Soumen Roy

Resilience and rewiring of the passenger airline networks in the United States.

The air transportation network, a fundamental component of critical infrastructure, is formed from a collection of individual air carriers, each one with a methodically designed and engineered network structure. We analyze the individual structures of the seven largest passenger carriers in the USA and find that networks with dense interconnectivity, as quantified by large k cores for high values of k, are extremely resilient to both targeted removal of airports (nodes) and random removal of flight paths (edges). Such networks stay connected and incur minimal increase in an heuristic travel time despite removal of a majority of nodes or edges. Similar results are obtained for targeted removal based on either node degree or centrality. We introduce network rewiring schemes that boost resilience to different levels of perturbation while preserving total number of flight and gate requirements. Recent studies have focused on the asymptotic optimality of hub-and-spoke spatial networks under normal operating conditions, yet our results indicate that point-to-point architectures can be much more resilient to perturbations.

Modeling and verifying a broad array of network properties

Motivated by widely observed examples in nature, society and software, where groups of related nodes arrive together and attach to existing networks, we consider network growth via sequential attachment of linked node groups or graphlets. We analyze the simplest case, attachment of the three node -graphlet, where, with probability α, we attach a peripheral node of the graphlet, and with probability (1-α), we attach the central node. Our analytical results and simulations show that tuning α produces a wide range in degree distribution and degree assortativity, achieving assortativity values that capture a diverse set of many real-world systems. We introduce a fifteen-dimensional attribute vector derived from seven well-known network properties, which enables comprehensive comparison between any two networks. Principal Component Analysis of this attribute vector space shows a significantly larger coverage potential of real-world network properties by a simple extension of the above model when compared against a classic model of network growth.

Systems biology beyond degree, hubs and scale-free networks: the case for multiple metrics in complex networks

Modeling and topological analysis of networks in biological and other complex systems, must venture beyond the limited consideration of very few network metrics like degree, betweenness or assortativity. A proper identification of informative and redundant entities from many different metrics, using recently demonstrated techniques, is essential. A holistic comparison of networks and growth models is best achieved only with the use of such methods.

Strong associations between microbe phenotypes and their network architecture

Understanding the dependence and interplay between architecture and function in biological networks has great relevance to disease progression, biological fabrication, and biological systems in general. We propose methods to assess the association of various microbe characteristics and phenotypes with the topology of their networks. We adopt an automated approach to characterize metabolic networks of 32 microbial species using 11 topological metrics from complex networks. Clustering allows us to extract the indispensable, independent, and informative metrics. Using hierarchical linear modeling, we identify relevant subgroups of these metrics and establish that they associate with microbial phenotypes surprisingly well. This work can serve as a stepping stone to cataloging biologically relevant topological properties of networks and toward better modeling of phenotypes. The methods we use can also be applied to networks from other disciplines.

Dynamics of Mycobacteriophage-Mycobacterial Host Interaction: Evidence for Secondary Mechanisms for Host Lethality

ABSTRACT Mycobacteriophages infect mycobacteria, resulting in their death. Therefore, the possibility of using them as therapeutic agents against the deadly mycobacterial disease tuberculosis (TB) is of great interest. To obtain better insight into the dynamics of mycobacterial inactivation by mycobacteriophages, this study was initiated using mycobacteriophage D29 and Mycobacterium smegmatis as the phage-host system. Here, we implemented a goal-oriented iterative cycle of experiments on one hand and mathematical modeling combined with Monte Carlo simulations on the other. This integrative approach lends valuable insight into the detailed kinetics of bacterium-phage interactions. We measured time-dependent changes in host viability during the growth of phage D29 in M. smegmatis at different multiplicities of infection (MOI). The predictions emerging out of theoretical analyses were further examined using biochemical and cell biological assays. In a phage-host interaction system where multiple rounds of infection are allowed to take place, cell counts drop more rapidly than expected if cell lysis is considered the only mechanism for cell death. The phenomenon could be explained by considering a secondary factor for cell death in addition to lysis. Further investigations reveal that phage infection leads to the increased production of superoxide radicals, which appears to be the secondary factor. Therefore, mycobacteriophage D29 can function as an effective antimycobacterial agent, the killing potential of which may be amplified through secondary mechanisms.

Slow poisoning and destruction of networks: edge proximity and its implications for biological and infrastructure networks.

We propose a network metric, edge proximity, P(e), which demonstrates the importance of specific edges in a network, hitherto not captured by existing network metrics. The effects of removing edges with high P(e) might initially seem inconspicuous but are eventually shown to be very harmful for networks. Compared to existing strategies, the removal of edges by P(e) leads to a remarkable increase in the diameter and average shortest path length in undirected real and random networks till the first disconnection and well beyond. P(e) can be consistently used to rupture the network into two nearly equal parts, thus presenting a very potent strategy to greatly harm a network. Targeting by P(e) causes notable efficiency loss in U.S. and European power grid networks. P(e) identifies proteins with essential cellular functions in protein-protein interaction networks. It pinpoints regulatory neural connections and important portions of the neural and brain networks, respectively. Energy flow interactions identified by P(e) form the backbone of long food web chains. Finally, we scrutinize the potential of P(e) in edge controllability dynamics of directed networks.

Mapping networks of light–dark transition in LOV photoreceptors

MOTIVATION In optogenetics, designing modules of long or short signaling state lifetime is necessary for control over precise cellular events. A critical parameter for designing artificial or synthetic photoreceptors is the signaling state lifetime of photosensor modules. Design and engineering of biologically relevant artificial photoreceptors is based on signaling mechanisms characteristic of naturally occurring photoreceptors. Therefore identifying residues important for light-dark transition is a definite first step towards rational design of synthetic photoreceptors. A thorough grasp of detailed mechanisms of photo induced signaling process would be immensely helpful in understanding the behaviour of organisms. RESULTS Herein, we introduce the technique of differential networks. We identify key biological interactions, using light-oxygen-voltage domains of all organisms whose dark and light state crystal structures are simultaneously available. Even though structural differences between dark and light states are subtle (other than the covalent bond formation between flavin chromophore and active site Cysteine), our results successfully capture functionally relevant residues and are in complete agreement with experimental findings from literature. Additionally, using sequence-structure alignments, we predict functional significance of interactions found to be important from network perspective yet awaiting experimental validation. Our approach would not only help in minimizing extensive photo-cycle kinetics procedure but is also helpful in providing first-hand information on the fundamentals of photo-adaptation and rational design of synthetic photoreceptors in optogenetics. CONTACT devrani.dbs@presiuniv.ac.in or soumen@jcbose.ac.in SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.

Is small-world network disordered?

Recent renormalization group results predict non self averaging behaviour at criticality for relevant disorder. However, we find strong self averaging (SA) behaviour in the critical region of a quenched Ising model on an ensemble of small-world networks, despite the relevance of the random bonds at the pure critical point.

Giant coherence in driven systems

We study the noise induced currents and reliability or coherence of transport in two different classes of rocking ratchets. For this, we consider the motion of Brownian particles in the overdamped limit in both adiabatic and non-adiabatic regimes subjected to unbiased temporally symmetric and asymmetric periodic driving forces. In the case of a time asymmetric driving, we find that even in the presence of a spatially symmetric simple sinusoidal potential, highly coherent transport occurs. These ratchet systems exhibit giant coherence of transport in the regime of parameter space where unidirectional currents in the deterministic case are observed. Outside this parameter range, i.e., when current vanishes in the deterministic regime, coherence in transport is very low. The transport coherence decreases as a function of temperature and is a non-monotonic function of the amplitude of driving. The transport becomes unreliable as we go from the adiabatic to the non-adiabatic domain of operation.

Using complex networks towards information retrieval and diagnostics in multidimensional imaging

We present a fresh and broad yet simple approach towards information retrieval in general and diagnostics in particular by applying the theory of complex networks on multidimensional, dynamic images. We demonstrate a successful use of our method with the time series generated from high content thermal imaging videos of patients suffering from the aqueous deficient dry eye (ADDE) disease. Remarkably, network analyses of thermal imaging time series of contact lens users and patients upon whom Laser-Assisted in situ Keratomileusis (Lasik) surgery has been conducted, exhibit pronounced similarity with results obtained from ADDE patients. We also propose a general framework for the transformation of multidimensional images to networks for futuristic biometry. Our approach is general and scalable to other fluctuation-based devices where network parameters derived from fluctuations, act as effective discriminators and diagnostic markers.