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Complex Networks in Biomedical Sciences
 

Complex Networks in Biomedical Sciences

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  • A recent study reported that among people who carried a single copy of the high-risk allele for the FTO gene, which is associated with fat mass and obesity, the risk of obesity increased by 30%. The risk of obesity increased by 67% among people who carried two alleles, and on average they gained 3.0 kg (6.6 lb) or more. 1 Given that approximately one sixth of the population of European descent is homozygous for this allele, this link between the FTO gene and obesity appears to be one of the strongest genotype–phenotype associations detected by modern genome-screening techniques. To understand various disease mechanisms, it is not sufficient to know the precise list of “disease genes”; instead, we should try to map out the detailed wiring diagram of the various cellular components that are influenced by these genes and gene products. Such network-based thinking has already provided insights into the pathogenesis of several diseases. For example, a recent study suggested that 18 of the 23 genes known to be associated with ataxia are part of a highly interlinked subnetwork 5 ; in another example, a reverse-engineered subnetwork indicated that the androgen-receptor gene might be used to detect the aggressiveness of primary prostate cancer. 6
  • BMC Syst Biol. 2011 Jan 21;5:13. When the human viral infectome and diseasome networks collide: towards a systems biology platform for the aetiology of human diseases. Navratil V , de Chassey B , Combe CR , Lotteau V . Source Université de Lyon, IFR128 BioSciences Lyon-Gerland, Lyon 69007, France. navratil@prabi.fr Abstract BACKGROUND: Comprehensive understanding of molecular mechanisms underlying viral infection is a major challenge towards the discovery of new antiviral drugs and susceptibility factors of human diseases. New advances in the field are expected from systems-level modelling and integration of the incessant torrent of high-throughput "-omics" data. RESULTS: Here, we describe the Human Infectome protein interaction Network, a novel systems virology model of a virtual virus-infected human cell concerning 110 viruses. This in silico model was applied to comprehensively explore the molecular relationships between viruses and their associated diseases. This was done by merging virus-host and host-host physical protein-protein interactomes with the set of genes essential for viral replication and involved in human genetic diseases. This systems-level approach provides strong evidence that viral proteomes target a wide range of functional and inter-connected modules of proteins as well as highly central and bridging proteins within the human interactome. The high centrality of targeted proteins was correlated to their essentiality for viruses' lifecycle, using functional genomic RNAi data. A stealth-attack of viruses on proteins bridging cellular functions was demonstrated by simulation of cellular network perturbations, a property that could be essential in the molecular aetiology of some human diseases. Networking the Human Infectome and Diseasome unravels the connectivity of viruses to a wide range of diseases and profiled molecular basis of Hepatitis C Virus-induced diseases as well as 38 new candidate genetic predisposition factors involved in type 1 diabetes mellitus. CONCLUSIONS: The Human Infectome and Diseasome Networks described here provide a unique gateway towards the comprehensive modelling and analysis of the systems level properties associated to viral infection as well as candidate genes potentially involved in the molecular aetiology of human diseases.
  • Reference: Wireless body area networks for healthcare: the MobiHealth project. van Halteren A, Bults R, Wac K, Dokovsky N, Koprinkov G, Widya I, Konstantas D, Jones V, Herzog R. Stud Health Technol Inform. 2004;108:181-93. Abstract: The forthcoming wide availability of high bandwidth public wireless networks will give rise to new mobile health care services. Towards this direction the MobiHealth project has developed and trialed a highly customisable vital signals' monitoring system based on a Body Area Network (BAN) and an m-health service platform utilizing next generation public wireless networks. The developed system allows the incorporation of diverse medical sensors via wireless connections, and the live transmission of the measured vital signals over public wireless networks to healthcare providers. Nine trials with different health care cases and patient groups in four different European countries have been conducted to test and verify the system, the service and the network infrastructure for its suitability and the restrictions it imposes to mobile health care applications. Main Affiliation: University of Twente, EWI/CTIT, P.O. Box 217, NL-7500 AE Enschede, The Netherlands. WorkLink: http://www.ncbi.nlm.nih.gov/pubmed/15718645 MobiHealth: http://www.mobihealth.org/
  • Reference: Mobile communications for monitoring heart disease and diabetes. Mulvaney D, Woodward B, Datta S, Harvey P, Vyas A, Thakker B, Farooq O. Conf Proc IEEE Eng Med Biol Soc. 2010;2010:2208-10. Abstract: This paper describes a practical development project to enable the monitoring of vital signs data obtained from patients located in remote rural locations. The data are gathered from a wireless network of sensors attached to a patient's body and stored locally for secure transmission over existing communication infrastructures to a hospital server. Clinicians are then able to monitor the patient offline and upload diagnoses. Main Affiliation: Department of Electronic and Electrical Engineering, Loughborough University LE11 3TU, UK. Email: d.j.mulvaney@lboro.ac.uk Link: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5627117
  • Arie Meir and Boris Rubinsky. Distributed Network, Wireless and Cloud Computing Enabled 3-D Ultrasound; a New Medical Technology Paradigm. PLoS One. 2009; 4(11): e7974. Published online 2009 November 19. doi: 10.1371/journal.pone.0007974 Abstract: Medical technologies are indispensable to modern medicine. However, they have become exceedingly expensive and complex and are not available to the economically disadvantaged majority of the world population in underdeveloped as well as developed parts of the world. For example, according to the World Health Organization about two thirds of the world population does not have access to medical imaging. In this paper we introduce a new medical technology paradigm centered on wireless technology and cloud computing that was designed to overcome the problems of increasing health technology costs. We demonstrate the value of the concept with an example; the design of a wireless, distributed network and central (cloud) computing enabled three-dimensional (3-D) ultrasound system. Specifically, we demonstrate the feasibility of producing a 3-D high end ultrasound scan at a central computing facility using the raw data acquired at the remote patient site with an inexpensive low end ultrasound transducer designed for 2-D, through a mobile device and wireless connection link between them. Producing high-end 3D ultrasound images with simple low-end transducers reduces the cost of imaging by orders of magnitude. It also removes the requirement of having a highly trained imaging expert at the patient site, since the need for hand-eye coordination and the ability to reconstruct a 3-D mental image from 2-D scans, which is a necessity for high quality ultrasound imaging, is eliminated. This could enable relatively untrained medical workers in developing nations to administer imaging and a more accurate diagnosis, effectively saving the lives of people. Link: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2775631/?tool=pubmed
  • Connectionism is a set of approaches in the fields of artificial intelligence, cognitive psychology, cognitive science, neuroscience and philosophy of mind, that models mental or behavioral phenomena as the emergent processes of interconnected networks of simple units . There are many forms of connectionism, but the most common forms use neural network models.
  • Ref. : The Anatomical Distance of Functional Connections Predicts Brain Network Topology in Health and Schizophrenia Aaron F. Alexander-Bloch , Petra E. Vértes , Reva Stidd , François Lalonde , Liv Clasen , Judith Rapoport , Jay Giedd , Edward T. Bullmore and Nitin Gogtay . Cereb. Cortex (2012) doi:10.1093/cercor/bhr388; l ink: http://cercor.oxfordjournals.org/content/early/2012/01/23/cercor.bhr388.abstract ABSTRACT: Abstract The human brain is a topologically complex network embedded in anatomical space. Here, we systematically explored relationships between functional connectivity, complex network topology, and anatomical (Euclidean) distance between connected brain regions, in the resting-state functional magnetic resonance imaging brain networks of 20 healthy volunteers and 19 patients with childhood-onset schizophrenia (COS). Normal between-subject differences in average distance of connected edges in brain graphs were strongly associated with variation in topological properties of functional networks. In addition, a club or subset of connector hubs was identified, in lateral temporal, parietal, dorsal prefrontal, and medial prefrontal/cingulate cortical regions. In COS, there was reduced strength of functional connectivity over short distances especially, and therefore, global mean connection distance of thresholded graphs was significantly greater than normal. As predicted from relationships between spatial and topological properties of normal networks, this disorder-related proportional increase in connection distance was associated with reduced clustering and modularity and increased global efficiency of COS networks. Between-group differences in connection distance were localized specifically to connector hubs of multimodal association cortex. In relation to the neurodevelopmental pathogenesis of schizophrenia, we argue that the data are consistent with the interpretation that spatial and topological disturbances of functional network organization could arise from excessive “pruning” of short-distance functional connections in schizophrenia.
  • ANN: Mathematically, a neuron's network function is defined as a composition of other functions , which can further be defined as a composition of other functions. This can be conveniently represented as a network structure, with arrows depicting the dependencies between variables. A widely used type of composition is the nonlinear weighted sum , where functions f(x) = K(SUM i (w i g i (x)), where K (commonly referred to as the activation function. An ANN is typically defined by three types of parameters: - The interconnection pattern between different layers of neurons - The learning process for updating the weights of the interconnections - The activation function that converts a neuron's weighted input to its output activation. Ref: Bishop, C.M. (1995) Neural Networks for Pattern Recognition , Oxford: Oxford University Press. ISBN 0-19-853849-9 Wiki: http://en.wikipedia.org/wiki/Artificial_neural_network
  • J Theor Biol. 2011 May 7;276(1):229-49. Epub 2011 Jan 26. NL MIND-BEST: a web server for ligands and proteins discovery--theoretic-experimental study of proteins of Giardia lamblia and new compounds active against Plasmodium falciparum. González-Díaz H , Prado-Prado F , Sobarzo-Sánchez E , Haddad M , Maurel Chevalley S , Valentin A , Quetin-Leclercq J , Dea-Ayuela MA , Teresa Gomez-Muños M , Munteanu CR , José Torres-Labandeira J , García-Mera X , Tapia RA , Ubeira FM . Source: Department of Microbiology and Parasitology, University of Santiago de Compostela (USC), Galicia, Spain. gonzalezdiazh@yahoo.es Abstract There are many protein ligands and/or drugs described with very different affinity to a large number of target proteins or receptors. In this work, we selected Ligands or Drug-target pairs (DTPs/nDTPs) of drugs with high affinity/non-affinity for different targets. Quantitative Structure-Activity Relationships (QSAR) models become a very useful tool in this context to substantially reduce time and resources consuming experiments. Unfortunately most QSAR models predict activity against only one protein target and/or have not been implemented in the form of public web server freely accessible online to the scientific community. To solve this problem, we developed here a multi-target QSAR (mt-QSAR) classifier using the MARCH-INSIDE technique to calculate structural parameters of drug and target plus one Artificial Neuronal Network (ANN) to seek the model . The best ANN model found is a Multi-Layer Perceptron (MLP) with profile MLP 20:20-15-1:1. This MLP classifies correctly 611 out of 678 DTPs (sensitivity=90.12%) and 3083 out of 3408 nDTPs (specificity=90.46%), corresponding to training accuracy=90.41%. The validation of the model was carried out by means of external predicting series. The model classifies correctly 310 out of 338 DTPs (sensitivity=91.72%) and 1527 out of 1674 nDTP (specificity=91.22%) in validation series, corresponding to total accuracy=91.30% for validation series (predictability). This model favorably compares with other ANN models developed in this work and Machine Learning classifiers published before to address the same problem in different aspects. We implemented the present model at web portal Bio-AIMS in the form of an online server called: Non-Linear MARCH-INSIDE Nested Drug-Bank Exploration & Screening Tool (NL MIND-BEST), which is located at URL: http://miaja.tic.udc.es/Bio-AIMS/NL-MIND-BEST.php. This online tool is based on PHP/HTML/Python and MARCH-INSIDE routines. Finally we illustrated two practical uses of this server with two different experiments. In experiment 1 , we report by first time Quantum QSAR study, synthesis, characterization, and experimental assay of antiplasmodial and cytotoxic activities of oxoisoaporphine alkaloids derivatives as well as NL MIND-BEST prediction of potential target proteins. In experiment 2 , we report sampling, parasite culture, sample preparation, 2-DE, MALDI-TOF, and -TOF/TOF MS, MASCOT search, MM/MD 3D structure modeling, and NL MIND-BEST prediction for different peptides a new protein of the found in the proteome of the human parasite Giardia lamblia, which is promising for anti-parasite drug-targets discovery. Copyright © 2011 Elsevier Ltd. All rights reserved.
  • ANN use to predict drug targets NL MIND-BEST, steps: Train network, Validate network with external series, Implement web server, (iv) Use to predict new drugs or protein targets. Refrences: LDA: H González-Díaz*, et al. Journal of Theoretical Biology, 276(1):229-249 ( 2011 ). ANN: González-Díaz H; Journal of Proteome Research, 10(4), 1698-718 ( 2011 ). Link: http://www.ncbi.nlm.nih.gov/pubmed/21277861
  • Betweenness is a centrality measure of a vertex within a graph . The betweenes of a vertex v in a graph G : = ( V , E ) with V vertices is computed as follows: 1. For each pair of vertices ( s , t ), compute the shortest paths between them. 2. For each pair of vertices ( s , t ), determine the fraction of shortest paths that pass through the vertex in question (here, vertex v ). 3. Sum this fraction over all pairs of vertices ( s , t ). More compactly the betweenness can be represented as where σ st is total number of shortest paths from node s to node t and σ st ( v ) is the number of those paths that pass through v . It was introduced as a measure for quantifying the control of a human on the communication between other humans in a social network by Linton Freeman . [9] In his conception, vertices that have a high probability to occur on a randomly chosen shortest path between two randomly chosen nodes have a high betweenness. See wiki: http://en.wikipedia.org/wiki/Centrality#Betweenness_centrality
  • FIGURE: Map of protein–protein interactions. The largest cluster, which contains 78% of all proteins, is shown. The colour of a node signifies the phenotypic effect of removing the corresponding protein (red, lethal; green, non-lethal; orange, slow growth; yellow, unknown). b , Connectivity distribution p ( k ) of interacting yeast proteins, giving the probability that a given protein interacts with k other proteins. The exponential cut-off 6 indicates that the number of proteins with more than 20 interactions is slightly less than expected for pure scale-free networks. In the absence of data on the link directions, all interactions have been considered as bidirectional. The parameter controlling the short-length scale correction has value k 0 1. c , The fraction of essential proteins with exactly k links versus their connectivity, k , in the yeast proteome. The list of 1,572 mutants with known phenotypic profile was obtained from the Proteome database 13 . Detailed statistical analysis, including r = 0.75 for Pearson's linear correlation coefficient, demonstrates a positive correlation between lethality and connectivity. For additional details, see http://www.nd.edu/~networks/cell .
  • Summary: Network Workbench: A Large-Scale Network Analysis, Modeling and Visualization Toolkit for Biomedical, Social Science and Physics Research.This project will design, evaluate, and operate a unique distributed, shared resources environment for large-scale network analysis, modeling, and visualization, named Network Workbench (NWB). The envisioned data-code-computing resources environment will provide .. more How to cite this project News & Updates 3.24.2011 NWB Tool utilized in Dr. Kristie Holmes' presentation, " Social Network Analysis: Using bibliographic data to construct co-author networks " at the University of Florida. 9.7.2010 NWB Tool utilized in INFO I400/H400 taught by Alex Vespignani, Filippo Menczer and Michael Conover (AI) 4.29.2010 Network Workbench Tool used in IU Undergraduate Course on Network Science taught by Alex Vespignani & Fil Menczer (Fall 2010) 4.29.2010 NWB Tool Featured in the 2010 ICPSR Summer Program by Stanley Wasserman, Ann McCranie, and Katherine Faust (Summer 2010) 3.29.10 Katy Börner presents Plug-and-Play Macroscopes Tutorial at 2010 International Conference on Social Computing, Behavioral Modeling and Prediction , Bethesda, MD. 9.15.2009 NWB 1.0.0 Official Release & Notes 5.1.09 Kaelble, Steve. 2009. Mapping the Future of Knowledge . Research & Creative Activity , 31, 2: 12-15. ( website accessed 5/1/09) 3.23.09 1.0.0 beta 5 Released 1.23.09 Ann Mcranie's tutorial abstract for Sunbelt 2009
  • Collation of Connectivity data on the Macaque brain (CoCo- Mac), a seminal contribution to neuroinformatics, is a publicly available database (20–22). Conscientiously and meticulously, the database curators have collated and annotated information on over 2,500 anatomical tracer injections from over 400 published experimental studies. CoCoMac is an objective, coordinate-independent collection of annotations that captures two relationships between pairs of brain regions, where each brain region refers to cortical and subcortical subdivisions as well as to combinations of such subdivisions into sulci, gyri, and other large ensembles. The first relationship is connectivity—whether a brain region in one study projects to another region in (possibly) a different study. There are 10,681 connectivity relations.† The second relationship is mapping—whether a brain region in one study is identical to, a substructure of, or a suprastructure of another region in (possibly) a different study. There are 16,712 mapping relations. Unfortunately, because of a multiplicity of brain maps, divergent nomenclature, boundary uncertainty, and differing resolutions in different studies, mapping relations are often conflicting and connectivity information is typically scattered across related brain regions.
  • New Markov-Shannon Entropy models to assess connectivity quality in complex networks: From molecular to cellular pathway, Parasite-Host, Neural, Industry, and Legal-Social networks. Riera-Fernández P , Munteanu CR , Escobar M , Prado-Prado F , Martín-Romalde R , Pereira D , Villalba K , Duardo-Sánchez A , González-Díaz H . J Theor Biol. 2012 Jan 21;293:174-88. Epub 2011 Oct 25. Source: Department of Microbiology & Parasitology, Faculty of Pharmacy, University of Santiago de Compostela (USC), 15782 Santiago de Compostela, Spain. Abstract: Graph and Complex Network theory is expanding its application to different levels of matter organization such as molecular, biological, technological, and social networks. A network is a set of items, usually called nodes, with connections between them, which are called links or edges. There are many different experimental and/or theoretical methods to assign node-node links depending on the type of network we want to construct. Unfortunately, the use of a method for experimental reevaluation of the entire network is very expensive in terms of time and resources; thus the development of cheaper theoretical methods is of major importance. In addition, different methods to link nodes in the same type of network are not totally accurate in such a way that they do not always coincide. In this sense, the development of computational methods useful to evaluate connectivity quality in complex networks (a posteriori of network assemble) is a goal of major interest. In this work, we report for the first time a new method to calculate numerical quality scores S(L(ij)) for network links L(ij) (connectivity) based on the Markov-Shannon Entropy indices of order k-th (θ(k)) for network nodes. The algorithm may be summarized as follows: (i) first, the θ(k)(j) values are calculated for all j-th nodes in a complex network already constructed; (ii) A Linear Discriminant Analysis (LDA) is used to seek a linear equation that discriminates connected or linked (L(ij)=1) pairs of nodes experimentally confirmed from non-linked ones (L(ij)=0); (iii) the new model is validated with external series of pairs of nodes; (iv) the equation obtained is used to re-evaluate the connectivity quality of the network, connecting/disconnecting nodes based on the quality scores calculated with the new connectivity function. This method was used to study different types of large networks. The linear models obtained produced the following results in terms of overall accuracy for network reconstruction: Metabolic networks (72.3%), Parasite-Host networks (93.3%), CoCoMac brain cortex co-activation network (89.6%), NW Spain fasciolosis spreading network (97.2%), Spanish financial law network (89.9%) and World trade network for Intelligent & Active Food Packaging (92.8%). In order to seek these models, we studied an average of 55,388 pairs of nodes in each model and a total of 332,326 pairs of nodes in all models. Finally, this method was used to solve a more complicated problem. A model was developed to score the connectivity quality in the Drug-Target network of US FDA approved drugs. In this last model the θ(k) values were calculated for three types of molecular networks representing different levels of organization: drug molecular graphs (atom-atom bonds), protein residue networks (amino acid interactions), and drug-target network (compound-protein binding). The overall accuracy of this model was 76.3%. This work opens a new door to the computational reevaluation of network connectivity quality (collation) for complex systems in molecular, biomedical, technological, and legal-social sciences as well as in world trade and industry.
  • Figure. 2. Plots of a model network and reachable sub-graphs. A: the largest component of a contact network over five years. B: a large reachable subgraph initiated from a node with degree five, with duration of infectiousness 100 weeks. Of the sub-graphs calculated, 48% contained at least 2000 nodes and 47% contained no more than 20 nodes, leaving only 5% in the range 20–2000. C: a small reachable sub-graph showing the increase in size with increasing duration of infectiousness. Infection starts at the red node; nodes of the same colour are at the same number of links from the starting point. Plots were created using Batagelj and Mrvar (1998). The degree of the originating node is 3. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)
  • Figure 1. Geographical maps of Galicia showing the location of the 275 sampled farms. A - The status of infection (empty circles: F. hepatica free and filled circles: F. hepatica infected) and the treatment administered on each farm are shown (blue: none; red: an anthelmintic effective against fluke mature stages and green: a fasciolicide effective against immature and mature stages). B - Distribution of farms according to the presence of F. hepatica infection (grey: uninfected; cyan: infected with a within-herd prevalence <25% and pink: infected with a within-herd prevalence ≥ 25%). C - Network for landscape parasite-spreading.
  • For all networks, except the WS model network, the Out (chained and unchained versions) is most efficient. Within this group the order of efficiency varies: For the arxiv network the Out strategy is more than twice as efficient as any other in the interval 0 .25 f 0.4. Another interesting observation is that, even if the degree distribution is narrow (such as for the seceder model), the more elaborate strategies were much more efficient than random vaccination. This is especially clear for higher f which suggests that the structural change of the network of susceptible vertices during the vaccination procedure is an important factor for the overall efficiency. For the WS model network, the chained algorithms performed poorer than random vaccination. This is in contrast to all other networks. We conclude that epidemiology-related results regarding the WS model networks should be cautiously generalized to real-world systems.
  • RND: vaccine nodes at random, DEG & CLO: vaccine nodes in decreasing order of node degree or clossenes.

Complex Networks in Biomedical Sciences Complex Networks in Biomedical Sciences Presentation Transcript

  • Complex Networks in Bio-Medical Sciences: From Chemical Structure of Drugs to Neuroinformatics, Telemedicine or Epidemiology Author: González-Díaz H., PhD, [email_address] Address: Faculty of Pharmacy, USC. Issue: Medical Bioinformatics, Degree in Medicine, Fac. Of Medicine, USC. Link: http://www.researchgate.net/profile/Humberto_Gonzalez-Diaz/
  • Problema del puente de Königsberg; cuna del filósofo I. Kant (1724-1804). Ilustración tomada de la publicación original de Leonhard Euler. Königsberg Bridges ( Los Puentes de Königsberg) González-Díaz H, Complex Networks in Bio-Medical Sciences
  • A Brief Review of Some Basic Networks concepts
    • A network is described as a graph i.e. , a pair of set G = (V,E). V is the set of vertices (or, as otherwise called, nodes ). E is the set of the edges. A graph can be completely described by an adjacency matrix A .The elements a ij of A are defined as follows: if there is an edge between the nodes i and j then a ij = 1; otherwise a ij = 0.
    • Un grafo G , se denota G = (V, A) , consiste en dos conjuntos V y A tales que V ≠ Ø y A es un conjunto de pares de elementos de V . Los elementos de V son los vértices o nodos del grafo C mientras que los elementos de A son sus aristas o conexiones.
    González-Díaz H, Complex Networks in Bio-Medical Sciences
  • A : Non-directed graph (Grafo no dirigido), v := nodes ( vértice s), e or a := edges ( aristas ). B : Pairwise afjacency relationships ( Lista de adyacencia ). C : Adjacency Matrix ( Matriz de adyacencia ). González-Díaz H, Complex Networks in Bio-Medical Sciences
  • Molecular Graphs & Residue Networks
    • Chemical Structure Networks of Drugs and/or Therapeutic Targets:
    • Drugs: Molecular Graphs => V := atoms, E:= chemical bonds.
    • Proteins: 3D Residue contact networks => V := aminoacids, E:= spatial contacts
    • RNAs: Secondary structure graphs => V := nucleotides, E:= self-folding hydrogen bonds
    González-Díaz H, Complex Networks in Bio-Medical Sciences
  • Molecular Biology Networks
    • Molecular Biology networks:
    • Gene / Proteins Interaction Networks (PINs) => V := genome or proteome (set of gene or proteins in a patient, e.g.) and E := regulation/interactions among them.
    • Metabolic Pathways => V := metabolome (set of metabolites), E := reactome (set of metabolic reactions)
    González-Díaz H, Complex Networks in Bio-Medical Sciences FIGURE: Human full metabolic network, from KEGG, link: http://www.genome.jp/kegg/pathway.html
  • Diseasome networks
    • Diseasomes & Human-Pathogens Biological networks:
    • Pathogen-Pathogen networks : Bacteria Co-aggregation Networks => V:= bateria species; E:= specie-specie co-aggregation.
    • Host-Pathogen networks =>
    • V := hosts, parasites, viruses; E := Pairwise parasitism. Commonly represented as Bipartite networks with two sets => Parasites := Vp := {v1, v2, v3); Hosts := Vh := {v, v5, v6}
    • Diseasome networks : Disease-Protein, Disease-Gene, Disease-Drug, Disease-Disease, … etc. V:= Diseases, Drugs, Proteins, Gene; E:= set of relationships between diseases and other elements
    González-Díaz H, Complex Networks in Bio-Medical Sciences
  • Networks Scales: Genome => Proteome => Metabolome => Diseasome => Social Network s
    • AL Barabási, Network Medicine — From Obesity to the “Diseasome”. N Engl J Med 2007.
    • FTO gene, associated with fat mass and obesity, risk of obesity increased by 30% and . 67% among people who carried two alleles.
    • This link between the FTO gene and obesity appears to be one of the strongest genotype–phenotype associations detected by modern genome-screening techniques.
    González-Díaz H, Complex Networks in Bio-Medical Sciences
  • Human Infectome Networks
    • The Hepatitis C Virus Infectome Diseasome Network: (a) viral proteins (red circles), host cellular proteins (blue circle), and diseases (black)
    • The Infectome-Autoimmune Diseasome Network . The Infectome-Autoimmune Diseasome Network is modelled as a multi-coloured graph with two types of nodes (diseases - black circles and host cellular proteins - blue square). Protein-protein interactions between host cellular proteins are represented by blue edges. Disease-gene associations are represented by black edges.
    González-Díaz H, Complex Networks in Bio-Medical Sciences
  • Networks in Telemedicine
    • Medical Communications & Technological Networks :
    • The Internet ( V := web pages and E := hyperlinks).
    • TeleMedicine & Communication networks => V := Pateients and E := sensor-to-processor medical information transmission.
    • The MoviHealth project. Body Area Network (BAN) and an health service platform with public wireless networks
    González-Díaz H, Complex Networks in Bio-Medical Sciences Wireless body area networks for healthcare: the MobiHealth project. A van Halteren, et al R. Stud Health Technol Inform. 2004;108:181-93 .
  • Body Area Networks? González-Díaz H, Complex Networks in Bio-Medical Sciences
    • Body Area Networks
    • Monitoring of vital signs data obtained from patients located in remote rural locations
    • Data are gathered from a wireless network of sensors attached to a patient's body and stored locally for secure transmission over existing communication infrastructures to a hospital server
    Mulvaney D. et al. Mobile communications for monitoring heart disease and diabetes. Conf Proc IEEE Eng Med Biol Soc. 2010;2010:2208-10. Link: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5627117
  • New Medical Technology Paradigm?
    • Medical tech. are not available to the economically disadvantaged world populatio
    • Wireless technology + cloud computing 3D ultrasound system = lower cost.
    • Cloud Computing???
    González-Díaz H, Complex Networks in Bio-Medical Sciences A Meir & B Rubinsky. Distributed Network, Wireless and Cloud Computing Enabled 3-D Ultrasound; a New Medical Technology Paradigm. PLoS One. 2009; 4(11): e7974. doi: 10.1371/journal.pone.0007974 Link: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2775631/?tool=pubmed
  • Cloud Computing = Computación en la Nube ?
    • Cloud computing providers deliver applications via the internet, which are accessed from web browsers and desktop and mobile apps, while the business software and data are stored on servers at a remote location.
    • In some cases, applications are delivered via a screen-sharing technology, while the computing resources are consolidated at a remote data centre location
    • It is a byproduct and consequence of the ease-of-access to remote computing sites provided by the Internet.
    González-Díaz H, Complex Networks in Bio-Medical Sciences Wiki: http://en.wikipedia.org/wiki/Cloud_computing
  • Connectionism, Neurosciences & NeuroInformatics
    • Neuronal Networks:
    • V:= neurons,
    • E:= synapses.
    • FIGURE:
    • From "Texture of the
    • Nervous System of Man
    • and the Vertebrates"
    • by Santiago Ramón y Cajal .
    • The figure illustrates the
    • diversity of neuronal
    • morphologies in the
    • auditory cortex.
    González-Díaz H, Complex Networks in Bio-Medical Sciences
    • Cerebral cortex networks : V:= Cerebral cortex regions, E:= pair-wise region co-activation .
    • FIGURE comes from: Hagmann P, et al. Mapping the Structural Core of Human Cerebral Cortex. PLoS Biol (2008) 6(7): e159. Link: http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0060159
    Connectionism, Neurosciences & NeuroInformatics “ The human brain is a topologically complex network embedded in anatomical space”…, see: A.F. Alexander-Bloch et al ., The Anatomical Distance of Functional Connections Predicts Brain Network Topology in Health and Schizophrenia. Cereb. Cortex (2012) doi:10.1093/cercor/bhr388 Study: Explored relationships between functional connectivity, complex network topology , and anatomical distance (Euclidean) between connected brain regions , in the resting-state fMRI brain networks of 20 healthy volunteers and 19 patients with childhood-onset schizophrenia (COS). González-Díaz H, Complex Networks in Bio-Medical Sciences
  • Artificial Neural Networks ( ANNs)
    • Artificial Neural Networks (ANNs):
    • V := set of neuron functions f(x);
    • E:= set of outputs-inputs weights
    González-Díaz H, Complex Networks in Bio-Medical Sciences
  • STATISTICA as ANN training, validation, & code generator software González-Díaz H, Complex Networks in Bio-Medical Sciences
  • MIND-BEST server Example of prediction of drugs and/or protein targets using structural parameters of drugs and 3D structure of proteins released to PDB. Ref.: H González-Díaz et al., Journal of Theoretical Biology, 276(1):229-249 ( 2011 ) [ online ] González-Díaz H, Complex Networks in Bio-Medical Sciences
  • MIND-BEST workflow González-Díaz H, Complex Networks in Bio-Medical Sciences
  • EnzClassPred
    • Example of Prediction of Enzyme function from protein 3D structures released to PDB without function annotation. Ref: Concu et al. BBA: Proteins & Proteomics 1794(12), 1784–1794 ( 2009 ) [ online ]
    González-Díaz H, Complex Networks in Bio-Medical Sciences
  • Can we express nodes importance numerically? González-Díaz H, Complex Networks in Bio-Medical Sciences
    • Node Centralities
    Proteomics , networks and connectivity indices. González-Díaz H, et al. Proteomics . 2008 Feb;8(4):750-78. Review.
  • CenTiBin: Centralities in Biological Networks González-Díaz H, Complex Networks in Bio-Medical Sciences Exploration of biological network centralities with CentiBiN . BH. Junker et al. BMC Bioinformatics, (2006) 7:219
  • Node Centrality vs. Protein Lethality
    • It is taffected to a large extent the phenotypic consequence of a single gene deletion by the topological position of its protein product in a PIN?
    • PIN node degree in predicts protein lethality ( phenotypic effect of removing the corresponding rotein) R = 0.75 in 1572 Saccharomyces cerevisiae yeast mutants.
    González-Díaz H, Complex Networks in Bio-Medical Sciences H Jeong, SP Mason, AL Barabási and ZN Oltvai Lethality and centrality in protein networks , Nature (2001) 411 , 41-42. doi:10.1038/35075138
  • Knowledge Trends Discovery
    • Medical Information & Scientific Social networks:
    • Co-citation Networks: PubMed ,
    • Research Networks: LinkedIn, ResearchGate, Facebook
    González-Díaz H, Complex Networks in Bio-Medical Sciences Link: http://nwb.cns.iu.edu/ YouTube: http://www.scivee.tv/node/27704 Co-citation network of US iRNA Patents
  • Do we need Network Collation algorithms?
    • 10,681 connectivity relations — whether a brain region in one study projects to another region in (possibly) a different study .
    • 16,712 mapping relationsmapping — whether a brain region in one study is identical to, a substructure of, or a suprastructure of another region in (possibly) a different study
    González-Díaz H, Complex Networks in Bio-Medical Sciences DS Modhaa & R Singh. Network architecture of the long-distance pathways in the macaque brain. PNAS, 2010, 107 ( 30) 13485–13490. Link: http://www.pnas.org/content/107/30/13485.abstract
    • Collation of Connectivity data on the Macaque brain (CoCoMac)
    • 410 anatomical tracing studies
    • 383 hierarchically organized regions spanning cortex, thalamus, and basal ganglia
    • 6,602 directed long-distance connections
  • QSPR collation algorithm!
    • Metabolic networks (72.3%),
    González-Díaz H, Complex Networks in Bio-Medical Sciences
    • Parasite-Host networks (93.3%),
    • CoCoMac brain network (89.6%),
  • Observed vs. Predicted González-Díaz H, Complex Networks in Bio-Medical Sciences
  • Complex Networks in Epidemiology (Epidemionics)
    • Deterministic models: differential (ODEs) or (integro)-partial differential equations (PDEs). These are the “continuum models”. Note: need powerful analysis techniques but may be not realistic.
    • Stochastic models: incorporate stochastic parameters and variables. Note: relax the hypothesis of continuum models about infinite population .
    • Individual-based models: import the uniqueness of the individual behavior in a general population (with memory). Note: relax the hypothesis of the stochastic ones about structural uniformity in the interactions.
    • Dynamic network-based / agent-based models: consider the interactions between individuals to be instantaneous as well as the hypothesis that these interactions do not affect the future interactions with other individuals (memory less). Note: relax the hypothesis of the above models
    • Ref. A.I. Reppas, K.G. Spiliotis and C.I. Siettos*. Epidemionics From the host-host interactions to the systematic analysis of the emergent macroscopic dynamics of epidemic network. Virulence 2010 , 1:4, 338-349.
    • Link: www.landesbioscience.com/journals/virulence/article/12196
    González-Díaz H, Complex Networks in Bio-Medical Sciences
  • Epidemiological & Medical-Social Networks:
    • Examples of Networks: Epidemic virus spread, Parasitism or Sexualy Transmitted Diseases (STDs) networks .
    • Networks elements: V := individuals and E := spatial proximity, physical, or sexual contacts among individuals
    • Sub-networks: small reachable sub-graph showing the increase in size with increasing duration (D) of infectiousness in weeks
    González-Díaz H, Complex Networks in Bio-Medical Sciences
  • Can we Predict Disease Spreading?
    • A: Geographical maps of Galicia showing the location of the 275 sampled farms.
    • B: Distribution of F. hepatica infection (grey: uninfected; cyan: infected w ith prevalence <25%, pink: infected prevalence ≥ 25%)
    • C: Network model for landscape parasite-spreading: d ij = 0.5*(h + h )*Tr *Tr *((xi - xj )^2 + (yi - yj ) 1/2 )
    • b ij = 1 => d ij > d cut-off *AVG(d ij )
    • S(PAT) is the a real-valued output variable that scores the propensity of one farm to present a Prevalence After Treatment (PAT) > 24% for F. hepatica infection.
    González-Díaz H, Complex Networks in Bio-Medical Sciences Complex Network Entropy: From Molecules to Biology, Parasitology, Technology, Social, Legal, and Neurosciences. González-Díaz H, Transworld Res. Net., 2011 .
  • Can we Halt Disease? - Networks & Vaccination Strategy
    • Immunization => Vaccination :=
    • Node attack := node removal
    • Random vaccination (RND): Remove nodes at random, has demonstrated to fails to prevent epidemics .
    • Neighbor vaccination (RNB): Vaccinate the neighbor of randomly chosen vertices.
    • Out vaccination (OUT) : vaccinating neighbors of a vertex v with a maximal number of edges out of v’s neighborhood .
    • Chain vaccination (C): RNBC, OUTC, etc. are variants of previous strategies but vaccinates a neighbor of the vertex vaccinated in the previous time step (if all neighbors are vaccinated a neighbor of a random vertex is chosen instead).
    González-Díaz H, Complex Networks in Bio-Medical Sciences
    • R Cohen, et al. Efficient Immunization Strategies for Computer Networks and Populations. PHYS REV LETT (2003) 91, 247901.
    • Link: http://link.aps.org/doi/10.1103/PhysRevLett.91.247901
    • P. Holme. Efficient local strategies for vaccination and network attack. EURO PHYS LETT (2004) 68 (6), 908–914.
    • Link: http://iopscience.iop.org/0295-5075/68/6/908/fulltext/
    Notes: S1 := average size of the largest connected sub-graph; f := fraction of vaccinated vertices (vertices deleted from the network); (I) karate club network, (II) Watts-Strogatz ( WS) model network.
  • Vaccination Strategy vs. Disease Dynamics
    • The average number of vertices that are infected once or more during an outbreak S for (a) the SIR and (b) the SIS disease dynamics.
    • SIS := a vertex goes from “susceptible” (S) to “infected” (I) and back to S. Stops when reach endemic state; when there are no susceptible vertices that have not had the disease at least once).
    • SIR := is similar, except that an infected vertex goes to the “removed” (R) state and stays there. Stops when there are no I-vertices present.
    • P. Holme. Efficient local strategies for vaccination and network attack. EURO PHYS LETT (2004) 68 (6), 908–914. Link: http://iopscience.iop.org/0295-5075/68/6/908/fulltext /
    González-Díaz H, Complex Networks in Bio-Medical Sciences
  • Further Readings:
    • The Structure and Dynamics of Networks . M. Newman, D. Watts, A.-L. Barabási. Princeton University Press, 2006 .
    • Linked: The New Science of Networks . A.-L. Barabási,Perseus, Cambridge, MA, 2002 .
    González-Díaz H, Complex Networks in Bio-Medical Sciences
    • Handbook of Graphs and Networks: From the Genome to the Internet.
    • S Bornholdt & HG Schuster, Wiley, 2003.
    • Complex Network Entropy: From Molecules to Biology, Parasitology, Technology, Social, Legal, and Neurosciences. González-Díaz H, Transworld Res. Net., 2011 .
  • Interactive Classes: Risk Groups and Vaccination
    • AIMS & EXCERCISES:
    • Introduction to CENTIBIN . Network assemble, upload, visualization, generation, node centralities calculation, giant component, …
    • Learning to assemble epidemic risk complex networks using medical interviews in a population/risk group.
    • Visual detection of risk communities with Kamada-Kawai algorithm.
    • Quantitative detection of risk individuals (hubs) with CENTIBIN. Local (node degree) vs. global risk scores (closeness).
    • Simulate vaccine strategies (RND, DEG, CLO) in a real complex network and compare results.
    • Compare vaccination strategies efficiency in terms of average distance D vs. f for giant component.
    González-Díaz H, Complex Networks in Bio-Medical Sciences Exploration of biological network centralities with CentiBiN . Björn H. Junker, Dirk Koschützki and Falk Schreiber. BMC Bioinformatics (2006 ) 7:219
  • Tutorial: A typical session with CentiBiN
    • Content
    • Start CentiBiN
    • Generate a random network
    • Load network data
    • Network layout
    • Prepare network for centralities
    • Calculate network centralities
    • Visualise network centralities
    González-Díaz H, Complex Networks in Bio-Medical Sciences
  • 1. Start CentiBiN
    • CentiBiN can be started at: http://centibin.ipk-gatersleben.de/starting.php . Please choose the version according to RAM available in your computer. Be assure Java runtime environment (JRE) is installed. JRE instructions are given on same page . Per default upon startup, CentiBiN shows a small randomly generated network.
    González-Díaz H, Complex Networks in Bio-Medical Sciences
  • 2. Generate a random network
    • If you want to create a different random network, CentiBiN offers some possibilities in the menu &quot;Generate&quot;.
    • It is recommended that you try at first the default values, higher values might lead to time-intensive creation- and layout procedures.
    González-Díaz H, Complex Networks in Bio-Medical Sciences
  • 3. Loading network data
    • As an alternative to step 2 (the creation of a random network), you can load network data from a file.
    • For that option, CentiBiN supports the Pajek format(*.net), DIPs tab separated files (*.tab), adjacency matrices given in text files (*mat), and the GraphML file format (*.xml).
    • For detailed explanations on the different file formats please refer to the &quot;Help&quot; menu.
    González-Díaz H, Complex Networks in Bio-Medical Sciences
  • 4. Network layout
    • Now you can improve the network visualization by using different layout algorithms.
    • Depending on the network structure, one or the other of the five algorithms might give the best result.
    González-Díaz H, Complex Networks in Bio-Medical Sciences
  • 5. Prepare network for centralities
    • Depending on the centrality measure to be applied the network has to fulfil certain preconditions. These can be simplicity, connectedness and loop-freeness .
    • Several algorithms are implemented to clean up the network , however the easiest way to prepare the network is just to apply the function &quot;Prepare for centralities&quot;, which is performing all cleanup procedures at once.
    • Choose the appropriate option depending on whether you have a directed or an undirected network.
    González-Díaz H, Complex Networks in Bio-Medical Sciences
  • 6. Calculate network centralities
    • This step is the main part of CentiBiN. You can now calculate 17 or 15 centralities for an undirected or directed graph, respectively.
    • A table will appear on the right side showing the nodes ranked according to the chosen centrality measure, including the correspondent value.
    • Note difference between node degree (undirected network) and In-degree + Out-Degree = Node Degree (directed networks)
    González-Díaz H, Complex Networks in Bio-Medical Sciences
  • 7. Visualize network centralities
    • On the table on the right side, you can now mark a number of nodes.
    • For better visibility, the marked nodes will be coloured red in the network.
    • Compare differences in node rank by local degree vs. global closeness .
    González-Díaz H, Complex Networks in Bio-Medical Sciences