Network Theoretical Approach Research Articles

Network theory and migration: Avoiding misapplications and misinterpretations

Network analysis is becoming a popular tool for the study of animal movement. Proliferation of software enables researchers to use network measures without reflecting on the underlying mathematics and biology. One common use characterizing movement networks, such as migration, with centrality measures. Predominantly developed to evaluate social systems, these measures now appear in applications across disciplines that vary greatly in system properties. Each network measure has a very specific mathematical definition, with implicit assumptions about system properties to which they are applied that often make them inappropriate for migration. We report mismatches between mathematical assumptions and applications of network measures to migration, primarily with bird examples. For example, of 11 trajectory-transmission network-flow processes observed in the real world, only two are applicable to bird migration, neither of which allows valid application of classical centrality measures. We also identify misinterpretations of network measures, such as nodes with the highest degree centrality as locations where individuals intermingle. Other misinterpretations include that a high betweenness score translates to a node that is frequently used or is an important site along a main migratory route. Finally, networks are used in different ways, ranging from descriptive visualization to quantitative prediction and we identify common cases, such as migratory connectivity, where this leads to miscommunication. Network-theoretic approaches to answer ecological research and conservation questions associated with migration are numerous, but new measures need to be developed for animal migration. We provide a guide for researchers of animal movement for measure selection, development, and documentation.

Single-cell transcriptomics reveals stage- and side-specificity of gene modules in colorectal cancer.

An understanding of mechanisms underlying colorectal cancer (CRC) development and progression is yet to be fully elucidated. This study aims to employ network theoretic approaches to analyse single cell transcriptomic data from CRC to better characterize its progression and sided-ness. We utilized a recently published single-cell RNA sequencing data (GEO-GSE178341) and parsed the cell X gene data by stage and side (right and left colon). Using Weighted Gene Co-expression Network Analysis (WGCNA), we identified gene modules with varying preservation levels (weak or strong) of network topology between early (pT1) and late stages (pT234), and between right and left colons. Spearman's rank correlation (ρ) was used to assess the similarity or dissimilarity in gene connectivity. Equalizing cell counts across different stages, we detected 13 modules for the early stage, two of which were non-preserved in late stages. Both non-preserved modules displayed distinct gene connectivity patterns between the early and late stages, characterized by low ρ values. One module predominately dealt with myeloid cells, with genes mostly enriched for cytokine-cytokine receptor interaction potentiallystimulating myeloid cells to participate in angiogenesis. The second module, representing a subset of epithelial cells, was mainly enriched for carbohydrate digestion and absorption, influencing the gut microenvironment through the breakdown of carbohydrates. In the comparison of left vs. right colons, two of 12 modules identified in the right colon were non-preserved in the left colon. One captured a small fraction of epithelial cells and was enriched for transcriptional misregulation in cancer, potentially impacting communication between epithelial cells and the tumor microenvironment. The other predominantly contained B cells with a crucial role in maintaining human gastrointestinal health and was enriched for B-cell receptor signalling pathway. We identified modules with topological and functional differences specific to cell types between the early and late stages, and between the right and left colons. This study enhances the understanding of roles played by different cell types at different stages and sides, providing valuable insights for future studies focused on the diagnosis and treatment of CRC.

Heat Kernel of Networks with Long-Range Interactions

The heat kernel associated with a discrete graph Laplacian is the basic solution to the heat diffusion equation of a strict graph or network. In addition, this kernel represents the heat transfer that occurs over time across the network edges. Its computation involves exponentiating the Laplacian eigensystem with respect to time. In this paper, we expand upon this concept by considering a novel network-theoretic approach developed in recent years, which involves defining the k-path Laplacian operator for networks. Prior studies have adopted the notion of integrating long-range interactions (LRI) in the transmission of “information” across the nodes and edges of the network. Various methods have been employed to consider long-range interactions. We explore here the incorporation of long-range interactions in network analysis through the use of Mellin and Laplace transforms applied to the k-path Laplacian matrix. The contribution of this paper is the computation of the heat kernel associated with thek-path Laplacian, called the generalized heat kernel (GHK), and its employment as the basis for extracting stable and useful novel versions of invariants for graph characterization. The results presented in this paper demonstrate that the use of LRI improves the results obtained with classical diffusion methods for networks characterization.

계산적 인지과학의 관점에서 바라본 과학적 설명

This paper introduces the process of explanation in computational cognitive science, which involves elucidating why phenomena or events occur within the framework of causal relationships. It highlights the similarity between everyday explanations and scientific explanations when examined through the lens of cognitive processes, noting that scientific explanations are more systematic, rigorous, and precise.
 The three crucial stages of explanation discussed are firstly gathering information to provision the system's structure, secondly generating hypotheses to advance the explanation, and thirdly evaluating competitive hypotheses or explanations. The paper goes on to introduce the computational implementation of cognitive science frameworks for scientific explanation through deductive, probabilistic, and neural network approaches.
 In the deductive approach, the discussion revolves around Hempel's (1965, 1966) explanatory model, illustrating its simulation as a rule-based system when given laws and conditions. The probabilistic approach, primarily utilizing Bayesian networks, is shown to involve tasks such as generating hypotheses and assessing suitable hypotheses through conditional probabilities. Similarly, the neural network theoretical approach demonstrates that hypothesis generation or explanation through identifying causes can occur through the interaction of neural cells.
 Notably, one significant advantage of neural network theory is its ability to perform abductive inference or hypothesis generation not only based on linguistic expressions but also by accepting visual, emotional, and non-linguistic information as inputs and producing non-linguistic outputs. This advantage is ultimately seen as the ability to accommodate a variety of expression forms, termed as a "multi-modal mode."

Infrastructure Asset Management System Optimized Configuration: A Genetic Algorithm–Complex Network Theoretic Metamanagement Approach

Infrastructure Asset Management System Optimized Configuration: A Genetic Algorithm–Complex Network Theoretic Metamanagement Approach

Disease- and headache-specific microRNA signatures and their predicted mRNA targets in peripheral blood mononuclear cells in migraineurs: role of inflammatory signalling and oxidative stress

BackgroundMigraine is a primary headache with genetic susceptibility, but the pathophysiological mechanisms are poorly understood, and it remains an unmet medical need. Earlier we demonstrated significant differences in the transcriptome of migraineurs' PBMCs (peripheral blood mononuclear cells), suggesting the role of neuroinflammation and mitochondrial dysfunctions. Post-transcriptional gene expression is regulated by miRNA (microRNA), a group of short non-coding RNAs that are emerging biomarkers, drug targets, or drugs. MiRNAs are emerging biomarkers and therapeutics; however, little is known about the miRNA transcriptome in migraine, and a systematic comparative analysis has not been performed so far in migraine patients.MethodsWe determined miRNA expression of migraineurs’ PBMC during (ictal) and between (interictal) headaches compared to age- and sex-matched healthy volunteers. Small RNA sequencing was performed from the PBMC, and mRNA targets of miRNAs were predicted using a network theoretical approach by miRNAtarget.com™. Predicted miRNA targets were investigated by Gene Ontology enrichment analysis and validated by comparing network metrics to differentially expressed mRNA data.ResultsIn the interictal PBMC samples 31 miRNAs were differentially expressed (DE) in comparison to healthy controls, including hsa-miR-5189-3p, hsa-miR-96-5p, hsa-miR-3613-5p, hsa-miR-99a-3p, hsa-miR-542-3p. During headache attacks, the top DE miRNAs as compared to the self-control samples in the interictal phase were hsa-miR-3202, hsa-miR-7855-5p, hsa-miR-6770-3p, hsa-miR-1538, and hsa-miR-409-5p. MiRNA-mRNA target prediction and pathway analysis indicated several mRNAs related to immune and inflammatory responses (toll-like receptor and cytokine receptor signalling), neuroinflammation and oxidative stress, also confirmed by mRNA transcriptomics.ConclusionsWe provide here the first evidence for disease- and headache-specific miRNA signatures in the PBMC of migraineurs, which might help to identify novel targets for both prophylaxis and attack therapy.

Identification of key proteins in host–pathogen interactions between Mycobacterium tuberculosis and Homo sapiens: A systematic network theoretical approach

Tuberculosis (T.B.) is an infectious disease caused primarily by the bacterial pathogen Mycobacterium tuberculosis (Mtb) in humans. The emergence of various drug-resistant Mtb strains threatens to disrupt worldwide attempts to control the infection. The state of disease can be attributed to the host–pathogen protein interaction network. Hence in this study, we have analyzed the protein interaction network at the intra-species level, i.e., within Mtb using the Louvain community detection and at the inter-species level, i.e., between human and Mtb proteins. We observed a higher inter-connectedness in the intra-species protein network as compared to the inter-species network. This reflects a critical role of protein interaction network modulation during host–pathogenesis conditions. After analyzing 269 Mtb proteins and 2287 human proteins using the interlog approach, we observed a total of 640 host–pathogen protein interaction pairing involving 120 humans and 90 Mtb proteins. The H.P.I.s were filtered using functional protein annotations and publicly available experimental results. In the Mtb PPI network, Rv1286, Rv0462, Rv2215, Rv3559c, Rv3504, and Rv3523 are associated as a motif (a governing unit in a network) and identified as key proteins. This interaction network will help researchers better understand host–pathogen protein–protein interactions, as well as shed light on how Mtb interacts with its host.

Network Theoretical Approach to Explore Factors Affecting Signal Propagation and Stability in Dementia's Protein-Protein Interaction Network.

Dementia—a syndrome affecting human cognition—is a major public health concern given to its rising prevalence worldwide. Though multiple research studies have analyzed disorders such as Alzheimer’s disease and Frontotemporal dementia using a systems biology approach, a similar approach to dementia syndrome as a whole is required. In this study, we try to find the high-impact core regulating processes and factors involved in dementia’s protein–protein interaction network. We also explore various aspects related to its stability and signal propagation. Using gene interaction databases such as STRING and GeneMANIA, a principal dementia network (PDN) consisting of 881 genes and 59,085 interactions was achieved. It was assortative in nature with hierarchical, scale-free topology enriched in various gene ontology (GO) categories and KEGG pathways, such as negative and positive regulation of apoptotic processes, macroautophagy, aging, response to drug, protein binding, etc. Using a clustering algorithm (Louvain method of modularity maximization) iteratively, we found a number of communities at different levels of hierarchy in PDN consisting of 95 “motif-localized hubs”, out of which, 7 were present at deepest level and hence were key regulators (KRs) of PDN (HSP90AA1, HSP90AB1, EGFR, FYN, JUN, CELF2 and CTNNA3). In order to explore aspects of network’s resilience, a knockout (of motif-localized hubs) experiment was carried out. It changed the network’s topology from a hierarchal scale-free topology to scale-free, where independent clusters exhibited greater control. Additionally, network experiments on interaction of druggable genome and motif-localized hubs were carried out where UBC, EGFR, APP, CTNNB1, NTRK1, FN1, HSP90AA1, MDM2, VCP, CTNNA1 and GRB2 were identified as hubs in the resultant network (RN). We finally concluded that stability and resilience of PDN highly relies on motif-localized hubs (especially those present at deeper levels), making them important therapeutic intervention candidates. HSP90AA1, involved in heat shock response (and its master regulator, i.e., HSF1), and EGFR are most important genes in pathology of dementia apart from KRs, given their presence as KRs as well as hubs in RN.

CDK1 and HSP90AA1 Appear as the Novel Regulatory Genes in Non-Small Cell Lung Cancer: A Bioinformatics Approach.

Lung cancer is one of the most invasive cancers affecting over a million of the population. Non-small cell lung cancer (NSCLC) constitutes up to 85% of all lung cancer cases, and therefore, it is essential to identify predictive biomarkers of NSCLC for therapeutic purposes. Here we use a network theoretical approach to investigate the complex behavior of the NSCLC gene-regulatory interactions. We have used eight NSCLC microarray datasets GSE19188, GSE118370, GSE10072, GSE101929, GSE7670, GSE33532, GSE31547, and GSE31210 and meta-analyzed them to find differentially expressed genes (DEGs) and further constructed a protein–protein interaction (PPI) network. We analyzed its topological properties and identified significant modules of the PPI network using cytoscape network analyzer and MCODE plug-in. From the PPI network, top ten genes of each of the six topological properties like closeness centrality, maximal clique centrality (MCC), Maximum Neighborhood Component (MNC), radiality, EPC (Edge Percolated Component) and bottleneck were considered for key regulator identification. We further compared them with top ten hub genes (those with the highest degrees) to find key regulator (KR) genes. We found that two genes, CDK1 and HSP90AA1, were common in the analysis suggesting a significant regulatory role of CDK1 and HSP90AA1 in non-small cell lung cancer. Our study using a network theoretical approach, as a summary, suggests CDK1 and HSP90AA1 as key regulator genes in complex NSCLC network.

Chromatin Network Analyses: Towards Structure-Function Relationships in Epigenomics.

Recent technological advances have allowed us to map chromatin conformation and uncover the genome’s spatial organization of the genome inside the nucleus. These experiments have revealed the complexities of genome folding, characterized by the presence of loops and domains at different scales, which can change across development and in different cell types. There is strong evidence for a relationship between the topological properties of chromatin contacts and cellular phenotype. Chromatin can be represented as a network, in which genomic fragments are the nodes and connections represent experimentally observed spatial proximity of two genomically distant regions in a specific cell type or biological condition. With this approach we can consider a variety of chromatin features in association with the 3D structure, investigating how nuclear chromatin organization can be related to gene regulation, replication, malignancy, phenotypic variability and plasticity. We briefly review the results obtained on genome architecture through network theoretic approaches. As previously observed in protein-protein interaction networks and many types of non-biological networks, external conditions could shape network topology through a yet unidentified structure-function relationship. Similar to scientists studying the brain, we are confronted with a duality between a spatially embedded network of physical contacts, a related network of correlation in the dynamics of network nodes and, finally, an abstract definition of function of this network, related to phenotype. We summarise major developments in the study of networks in other fields, which we think can suggest a path towards better understanding how 3D genome configuration can impact biological function and adaptation to the environment.

Molecular Network Approach Reveals Rictor as a Central Target of Cardiac ProtectomiRs

Cardioprotective medications are still unmet clinical needs. We have previously identified several cardioprotective microRNAs (termed ProtectomiRs), the mRNA targets of which may reveal new drug targets for cardioprotection. Here we aimed to identify key molecular targets of ProtectomiRs and confirm their association with cardioprotection in a translational pig model of acute myocardial infarction (AMI). By using a network theoretical approach, we identified 882 potential target genes of 18 previously identified protectomiRs. The Rictor gene was the most central and it was ranked first in the protectomiR-target mRNA molecular network with the highest node degree of 5. Therefore, Rictor and its targeting microRNAs were further validated in heart samples obtained from a translational pig model of AMI and cardioprotection induced by pre- or postconditioning. Three out of five Rictor-targeting pig homologue of rat ProtectomiRs showed significant upregulation in postconditioned but not in preconditioned pig hearts. Rictor was downregulated at the mRNA and protein level in ischemic postconditioning but not in ischemic preconditioning. This is the first demonstration that Rictor is the central molecular target of ProtectomiRs and that decreased Rictor expression may regulate ischemic postconditioning-, but not preconditioning-induced acute cardioprotection. We conclude that Rictor is a potential novel drug target for acute cardioprotection.

A complex network theoretic approach for interdependence investigation: An application to radionuclide behavior in the subsurface

The failure of uranium mine tailings dams results in the infiltration and spreading of tailings in the subsurface. The fate and transport of radionuclides in the subsurface depends on several confounding, complex interdependent factors that describe the elements of the integrated system (i.e., meteorological; hydrological; hydrogeological; and, soil, groundwater, and mine tailings chemistry). The factors describing the integrated system have typically been investigated independently; however, their interdependence and resulting collective influence on the subsurface migration of radionuclides are yet to be explored. The current study develops a complex network theoretical approach to analyze these interdependencies. In this respect, a network of factors (NoF) was developed, and its characteristics (e.g., diameter, density, characteristic pathlength, average clustering coefficient, and factor centrality measures) were evaluated to determine the importance of considering these interdependencies when developing radionuclide fate and transport models. A sensitivity analysis was subsequently performed on the NoF to characterize the propagation of uncertainty associated with the factors in the NoF through a fate and transport model. The sensitivity analysis indicated that microorganisms present in the soil and mine tailings, fraction of organic carbon in the soil matrix, infiltration, and transmissivity must be well characterized (i.e., to minimize their uncertainty) when developing an integrated subsurface radionuclide fate and transport model, as uncertainty in these parameters will be amplified in the model output. The NoF developed in this study can be used to allocate data collection resources strategically in order to minimizing uncertainty in fate and transport models. This improves the reliability of fate and transport models, and ultimately leads to better management and remediation strategies to mitigate impacts from UMTD failures.

Stakeholder analysis in sustainable forest management: An application in the Yavoriv region (Ukraine)

In Ukraine, the timber volume harvested from sanitary cuttings is increasing, while the state of national forests is decreasing. These trends have focused the attention of public authorities and increased public debate on the role of forests for society. A forest policy reform process needs to build on a sound understanding of the relevant stakeholders, their mutual relationships and interests in the forestry sector. In this paper, a stakeholder analysis based on a network theoretical approach was implemented in a case study in Ukraine (Yavoriv region) to support the forest policy reform process. The study is based on experts' opinions and was structured in three stages: identification of the experts; questionnaire survey and identification of local stakeholders; classification of stakeholders. The results identified 15 stakeholders thus distributed: (1) eight stakeholders belong to the ecological coalition and seven to the economic coalition; (2) three are key stakeholders, four primary stakeholders, and eight secondary stakeholders. The proposed method of stakeholder analysis is aimed to balance the number of stakeholders from each coalition to include all interests at stake in the participatory process. The stakeholder analysis marks the first step in the implementation of sustainable forest management in the Yavoriv region and could serve as a model for the other Ukrainian regions.

Extreme synchronization events in a Kuramoto model: The interplay between resource constraints and explosive transitions.

Many living and artificial systems possess structural and dynamical properties of complex networks. One of the most exciting living networked systems is the brain, in which synchronization is an essential mechanism of its normal functioning. On the other hand, excessive synchronization in neural networks reflects undesired pathological activity, including various forms of epilepsy. In this context, network-theoretical approach and dynamical modeling may uncover deep insight into the origins of synchronization-related brain disorders. However, many models do not account for the resource consumption needed for the neural networks to synchronize. To fill this gap, we introduce a phenomenological Kuramoto model evolving under the excitability resource constraints. We demonstrate that the interplay between increased excitability and explosive synchronization induced by the hierarchical organization of the network forces the system to generate short-living extreme synchronization events, which are well-known signs of epileptic brain activity. Finally, we establish that the network units occupying the medium levels of hierarchy most strongly contribute to the birth of extreme events emphasizing the focal nature of their origin.

Transformerless Stacked Active Bridge Converters: Analysis, Properties, and Synthesis

This article presents analysis, properties, and systematic synthesis of a new class of hybrid dc–dc converters named transformerless stacked active bridge (TSAB) converters. The TSAB converters, which are obtained from switched-capacitor (SC) converters by insertion of small ac inductors, inherit advantages of parent SC converters together with conversion and control characteristics similar to transformer-isolated dual-active-bridge (DAB) dc–dc converters. Features of TSAB converters include soft charging and discharging of capacitors, zero voltage switching, low peak and rms current stresses, low energy storage requirement of magnetic components, and regulation capabilities using simple phase-shift control. Based on a network-theoretic approach, an algebraic representation of TSAB converters yields general results for dc characteristics and component stresses. Furthermore, a systematic synthesis approach is formulated allowing construction of TSAB converter topologies starting from various two-phase SC converters. Synthesis results are presented for TSAB converters with two inductors obtained from well-known SC parent topologies, including Dickson, ladder, stacked-ladder, doubler, and Fibonacci. Experimental results are summarized for 48-to-12 V doubler, 48-to-12 V Dickson, and 36-to-12 V ladder TSAB prototypes. It is shown how the TSAB prototypes use small ac inductors (tens to hundreds of nanohenries) while operating at relatively low switching frequencies (150–200 kHz) and have measured efficiency above 98% over wide load range.

A Network-Based Mixed Methods Approach to Analyze Current Perspectives on Personalized Oncological Medicine in Austria.

Personalized medicine (PM) to tailor healthcare (HC) to the individual, is a promising but challenging concept. So far, no study exists investigating stakeholders’ perspectives on PM in oncology in Austria potentially hindering implementation, which was the aim of this study. We performed semi-structured interviews among experts (n = 14) and cancer patients (n = 2) of the Vienna General Hospital and the Medical University of Vienna and analyzed them by a mixed methods network theoretical approach. Study results show a great variety of topics addressed by the interviewees. Clear differences in the topic selection between patients and experts could be observed. Patient-doctor relationship was the most prominent theme among experts, whereas HC systems and public health in PM was more relevant for the patients. Although promising new molecular pathology methods were explicitly mentioned, the experts believed that their practical implementation and the implementation of PM in standard care will take a long time in Austria. A variety of concerns regarding PM were mentioned by the experts, including communication issues and knowledge gaps. Besides important insights into the current situation of PM in Austria, the study has shown that network theory is a powerful tool for analyzing qualitative interview data.

Territoriality, environment and hybrid governance tensions in alternative food networks: Cases of small‐scale viticulture in Chile

AbstractViticulture and wine making are a key part of the socio‐ecological landscape of central and south‐central Chile. The sector is comprised of both dominant export‐oriented companies (oriented to monoculture and agribusiness) and traditional small‐scale production (more‐or‐less locally embedded and community oriented). Since the 1990s, different small‐scale viticulture organizations emerged to deal with different challenges imposed by globalisation in a neoliberal institutional setting which favours extractive forms of productions (environmental resource intensive). In this article, we use the Alternative Food Network (AFN) theoretical framework and hybrid governance approach to analyse the governance tensions in five small‐scale viticulture organisations from Marga‐Marga, Lontué and Itata valleys. Using interviews and social cartography as methods, the study shows how the territorial strategies of AFNs shape governance tensions on three dimensions: geographic space, environmental resources and human relations. Finally, the article proposes a conceptual framework to understand the relationship between scalar dynamics and the socio‐environmental core values, strategic objectives and societal missions of AFNs.

Kinless hubs are potential target genes in prostate cancer network

Complex disease networks can be studied successfully using network theoretical approach which helps in finding key disease genes and associated disease modules. We studied prostate cancer (PCa) protein-protein interaction (PPI) network constructed from patients' gene expression datasets and found that the network exhibits hierarchical scale free topology which lacks centrality lethality rule. Knockout experiments of the sets of leading hubs from the network leads to transition from hierarchical (HN) to scale free (SF) topology affecting network integration and organization. This transition, HN → SF, due to removal of significant number of the highest degree hubs, leads to relatively decrease in information processing efficiency, cost effectiveness of signal propagation, compactness, clustering of nodes and energy distributions. A systematic transition from a diassortative PCa PPI network to assortative networks after the removal of top 50 hubs then again reverting to disassortativity nature on further removal of the hubs was also observed indicating the dominance of the largest hubs in PCa network intergration. Further, functional classification of the hubs done by using within module degrees and participation coefficients for PCa network, and leading hubs knockout experiments indicated that kinless hubs serve as the basis of establishing links among constituting modules and heterogeneous nodes to maintain network stabilization. We, then, checked the essentiality of the hubs in the knockout experiment by performing Fisher's exact test on the hubs, and showed that removal of kinless hubs corresponded to maximum lethality in the network. However, excess removal of these hubs essentially may cause network breakdown.

Analyzing the relationship between financial literacy and financial inclusion by microfinance banks in developing countries: social network theoretical approach

PurposeThe main purpose of this study is to establish the mediating effect of social network in the relationship between financial literacy and financial inclusion of the poor by microfinance banks in developing countries.Design/methodology/approachThe study adopted a cross-sectional research design and data were collected from the poor who resides in rural Uganda. Structural equation modelling (SEM) through analysis of moment structures (AMOS) was used to analyze the data. Bootstrap approach with 5,000 samples was run to establish the mediating effect of social network in the relationship between financial literacy and financial inclusion of the poor by microfinance banks in developing countries.FindingsThe results showed that social network significantly and positively mediate the relationship between financial literacy and financial inclusion of the poor by microfinance banks in developing countries. In addition, financial literacy also has a direct significant and positive effect on financial inclusion. Overall, the findings suggest that the presence of social network fully mediate the effect of financial literacy on financial inclusion of the poor by microfinance banks in developing countries.Research limitations/implicationsThis study adopted a cross-sectional research design and data were collected using a semi-structured questionnaire. Future studies could adopt longitudinal research design to establish the dynamic characteristics of the samples under study over time. Besides, this study collected data from only poor households who were clients of microfinance banks located in rural Uganda. It ignored the other section of the population who were not the poor. Therefore, future studies could use the other section of the population who are clients of commercial banks.Practical implicationsThe advocates of financial literacy and managers of microfinance banks in developing countries should ensure using existing local structures such as community and village associations to conduct financial literacy training. The village associations help in mobilizing members who are close-knit based on the existing societal ties that can be used as a channel for disseminating vital financial literacy information. Indeed, financial literacy workshops, seminars, and business clinics can be easily conducted to individuals who are members of the village associations.Originality/valueThis paper integrates social network theory in the relationship between financial literacy and financial inclusion of the poor by microfinance banks in developing countries. Social network acts as a conduit through which financial knowledge and skills flow to increase the scope of financial inclusion of the poor in developing countries.

Exploring modularity in biological networks.

Network theoretical approaches have shaped our understanding of many different kinds of biological modularity. This essay makes the case that to capture these contributions, it is useful to think about the role of network models in exploratory research. The overall point is that it is possible to provide a systematic analysis of the exploratory functions of network models in bioscientific research. Using two examples from molecular and developmental biology, I argue that often the same modelling approach can perform one or more exploratory functions, such as introducing new directions of research, offering a complementary set of concepts, methods and algorithms for individuating important features of natural phenomena, generating proofs of principle demonstrations and potential explanations for phenomena of interest and enlarging the scope of certain research agendas. This article is part of the theme issue 'Unifying the essential concepts of biological networks: biological insights and philosophical foundations'.