Showing papers by "Sarika Jalan published in 2017"
TL;DR: This novel approach helps in understanding cancer at the fundamental level and provides a clue to develop promising and nascent concept of single drug therapy for multiple diseases as well as personalized medicine.
Abstract: Cancer complexome comprises a heterogeneous and multifactorial milieu that varies in cytology, physiology, signaling mechanisms and response to therapy. The combined framework of network theory and spectral graph theory along with the multilayer analysis provides a comprehensive approach to analyze the proteomic data of seven different cancers, namely, breast, oral, ovarian, cervical, lung, colon and prostate. Our analysis demonstrates that the protein-protein interaction networks of the normal and the cancerous tissues associated with the seven cancers have overall similar structural and spectral properties. However, few of these properties implicate unsystematic changes from the normal to the disease networks depicting difference in the interactions and highlighting changes in the complexity of different cancers. Importantly, analysis of common proteins of all the cancer networks reveals few proteins namely the sensors, which not only occupy significant position in all the layers but also have direct involvement in causing cancer. The prediction and analysis of miRNAs targeting these sensor proteins hint towards the possible role of these proteins in tumorigenesis. This novel approach helps in understanding cancer at the fundamental level and provides a clue to develop promising and nascent concept of single drug therapy for multiple diseases as well as personalized medicine.
39 citations
TL;DR: The investigations reveal the existence of a few special structural features of such optimized networks, for instance, the presence of a set of edges which are necessary for localization, and rewiring only one of them leads to complete delocalization of the principal eigenvector.
Abstract: Network science is increasingly being developed to get new insights about behavior and properties of complex systems represented in terms of nodes and interactions. One useful approach is investigating the localization properties of eigenvectors having diverse applications including disease-spreading phenomena in underlying networks. In this work, we evolve an initial random network with an edge rewiring optimization technique considering the inverse participation ratio as a fitness function. The evolution process yields a network having a localized principal eigenvector. We analyze various properties of the optimized networks and those obtained at the intermediate stage. Our investigations reveal the existence of a few special structural features of such optimized networks, for instance, the presence of a set of edges which are necessary for localization, and rewiring only one of them leads to complete delocalization of the principal eigenvector. Furthermore, we report that principal eigenvector localization is not a consequence of changes in a single network property and, preferably, requires the collective influence of various distinct structural as well as spectral features.
25 citations
TL;DR: In this article, a simple transformation of the network's adjacency matrix provides an understanding to the origins of the occurrence of high multiplicities in the networks spectra, and the eigenvectors associated with the degenerate eigenvalues shed light on the structures contributing to the degeneracy.
Abstract: Many real-world networks exhibit a high degeneracy at few eigenvalues. We show that a simple transformation of the network's adjacency matrix provides an understanding to the origins of the occurrence of high multiplicities in the networks spectra. We find that the eigenvectors associated with the degenerate eigenvalues shed light on the structures contributing to the degeneracy. Since these degeneracies are rarely observed in model graphs, we present results for various cancer networks. This approach gives the opportunity to search for structures contributing to degeneracy which might have an important role in a network.
24 citations
TL;DR: This work investigates the optimization of synchronizability in multiplex networks by evolving only one layer while keeping other layers fixed to show the conditions under which the efficiency of convergence to the most optimal structure is almost as good as the case where both layers are rewired during an optimization process.
Abstract: The mathematical framework of multiplex networks has been increasingly realized as a more suitable framework for modeling real-world complex systems. In this work, we investigate the optimization of synchronizability in multiplex networks by evolving only one layer while keeping other layers fixed. Our main finding is to show the conditions under which the efficiency of convergence to the most optimal structure is almost as good as the case where both layers are rewired during an optimization process. In particular, interlayer coupling strength responsible for the integration between the layers turns out to be a crucial factor governing the efficiency of optimization even for the cases when the layer going through the evolution has nodes interacting much more weakly than those in the fixed layer. Additionally, we investigate the dependency of synchronizability on the rewiring probability which governs the network structure from a regular lattice to the random networks. The efficiency of the optimization process preceding evolution driven by the optimization process is maximum when the fixed layer has regular architecture, whereas the optimized network is more synchronizable for the fixed layer having the rewiring probability lying between the small-world transition and the random structure.
22 citations
TL;DR: It is shown that degree homogeneity plays a crucial role in determining the fractal nature of the underlying network, and is reported on six different protein-protein interaction networks along with their corresponding random networks.
Abstract: The fractal nature of graphs has traditionally been investigated by using the network’s nodes as the basic units Here, instead, we propose to concentrate on the graph’s edges, and introduce a practical and computationally not demanding method for revealing changes in the fractal behavior of networks, and particularly for allowing distinction between mono-fractal, quasi mono-fractal, and multi-fractal structures We show that degree homogeneity plays a crucial role in determining the fractal nature of the underlying network, and report on six different protein-protein interaction networks along with their corresponding random networks Our analysis allows to identify varying levels of complexity in the species
21 citations
TL;DR: It is demonstrated that ES is a generic feature of multiplex network of second-order Kuramoto oscillators and can exist in absence of a frequency-degree correlation, and induced ES transition in the heterogeneous layer of multipleX networks can be controlled by varying inter and intra-layer coupling strengths.
Abstract: Explosive synchronization (ES) of coupled oscillators on networks is shown to be originated from the existence of correlation between natural frequencies of oscillators and degrees of corresponding nodes. Here, we demonstrate that ES is a generic feature of multiplex network of second-order Kuramoto oscillators and can exist in the absence of a frequency-degree correlation. A monoplex network of second-order Kuramoto oscillators bearing homogeneous (heterogeneous) degree distribution is known to display the first-order (second-order) transition to synchronization. We report that multiplexing of two such networks having homogeneous degree distribution support the first-order transition in both the layers thereby facilitating ES. More interesting is the multiplexing of a layer bearing heterogeneous degree distribution with another layer bearing homogeneous degree distribution, which induces a first-order (ES) transition in the heterogeneous layer which was incapable of showing the same in isolation. Further, we report that such induced ES transition in the heterogeneous layer of multiplex networks can be controlled by varying inter- and intra-layer coupling strengths. Our findings emphasize the importance of multiplexing or the impact of one layer on the dynamical evolution of other layers of systems having inherent multiplex or multilevel architecture.
17 citations
TL;DR: In this paper, the authors investigate the impact of multiplexing of a layer having repulsively coupled oscillators on the occurrence of chimeras in the layer having attractively coupled identical oscillators.
Abstract: Yes! Very much so. A chimera state refers to the coexistence of a coherent-incoherent dynamical evolution of identically coupled oscillators. We investigate the impact of multiplexing of a layer having repulsively coupled oscillators on the occurrence of chimeras in the layer having attractively coupled identical oscillators. We report that there exists an enhancement in the appearance of the chimera state in one layer of the multiplex network in the presence of repulsive coupling in the other layer. Furthermore, we show that a small amount of inhibition or repulsive coupling in one layer is sufficient to yield the chimera state in another layer by destroying its synchronized behavior. These results can be used to obtain insight into dynamical behaviors of those systems where both attractive and repulsive couplings exist among their constituents.
17 citations
TL;DR: It is found that an indirect but sufficiently strong coupling through the regular layer can induce both phase order in the originally nonsynchronized random layer and global order, even when an isolated regular layer does not manifest it in principle.
Abstract: Inspired by the recent interest in collective dynamics of biological neural networks immersed in the glial cell medium, we investigate the frequency and phase order, i.e., Kuramoto type of synchronization in a multiplex two-layer network of phase oscillators of different time scales and topologies. One of them has a long-range connectivity, exemplified by the Erdős-Renyi random network, and supports both kinds of synchrony. The other is a locally coupled two-dimensional lattice that can reach frequency synchronization but lacks phase order. Drastically different layer frequencies disentangle intra- and interlayer synchronization. We find that an indirect but sufficiently strong coupling through the regular layer can induce both phase order in the originally nonsynchronized random layer and global order, even when an isolated regular layer does not manifest it in principle. At the same time, the route to global synchronization is complex: an initial onset of (partial) synchrony in the regular layer, when its intra- and interlayer coupling is increased, provokes the loss of synchrony even in the originally synchronized random layer. Ultimately, a developed asynchronous dynamics in both layers is abruptly taken over by the global synchrony of both kinds.
12 citations
TL;DR: In this paper, the authors introduce a simple model where communication delays and multiplexing are simultaneously present and report the rich phenomenology which is actually due to their interplay on cluster synchronization.
Abstract: Communication delays and multiplexing are ubiquitous features of real-world network systems. We here introduce a simple model where these two features are simultaneously present and report the rich phenomenology which is actually due to their interplay on cluster synchronization. A delay in one layer has non trivial impacts on the collective dynamics of the other layers, enhancing or suppressing synchronization. At the same time, multiplexing may also enhance cluster synchronization of delayed layers. We elucidate several nontrivial (and anti-intuitive) scenarios, which are of interest and potential application in various real-world systems, where the introduction of a delay may render synchronization of a layer robust against changes in the properties of the other layers.
11 citations
TL;DR: It is shown that a small amount of inhibition or repulsive coupling in one layer is sufficient to yield the chimera state in another layer by destroying its synchronized behavior, and can be used to obtain insight into dynamical behaviors of those systems where both attractive and repulsive couplings exist among their constituents.
Abstract: Yes! Very much so.
A chimera state refers to the coexistence of a coherent-incoherent dynamical evolution of identically coupled oscillators. We investigate the impact of multiplexing of a lyer having repulsively coupled oscillators on occurrence of chimeras in the layer having attractively coupled identical oscillators. We report that there exists an enhancement in the appearance of chimera state in one layer of multiplex network in the presence of repulsive coupling in the other layer. Furthermore, we show that a small amount of inhibition or repulsive coupling in one layer is sufficient to yield chimera state in another layer by destroying its synchronized behavior. These results can be used to get insight into dynamical behaviors of those systems where both attractive and repulsive coupling exist among their constituents.
9 citations
TL;DR: It is shown that the degree-degree correlations have a major impact on global synchronizability (GS) of multiplex networks, enabling the specification of synchronIZability by only changing the degree ofdegree correlations of the mirror nodes while maintaining the connection architecture of the individual layer unaltered.
Abstract: We show that the degree-degree correlations have a major impact on global synchronizability (GS) of multiplex networks, enabling the specification of synchronizability by only changing the degree-degree correlations of the mirror nodes while maintaining the connection architecture of the individual layer unaltered. If individual layers have nodes that are mildly correlated, the multiplex network is best synchronizable when the mirror degrees are strongly negatively correlated. If individual layers have nodes with strong degree-degree correlations, mild correlations among the degrees of mirror nodes are the best strategy for the optimization of GS. Global synchronization also depend on the density of connections, a phenomenon not observed in a single layer network. The results are crucial to understand, predict, and specify behavior of systems having multiple types of connections among the interacting units.
TL;DR: In this article, the combined framework of network theory and spectral graph theory along with the multilayer anal- ysis provides a comprehensive approach to analyze the proteomic data of seven different cancers, namely, breast, oral, ovarian, cervical, lung, colon and prostate.
Abstract: Cancer complexome comprises a heterogeneous and multifactorial milieu that varies in cytology, physiology, signaling mechanisms and response to therapy. The combined framework of network theory and spectral graph theory along with the multilayer anal- ysis provides a comprehensive approach to analyze the proteomic data of seven different cancers, namely, breast, oral, ovarian, cervical, lung, colon and prostate. Our analysis demonstrates that the protein-protein interaction networks of the normal and the cancerous tissues associated with the seven cancers have overall similar structural and spectral properties. However, few of these properties implicate unsystematic changes from the normal to the disease networks depicting difference in the interactions and highlighting changes in the complexity of different cancers. Importantly, analysis of common proteins of all the cancer networks reveals few proteins namely the sensors, which not only occupy significant position in all the layers but also have direct involvement in causing cancer. The prediction and analysis of miRNAs targeting these sensor proteins hint towards the possible role of these proteins in tumorigenesis. This novel approach helps in understanding cancer at the fundamental level and provides a clue to develop promising and nascent concept of single drug therapy for multiple diseases as well as personalized medicine.
TL;DR: This work evolves multiplex networks, comprising antisymmetric couplings in one layer depicting predator-prey relationship and symmetric coupling in the other depicting mutualistic (or competitive) relationship, based on stability maximization through the largest eigenvalue of the corresponding adjacency matrices.
Abstract: Investigating the relation between various structural patterns found in real-world networks and the stability of underlying systems is crucial to understand the importance and evolutionary origin of such patterns. We evolve multiplex networks, comprising antisymmetric couplings in one layer depicting predator-prey relationship and symmetric couplings in the other depicting mutualistic (or competitive) relationship, based on stability maximization through the largest eigenvalue of the corresponding adjacency matrices. We find that there is an emergence of the correlated multiplexity between the mirror nodes as the evolution progresses. Importantly, evolved values of the correlated multiplexity exhibit a dependence on the interlayer coupling strength. Additionally, the interlayer coupling strength governs the evolution of the disassortativity property in the individual layers. We provide analytical understanding to these findings by considering starlike networks representing both the layers. The framework discussed here is useful for understanding principles governing the stability as well as the importance of various patterns in the underlying networks of real-world systems ranging from the brain to ecology which consist of multiple types of interaction behavior.
Posted Content•
12 Dec 2017
TL;DR: The investigations reveal the sensitivity of the PEV to a single edge rewiring in the optimized multiplex network structure corresponding to the most localized PEV providing the deeper insight into the localization behavior of the multiplex networks.
Abstract: We study localization properties of principal eigenvector (PEV) of multiplex networks. Starting with a multiplex network corresponding to a delocalized PEV, we rewire the network edges using an optimization technique such that the PEV of the rewired multiplex network becomes more localized. The framework allows us to scrutinize structural and spectral properties of the networks at various localization points during the rewiring process. We show that by rewiring only one layer of the multiplex network, we can achieve the PEV localization of the entire system. Our investigations reveal the sensitivity of the PEV to a single edge rewiring in the optimized multiplex network structure corresponding to the most localized PEV providing the deeper insight into the localization behavior of the multiplex networks. Furthermore, by constructing multiplex network using real-world social and biological data, we show that our simulation results for model multiplex network are in good agreement with the properties of these real-world multiplex network. The study is relevant to applications that require understanding of how perturbation propagates in multiplex networks.
TL;DR: This Article contains an error in Figure 1, where the graph for Figure 1d is a duplicate of Figure 1b, and the correct Figure 1 appears below.
Abstract: Scientific Reports 7: Article number: 41676; published online: 03 February 2017; updated: 03 May 2017. This Article contains an error in Figure 1, where the graph for Figure 1d is a duplicate of Figure 1b. The correct Figure 1 appears below.
05 Oct 2017
TL;DR: It is suggested that late appearance of known early sporulation regulators and a delay in crosstalk between functional modules can be construed as the prime reasons behind low sporulation efficiency of the S288c.
Abstract: Using network theory on an integrated time-resolved genome-wide gene expression data, we investigated the intricate dynamic regulatory relationships of transcription factors and target genes to unravel signatures that contribute to extreme phenotypic differences in yeast, Saccharomyces cerevisiae. We performed a comparative analysis of the gene expression profiles of two yeast strains SK1 and S288c which are known for high and low sporulation efficiency, respectively. The results based on various structural attributes of the networks, such as clustering coefficient, degree-degree correlations, and betweenness centrality suggested that a delay in crosstalk between functional modules can be construed as one of the prime reasons behind low sporulation efficiency of S288c strain. A more hierarchical structure in the late phase of sporulation in S288c indicated an attempt of this low sporulating strain to obtain modularity, which is a feature of early sporulation phase. Further, the weak ties analysis revealed that mostly meiosis-associated genes were the end nodes of the weak ties for the high sporulating SK1 strain, while for the low sporulating S288c strain these nodes were mitotic genes. This again was a clear indication of the delay in regulatory activities in the S288c strain, which are essential to initiate sporulation. Our results demonstrate the potential of this framework in identifying candidate nodes contributing to phenotypic diversity in natural populations with application prospects in drug target discovery and personalized health.
05 Oct 2017
TL;DR: In this paper, a powerful network model was developed to describe complex mitochondrial evolutionary patterns among codon and non-codon positions, and the diversity observed in the mtDNA was compared with mutations, co-occurring mutations, and network motifs considering codon positions as causing agent.
Abstract: The nucleotide polymorphism in the human mitochondrial genome (mtDNA) tolled by codon position bias plays an indispensable role in human population dispersion and expansion. Herein, genome-wide nucleotide co-occurrence networks were constructed using data comprised of five different geographical regions and around 3000 samples for each region. We developed a powerful network model to describe complex mitochondrial evolutionary patterns among codon and non-codon positions. We found evidence that the evolution of human mitochondria DNA is dominated by adaptive forces, particularly mutation and selection, which was supported by many previous studies. The diversity observed in the mtDNA was compared with mutations, co-occurring mutations, network motifs considering codon positions as causing agent. This comparison showed that long-range nucleotide co-occurrences have a large effect on genomic diversity. Most notably, codon motifs apparently underpinned the preferences among codon positions for co-evolution which is probably highly biased during the origin of the genetic code. Our analysis also showed that variable nucleotide positions of different human sub-populations implemented the independent mtDNA evolution to its geographical dispensation. Ergo, this study has provided both a network framework and a codon glance to investigate co-occurring genomic variations that are critical in underlying complex mitochondrial evolution.
Dissertation•
16 Dec 2017