the early COVID-19 pandemic response in the UK.

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