Giovanni Scardoni

Analyzing biological network parameters with CentiScaPe

Summary: The increasing availability of large network datasets along with the progresses in experimental high-throughput technologies have prompted the need for tools allowing easy integration of experimental data with data derived form network computational analysis. In order to enrich experimental data with network topological parameters, we have developed the Cytoscape plug-in CentiScaPe. The plug-in computes several network centrality parameters and allows the user to analyze existing relationships between experimental data provided by the users and node centrality values computed by the plug-in. CentiScaPe allows identifying network nodes that are relevant from both experimental and topological viewpoints. CentiScaPe also provides a Boolean logic-based tool that allows easy characterization of nodes whose topological relevance depends on more than one centrality. Finally, different graphic outputs and the included description of biological significance for each computed centrality facilitate the analysis by the end users not expert in graph theory, thus allowing easy node categorization and experimental prioritization. Availability: CentiScaPe can be downloaded via the Cytoscape web site: http://chianti.ucsd.edu/cyto_web/plugins/index.php. Tutorial, centrality descriptions and example data are available at: http://profs.sci.univr.it/∼scardoni/centiscape/centiscapepage.php Contact: giovanni.scardoni@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online.

Centralities Based Analysis of Complex Networks

Characterizing, describing, and extracting information from a network is by now one of the main goals of science, since the study of network currently draws the attention of several fields of research, as biology, economics, social science, computer science and so on. The main goal is to analyze networks in order to extract their emergent properties (Bhalla & Iyengar (1999)) and to understand functionality of such complex systems. Two possible analysis approaches can be applied to a complex network: the first based on the study of its topological structure, the second based on the dynamic properties of the system described by the network itself. Since “always structure affects function” (Strogatz (2001)), the topological approach wants to understand networks functionality through the analysis of their structure. For instance, the topological structure of the road network affects critical traffic jam areas, the topology of social networks affects the spread of information and disease and the topology of the power grid affects the robustness and stability of power transmission. Remarkable results have been reached in the topological analysis of networks, concerning the study and characterization of networks structure, and even if far from being complete, several key notions have been introduced. These unifying principles underly the topology of networks belonging to different fields of science. Fundamental are the notions of scale-free network (Barabasi & Albert (1999); Jeong et al. (2000)), cluster (Newman (2006)), network motifs (Milo et al. (2002); Shen-Orr et al. (2002)), small-world property (Watts & Strogatz (1998); Watts (1999); Wagner & Fell (2001)) and centralities. Particularly, centralities have been initially applied to the field of social science (Freeman (1977)) and then to biological networks (Wuchty & Stadler (2003)). Usually, works regarding biological networks rightly consider global properties of the network and when centralities are used, they are often considered from a global point of view, as for example analyzing degree or centralities distribution (Jeong et al. (2000); Wagner & Fell (2001); Wuchty & Stadler (2003); Yamada & Bork (2009); Joy et al. (2005)). A node-oriented approach have been used analyzing attack tolerance of network, where consequences of central nodes deletion are studied (Albert et al. (2000); Crucitti et al. (2004)). But also in this case the analysis have been concentrate on global properties of the network and not on the relevance of the single nodes in the network. Similarly, available software for network analysis is usually oriented to global analysis and characterization of the whole networks. To identify relevant nodes of a biological network, protocols of analysis integrating centralities analysis and lab experimental data are needed and the same for software allowing this kind of analysis. Cytoscape is an excellent visualization and analysis tool with the analysis features greatly enhanced by plug-ins. Plug-in such as NetworkAnalyzer (Assenov et al. (2008)) computes some node centralities but does not allow direct integration with experimental data. Applications such as VisANT 16

Computational Identification of Phospho-Tyrosine Sub-Networks Related to Acanthocyte Generation in Neuroacanthocytosis

Acanthocytes, abnormal thorny red blood cells (RBC), are one of the biological hallmarks of neuroacanthocytosis syndromes (NA), a group of rare hereditary neurodegenerative disorders. Since RBCs are easily accessible, the study of acanthocytes in NA may provide insights into potential mechanisms of neurodegeneration. Previous studies have shown that changes in RBC membrane protein phosphorylation state affect RBC membrane mechanical stability and morphology. Here, we coupled tyrosine-phosphoproteomic analysis to topological network analysis. We aimed to predict signaling sub-networks possibly involved in the generation of acanthocytes in patients affected by the two core NA disorders, namely McLeod syndrome (MLS, XK-related, Xk protein) and chorea-acanthocytosis (ChAc, VPS13A-related, chorein protein). The experimentally determined phosphoproteomic data-sets allowed us to relate the subsequent network analysis to the pathogenetic background. To reduce the network complexity, we combined several algorithms of topological network analysis including cluster determination by shortest path analysis, protein categorization based on centrality indexes, along with annotation-based node filtering. We first identified XK- and VPS13A-related protein-protein interaction networks by identifying all the interactomic shortest paths linking Xk and chorein to the corresponding set of proteins whose tyrosine phosphorylation was altered in patients. These networks include the most likely paths of functional influence of Xk and chorein on phosphorylated proteins. We further refined the analysis by extracting restricted sets of highly interacting signaling proteins representing a common molecular background bridging the generation of acanthocytes in MLS and ChAc. The final analysis pointed to a novel, very restricted, signaling module of 14 highly interconnected kinases, whose alteration is possibly involved in generation of acanthocytes in MLS and ChAc.

Biological network analysis with CentiScaPe: centralities and experimental dataset integration.

The growing dimension and complexity of the available experimental data generating biological networks have increased the need for tools that help in categorizing nodes by their topological relevance. Here we present CentiScaPe, a Cytoscape app specifically designed to calculate centrality indexes used for the identification of the most important nodes in a network. CentiScaPe is a comprehensive suite of algorithms dedicated to network nodes centrality analysis, computing several centralities for undirected, directed and weighted networks. The results of the topological analysis can be integrated with data set from lab experiments, like expression or phosphorylation levels for each protein represented in the network. Our app opens new perspectives in the analysis of biological networks, since the integration of topological analysis with lab experimental data enhance the predictive power of the bioinformatics analysis.

Node interference and robustness: performing virtual knock-out experiments on biological networks: the case of leukocyte integrin activation network.

The increasing availability of large network datasets derived from high-throughput experiments requires the development of tools to extract relevant information from biological networks, and the development of computational methods capable of detecting qualitative and quantitative changes in the topological properties of biological networks is of critical relevance. We introduce the notions of node and as measures of the reciprocal influence between nodes within a network. We examine the theoretical significance of these new, centrality-based, measures by characterizing the topological relationships between nodes and groups of nodes. Node interference analysis allows topologically determining the context of functional influence of single nodes. Conversely, the node robustness analysis allows topologically identifying the nodes having the highest functional influence on a specific node. A new Cytoscape plug-in calculating these measures was developed and applied to a protein-protein interaction network specifically regulating integrin activation in human primary leukocytes. Notably, the functional effects of compounds inhibiting important protein kinases, such as SRC, HCK, FGR and JAK2, are predicted by the interference and robustness analysis, are in agreement with previous studies and are confirmed by laboratory experiments. The interference and robustness notions can be applied to a variety of different contexts, including, for instance, the identification of potential side effects of drugs or the characterization of the consequences of genes deletion, duplication or of proteins degradation, opening new perspectives in biological network analysis.

Protein tyrosine phosphatase receptor type γ is a JAK phosphatase and negatively regulates leukocyte integrin activation.

Regulation of signal transduction networks depends on protein kinase and phosphatase activities. Protein tyrosine kinases of the JAK family have been shown to regulate integrin affinity modulation by chemokines and mediated homing to secondary lymphoid organs of human T lymphocytes. However, the role of protein tyrosine phosphatases in leukocyte recruitment is still elusive. In this study, we address this issue by focusing on protein tyrosine phosphatase receptor type γ (PTPRG), a tyrosine phosphatase highly expressed in human primary monocytes. We developed a novel methodology to study the signaling role of receptor type tyrosine phosphatases and found that activated PTPRG blocks chemoattractant-induced β2 integrin activation. Specifically, triggering of LFA-1 to high-affinity state is prevented by PTPRG activation. High-throughput phosphoproteomics and computational analyses show that PTPRG activation affects the phosphorylation state of at least 31 signaling proteins. Deeper examination shows that JAKs are critically involved in integrin-mediated monocyte adhesion and that PTPRG activation leads to JAK2 dephosphorylation on the critical 1007–1008 phosphotyrosine residues, implying JAK2 inhibition and thus explaining the antiadhesive role of PTPRG. Overall, the data validate a new approach to study receptor tyrosine phosphatases and show that, by targeting JAKs, PTPRG downmodulates the rapid activation of integrin affinity in human monocytes, thus emerging as a potential novel critical regulator of leukocyte trafficking.

An Approach to Investigate Intracellular Protein Network Responses

Modern toxicological evaluations have evolved to consider toxicity as a perturbation of biological pathways or networks. As such, toxicity testing approaches are shifting from common end point evaluations to pathway based approaches, where the degree of perturbation of select biological pathways is monitored. These new approaches are greatly increasing the data available to toxicologists, but methods of analyses to determine the inter-relationships between potentially affected pathways are needed to fully understand the consequences of exposure. An approach to construct dose-response curves that use graph theory to describe network perturbations among three disparate mitogen-activated protein kinase (MAPK) pathways is presented. Mitochondrial stress was induced in human hepatocytes (HepG2) by exposing the cells to increasing doses of the complex I inhibitor, deguelin. The relative phosphorylation responses of proteins involved in the regulation of the stress response were measured. Graph theory was applied to the phosphorylation data to obtain parameters describing the network perturbations at each individual dose tested. The graph theory results depicted the dynamic nature of the relationship between p38, JNK, and ERK1/2 under conditions of mitochondrial stress and revealed shifts in the relationships between these MAPK pathways at low doses. The inter-relationship, or crosstalk, among these 3 traditionally linear MAPK cascades was further probed by coexposing cells to deguelin plus SB202190 (JNK and p38 inhibitor) or deguelin plus SB202474 (JNK inhibitor). The cells exposed to deguelin plus SB202474 resulted in significantly decreased viability, which could be visualized and attributed to the decrease of ERK1/2 network centrality. The approach presented here allows for the construction and visualization of dose-response curves that describe network perturbations induced by chemical stress, which provides an informative and sensitive means of assessing toxicological effects on biological systems.

Creating, generating and comparing random network models with NetworkRandomizer

Biological networks are becoming a fundamental tool for the investigation of high-throughput data in several fields of biology and biotechnology. With the increasing amount of information, network-based models are gaining more and more interest and new techniques are required in order to mine the information and to validate the results. To fill the validation gap we present an app, for the Cytoscape platform, which aims at creating randomised networks and randomising existing, real networks. Since there is a lack of tools that allow performing such operations, our app aims at enabling researchers to exploit different, well known random network models that could be used as a benchmark for validating real, biological datasets. We also propose a novel methodology for creating random weighted networks, i.e. the multiplication algorithm, starting from real, quantitative data. Finally, the app provides a statistical tool that compares real versus randomly computed attributes, in order to validate the numerical findings. In summary, our app aims at creating a standardised methodology for the validation of the results in the context of the Cytoscape platform.Biological networks are becoming a fundamental tool for the investigation of high-throughput data in several fields of biology and biotechnology. With the increasing amount of information, network-based models are gaining more and more interest and new techniques are required in order to mine the information and to validate the results. To fill the validation gap we present an app, for the Cytoscape platform, which aims at creating randomised networks and randomising existing, real networks. Since there is a lack of tools that allow performing such operations, our app aims at enabling researchers to exploit different, well known random network models that could be used as a benchmark for validating real, biological datasets. We also propose a novel methodology for creating random weighted networks, i.e. the multiplication algorithm, starting from real, quantitative data. Finally, the app provides a statistical tool that compares real versus randomly computed attributes, in order to validate the numerical findings. In summary, our app aims at creating a standardised methodology for the validation of the results in the context of the Cytoscape platform.

Finding the shortest path with PesCa: a tool for network reconstruction

Network analysis is of growing interest in several fields ranging from economics to biology. Several methods have been developed to investigate different properties of physical networks abstracted as graphs, including quantification of specific topological properties, contextual data enrichment, simulation of pathway dynamics and visual representation. In this context, the PesCa app for the Cytoscape network analysis environment is specifically designed to help researchers infer and manipulate networks based on the shortest path principle. PesCa offers different algorithms allowing network reconstruction and analysis starting from a list of genes, proteins and in general a set of interconnected nodes. The app is useful in the early stage of network analysis, i.e. to create networks or generate clusters based on shortest path computation, but can also help further investigations and, in general, it is suitable for every situation requiring the connection of a set of nodes that apparently do not share links, such as isolated nodes in sub-networks. Overall, the plugin enhances the ability of discovering interesting and not obvious relations between high dimensional sets of interacting objects.

Dynamic Modeling and Simulation of Leukocyte Integrin Activation through an Electronic Design Automation Framework

Model development and analysis of biological systems is recognized as a key requirement for integrating in-vitro and in-vivo experimental data. In-silico simulations of a biochemical model allows one to test different experimental conditions, helping in the discovery of the dynamics that regulate the system. Several characteristics and issues of biological system modeling are common to the electronics system modeling, such as concurrency, reactivity, abstraction levels, as well as state space explosion during verification. This paper proposes a modeling and simulation framework for discrete event-based execution of biochemical systems based on SystemC. SystemC is the reference language in the electronic design automation (EDA) field for modeling and verifying complex systems at different abstraction levels. SystemC-based verification is the de-facto an alternative to model checking when such a formal verification technique cannot deal with the state space complexity of the model. The paper presents how the framework has been applied to model the intracellular signalling network controlling integrin activation mediating leukocyte recruitment from the blood into the tissues, by handling the solution space complexity through different levels of simulation accuracy.