Showing papers by "Sarika Jalan published in 2015"

Impact of a leader on cluster synchronization.

Abstract: We study the mechanisms of frequency-synchronized cluster formation in coupled nonidentical oscillators and investigate the impact of presence of a leader on the cluster synchronization. We find that the introduction of a leader, a node having large parameter mismatch, induces a profound change in the cluster pattern as well as in the mechanism of the cluster formation. The emergence of a leader generates a transition from the driven to the mixed cluster state. The frequency mismatch turns out to be responsible for this transition. Additionally, for a chaotic evolution, the driven mechanism stands as a primary mechanism for the cluster formation, whereas for a periodic evolution the self-organization mechanism becomes equally responsible.

A multilayer protein-protein interaction network analysis of different life stages in Caenorhabditis elegans

Abstract: Molecular networks act as the backbone of cellular activities, providing an excellent opportunity to understand the developmental changes in an organism. While network data usually constitute only stationary network graphs, constructing a multilayer PPI network may provide clues to the particular developmental role at each stage of life and may unravel the importance of these developmental changes. The developmental biology model of Caenorhabditis elegans analyzed here provides a ripe platform to understand the patterns of evolution during the life stages of an organism. In the present study, the widely studied network properties exhibit overall similar statistics for all the PPI layers. Further, the analysis of the degree-degree correlation and spectral properties not only reveals crucial differences in each PPI layer but also indicates the presence of the varying complexity among them. The PPI layer of the nematode life stage exhibits various network properties different to the rest of the PPI layers, indicating the specific role of cellular diversity and developmental transitions at this stage. The framework presented here provides a direction to explore and understand the developmental changes occurring in the different life stages of an organism.

Randomness and preserved patterns in cancer network

Abstract: Breast cancer has been reported to account for the maximum cases among all female cancers till date. In order to gain a deeper insight into the complexities of the disease, we analyze the breast cancer network and its normal counterpart at the proteomic level. While the short range correlations in the eigenvalues exhibiting universality provide an evidence towards the importance of random connections in the underlying networks, the long range correlations along with the localization properties reveal insightful structural patterns involving functionally important proteins. The analysis provides a benchmark for designing drugs which can target a subgraph instead of individual proteins.

Origin and implications of zero degeneracy in networks spectra.

Abstract: The spectra of many real world networks exhibit properties which are different from those of random networks generated using various models. One such property is the existence of a very high degeneracy at the zero eigenvalue. In this work, we provide all the possible reasons behind the occurrence of the zero degeneracy in the network spectra, namely, the complete and partial duplications, as well as their implications. The power-law degree sequence and the preferential attachment are the properties which enhances the occurrence of such duplications and hence leading to the zero degeneracy. A comparison of the zero degeneracy in protein-protein interaction networks of six different species and in their corresponding model networks indicates importance of the degree sequences and the power-law exponent for the occurrence of zero degeneracy.

Synchronization in delayed multiplex networks

Abstract: We study the impact of multiplexing on the global phase synchronizability of different layers in the delayed coupled multiplex networks. We find that at strong couplings, the multiplexing induces the global synchronization in sparse networks. The introduction of global synchrony depends on the connection density of the layers being multiplexed, which further depends on the underlying network architecture. Moreover, multiplexing may lead to a transition from a quasi-periodic or chaotic evolution to a periodic evolution. For the periodic case, the multiplexing may lead to a change in the period of the dynamical evolution. Additionally, delay in the couplings may bring upon synchrony to those multiplex networks which do not exhibit synchronization for the undelayed evolution. Using a simple example of two globally connected layers forming a multiplex network, we show how delay brings upon a possibility for the inter-layer global synchrony, that is not possible for the undelayed evolution.

Assortative and disassortative mixing investigated using the spectra of graphs.

Abstract: We investigate the impact of degree-degree correlations on the spectra of networks. Even though density distributions exhibit drastic changes depending on the (dis)assortative mixing and the network architecture, the short-range correlations in eigenvalues exhibit universal random matrix theory predictions. The long-range correlations turn out to be a measure of randomness in (dis)assortative networks. The analysis further provides insight into the origin of high degeneracy at the zero eigenvalue displayed by a majority of biological networks.

Dissortativity and duplications in oral cancer

Abstract: More than 300 000 new cases worldwide are being diagnosed with oral cancer annually. Complexity of oral cancer renders designing drug targets very difficult. We analyse protein-protein interaction network for the normal and oral cancer tissue and detect crucial changes in the structural properties of the networks in terms of the interactions of the hub proteins and the degree-degree correlations. Further analysis of the spectra of both the networks, while exhibiting universal statistical behaviour, manifest distinction in terms of the zero degeneracy, providing insight to the complexity of the underlying system.

Optimization of synchronizability in multiplex networks

Abstract: We investigate the optimization of synchronizability in multiplex networks and demonstrate that the interlayer coupling strength is the deciding factor for the efficiency of optimization. The optimized networks have homogeneity in the degree as well as in the betweenness centrality. Additionally, the interlayer coupling strength crucially affects various properties of individual layers in the optimized multiplex networks. We provide an understanding as to how the emerged network properties are shaped or affected when the evolution renders them better synchronizable.

Network Topologies Decoding Cervical Cancer.

Abstract: According to the GLOBOCAN statistics, cervical cancer is one of the leading causes of death among women worldwide. It is found to be gradually increasing in the younger population, specifically in the developing countries. We analyzed the protein-protein interaction networks of the uterine cervix cells for the normal and disease states. It was found that the disease network was less random than the normal one, providing an insight into the change in complexity of the underlying network in disease state. The study also portrayed that, the disease state has faster signal processing as the diameter of the underlying network was very close to its corresponding random control. This may be a reason for the normal cells to change into malignant state. Further, the analysis revealed VEGFA and IL-6 proteins as the distinctly high degree nodes in the disease network, which are known to manifest a major contribution in promoting cervical cancer. Our analysis, being time proficient and cost effective, provides a direction for developing novel drugs, therapeutic targets and biomarkers by identifying specific interaction patterns, that have structural importance.

Prognostic interaction patterns in diabetes mellitus II: A random-matrix-theory relation.

Abstract: We analyze protein-protein interactions in diabetes mellitus II and its normal counterpart under the combined framework of random matrix theory and network biology. This disease is the fifth-leading cause of death in high-income countries and an epidemic in developing countries, affecting around 8% of the total adult population in the world. Treatment at the advanced stage is difficult and challenging, making early detection a high priority in the cure of the disease. Our investigation reveals specific structural patterns important for the occurrence of the disease. In addition to the structural parameters, the spectral properties reveal the top contributing nodes from localized eigenvectors, which turn out to be significant for the occurrence of the disease. Our analysis is time-efficient and cost-effective, bringing a new horizon in the field of medicine by highlighting major pathways involved in the disease. The analysis provides a direction for the development of novel drugs and therapies in curing the disease by targeting specific interaction patterns instead of a single protein.

Optimization of synchronizability in multiplex networks

Abstract: We investigate the optimization of synchronizability in multiplex networks and demonstrate that the interlayer coupling strength is the deciding factor for the efficiency of optimization. The optimized networks have homogeneity in the degree as well as in the betweenness centrality. Additionally, the interlayer coupling strength crucially affects various properties of individual layers in the optimized multiplex networks. We provide an understanding to how the emerged network properties are shaped or affected when the evolution renders them better synchronizable.

Deciphering versatility and cooperation in multilayer social networks.

Abstract: Despite large scale availability of social data, our understanding of the basic laws governing human behaviour remains limited, owing to the lack of a proper framework which can capture the interplay of various interdependent factors affecting social interactions. In the recent years, multilayer networks has increasingly been realized to provide an efficient framework for understanding the intricacies of complex real world systems. The present study encompasses the multilayer network analysis of Bollywood, the largest film industry of the world, comprising of a massive time-varying social data. Making around 1500 films annually, Bollywood has emerged as a globally recognized and appreciated platform for cultural exchange. This film industry acts as a mirror of the society and the rapidly changing nature of the society is reflected in the depictions of films. This renders this model system to provide a ripe platform to understand social behaviour by analyzing the patterns of evolution and the success of individuals in the society. Similarity in the degree distribution across the individual layers validates our basis of multilayering. While the degree-degree correlations of the whole networks reveal that in general nodes do not exhibit any particular preference for pairing up, multilayer framework indicates the gradual restoration of cooperation in the recent dataset. Working in more number of genres comes up as an intrinsic property of lead nodes. Further, versatility in pairs and triads has been shown to demonstrate its impact on the success of lead nodes. While repeated cooperation in pairs of dissimilar types of nodes has been shown to yield success to the lead nodes, triads of similar types of nodes turn out to be more successful. Weak ties analysis emphasize on the importance of every type of node in the society.

Importance of randomness in biological networks: A random matrix analysis

Abstract: Random matrix theory, initially proposed to understand the complex interactions in nuclear spectra, has demonstrated its success in diverse domains of science ranging from quantum chaos to galaxies. We demonstrate the applicability of random matrix theory for networks by providing a new dimension to complex systems research. We show that in spite of huge differences these interaction networks, representing real-world systems, posses from random matrix models, the spectral properties of the underlying matrices of these networks follow random matrix theory bringing them into the same universality class. We further demonstrate the importance of randomness in interactions for deducing crucial properties of the underlying system. This paper provides an overview of the importance of random matrix framework in complex systems research with biological systems as examples.

Application of Random Matrix Theory to Complex Networks

Abstract: The present article provides an overview of recent developments in spectral analysis of complex networks under random matrix theory framework. Adjacency matrix of unweighted networks, reviewed here, differ drastically from a random matrix, as former have only binary entries. Remarkably, short range correlations in corresponding eigenvalues of such matrices exhibit Gaussian orthogonal statistics of RMT and thus bring them into the universality class. Spectral rigidity of spectra provides measure of randomness in underlying networks. We will consider several examples of model networks vastly studied in last two decades. To the end we would provide potential of RMT framework and obtained results to understand and predict behavior of complex systems with underlying network structure.

Interplay of mutation and disassortativity.

Abstract: Despite disassortativity being commonly observed in many biological networks, our current understanding of its evolutionary origin is inadequate. Motivated by the occurrence of mutations during an evolutionary time span that results in changes in the behavior of interactions, we demonstrate that if we maximize the stability of the underlying system, the genetic algorithm leads to the evolution of a disassortative structure. The mutation probability governs the degree of saturation of the disassortativity coefficient, and this reveals the origin of the wide range of disassortativity values found in real systems. We analytically verify these results for star networks, and by considering various values for the antisymmetric couplings, we find a regime in which scale-free networks are more stable than the corresponding random networks.

Emergence of chimera in multiplex network

Abstract: Chimera is a relatively new emerging phenomenon where coexistence of synchronous and asynchronous state is observed in symmetrically coupled dynamical units. We report observation of the chimera state in multiplex networks where individual layer is represented by 1-d lattice with non-local interactions. While, multiplexing does not change the type of the chimera state and retains the multi-chimera state displayed by the isolated networks, it changes the regions of the incoherence. We investigate emergence of coherent-incoherent bifurcation upon varying the control parameters, namely, the coupling strength and the network size. Additionally, we investigate the effect of initial condition on the dynamics of the chimera state. Using a measure based on the differences between the neighboring nodes which distinguishes smooth and non-smooth spatial profile, we find the critical coupling strength for the transition to the chimera state. Observing chimera in a multiplex network with one to one inter layer coupling is important to gain insight to many real world complex systems which inherently posses multilayer architecture.

Emergence of (bi)multi-partiteness in networks having inhibitory and excitatory couplings

Abstract: (Bi)multi-partite interaction patterns are observed frequently in real-world systems which have inhibitory and excitatory couplings. We hypothesize these structural interaction patterns to be stable and naturally arising in the course of evolution. We demonstrate that a random structure evolves to a (bi)multi-partite structure by imposing stability criterion through minimization of the largest eigenvalue in the genetic algorithm devised on the interacting units having inhibitory and excitatory couplings. The evolved interaction patterns are robust against changes in the initial network architecture as well as against fluctuations in the interaction weights.

Emergence of (bi)multi-partiteness in networks having inhibitory and excitatory couplings

Abstract: (Bi)multi-partite interaction patterns are commonly observed in real world systems which have inhibitory and excitatory couplings. We hypothesize these structural interaction pattern to be stable and naturally arising in the course of evolution. We demonstrate that a random structure evolves to the (bi)multi-partite structure by imposing stability criterion through minimization of the largest eigenvalue in the genetic algorithm devised on the interacting units having inhibitory and excitatory couplings. The evolved interaction patterns are robust against changes in the initial network architecture as well as fluctuations in the interaction weights.