A vision and strategy for the virtual physiological human: 2012 update.

European funding under Framework 7 (FP7) for the virtual physiological human (VPH) project has been in place now for 5 years. The VPH Network of Excellence (NoE) has been set up to help develop common standards, open source software, freely accessible data and model repositories, and various training and dissemination activities for the project. It is also working to coordinate the many clinically targeted projects that have been funded under the FP7 calls. An initial vision for the VPH was defined by the FP6 STEP project in 2006. In 2010, we wrote an assessment of the accomplishments of the first two years of the VPH in which we considered the biomedical science, healthcare and information and communications technology challenges facing the project (Hunter et al. 2010 Phil. Trans. R. Soc. A 368, 2595-2614 (doi:10.1098/rsta.2010.0048)). We proposed that a not-for-profit professional umbrella organization, the VPH Institute, should be established as a means of sustaining the VPH vision beyond the time-frame of the NoE. Here, we update and extend this assessment and in particular address the following issues raised in response to Hunter et al.: (i) a vision for the VPH updated in the light of progress made so far, (ii) biomedical science and healthcare challenges that the VPH initiative can address while also providing innovation opportunities for the European industry, and (iii) external changes needed in regulatory policy and business models to realize the full potential that the VPH has to offer to industry, clinics and society generally.

A computational exploration of bacterial metabolic diversity identifying metabolic interactions and growth-efficient strain communities.

BACKGROUND: Metabolic interactions involve the exchange of metabolic products among microbial species. Most microbes live in communities and usually rely on metabolic interactions to increase their supply for nutrients and better exploit a given environment. Constraint-based models have successfully analyzed cellular metabolism and described genotype-phenotype relations. However, there are only a few studies of genome-scale multi-species interactions. Based on genome-scale approaches, we present a graph-theoretic approach together with a metabolic model in order to explore the metabolic variability among bacterial strains and identify and describe metabolically interacting strain communities in a batch culture consisting of two or more strains. We demonstrate the applicability of our approach to the bacterium E. coli across different single-carbon-source conditions. RESULTS: A different diversity graph is constructed for each growth condition. The graph-theoretic properties of the constructed graphs reflect the inherent high metabolic redundancy of the cell to single-gene knockouts, reveal mutant-hubs of unique metabolic capabilities regarding by-production, demonstrate consistent metabolic behaviors across conditions and show an evolutionary difficulty towards the establishment of polymorphism, while suggesting that communities consisting of strains specifically adapted to a given condition are more likely to evolve. We reveal several strain communities of improved growth relative to corresponding monocultures, even though strain communities are not modeled to operate towards a collective goal, such as the community growth and we identify the range of metabolites that are exchanged in these batch co-cultures. CONCLUSIONS: This study provides a genome-scale description of the metabolic variability regarding by-production among E. coli strains under different conditions and shows how metabolic differences can be used to identify metabolically interacting strain communities. This work also extends the existing stoichiometric models in order to describe batch co-cultures and provides the extent of metabolic interactions in a strain community revealing their importance for growth.

BrainNetVis: an open-access tool to effectively quantify and visualize brain networks.

This paper presents BrainNetVis, a tool which serves brain network modelling and visualization, by providing both quantitative and qualitative network measures of brain interconnectivity. It emphasizes the needs that led to the creation of this tool by presenting similar works in the field and by describing how our tool contributes to the existing scenery. It also describes the methods used for the calculation of the graph metrics (global network metrics and vertex metrics), which carry the brain network information. To make the methods clear and understandable, we use an exemplar dataset throughout the paper, on which the calculations and the visualizations are performed. This dataset consists of an alcoholic and a control group of subjects.

A vision and strategy for the virtual physiological human in 2010 and beyond.

European funding under framework 7 (FP7) for the virtual physiological human (VPH) project has been in place now for nearly 2 years. The VPH network of excellence (NoE) is helping in the development of common standards, open-source software, freely accessible data and model repositories, and various training and dissemination activities for the project. It is also helping to coordinate the many clinically targeted projects that have been funded under the FP7 calls. An initial vision for the VPH was defined by framework 6 strategy for a European physiome (STEP) project in 2006. It is now time to assess the accomplishments of the last 2 years and update the STEP vision for the VPH. We consider the biomedical science, healthcare and information and communications technology challenges facing the project and we propose the VPH Institute as a means of sustaining the vision of VPH beyond the time frame of the NoE.

BrainNetVis: analysis and visualization of brain functional networks.

BrainNetVis is an application, written in Java, that displays and analyzes synchronization networks from brain signals. The program implements a number of network indices and visualization techniques. We demonstrate its use through a case study of left hand and foot motor imagery. The data sets were provided by the Berlin BCI group. Using this program we managed to find differences between the average left hand and foot synchronization networks by comparing them with the average idle state synchronization network.

Optimal brain network synchrony visualization: application in an alcoholism paradigm.

Although Electroencephalographic (EEG) signal synchronization studies have been a topic of increasing interest lately, there is no similar effort in the visualization of such measures. In this direction a graph-theoretic approach devised to study and stress the coupling dynamics of task-performing dynamical networks is proposed. Both linear and nonlinear interdependence measures are investigated in an alcoholism paradigm during mental rehearsal of pictures, which is known to reflect synchronization impairment. More specifically, the widely used magnitude squared coherence; phase synchronization and a robust nonlinear state-space generalized synchronization assessment method are investigated. This paper mostly focuses on a signal-based technique of selecting the optimal visualization threshold using surrogate datasets to correctly identify the most significant correlation patterns. Furthermore, a graph statistical parameter attempts to capture and quantify collective motifs present in the functional brain network. The results are in accordance with previous psychophysiology studies suggesting that an alcoholic subject has impaired synchronization of brain activity and loss of lateralization during the rehearsal process, most prominently in alpha (8-12 Hz) band, as compared to a control subject. Lower beta (13-30 Hz) synchronization was also evident in the alcoholic subject.

SYMBIOmatics: synergies in Medical Informatics and Bioinformatics--exploring current scientific literature for emerging topics.

BACKGROUND: The SYMBIOmatics Specific Support Action (SSA) is "an information gathering and dissemination activity" that seeks "to identify synergies between the bioinformatics and the medical informatics" domain to improve collaborative progress between both domains (ref. to http://www.symbiomatics.org). As part of the project experts in both research fields will be identified and approached through a survey. To provide input to the survey, the scientific literature was analysed to extract topics relevant to both medical informatics and bioinformatics. RESULTS: This paper presents results of a systematic analysis of the scientific literature from medical informatics research and bioinformatics research. In the analysis pairs of words (bigrams) from the leading bioinformatics and medical informatics journals have been used as indication of existing and emerging technologies and topics over the period 2000-2005 ("recent") and 1990-1990 ("past"). We identified emerging topics that were equally important to bioinformatics and medical informatics in recent years such as microarray experiments, ontologies, open source, text mining and support vector machines. Emerging topics that evolved only in bioinformatics were system biology, protein interaction networks and statistical methods for microarray analyses, whereas emerging topics in medical informatics were grid technology and tissue microarrays. CONCLUSION: We conclude that although both fields have their own specific domains of interest, they share common technological developments that tend to be initiated by new developments in biotechnology and computer science.

Time-significant wavelet coherence for the evaluation of schizophrenic brain activity using a graph theory approach.

Among the various frameworks in which electroencephalographic (EEG) signal synchronization has been traditionally formulated, the most widely studied and used is the coherence that is entirely based on frequency analysis. However, at present time it is possible to capture information about the temporal profile of coherence, which is particularly helpful in studying non-stationary time-varying brain dynamics, like the wavelet coherence (WC). In this paper we propose a new approach of studying brain synchronization dynamics by extending the use of WC to include certain statistically significant (in terms of signal coherence) time segments, to study and characterize any disturbances present in the functional connectivity network of schizophrenia patients. Graph theoretical measures and visualization provide the tools to study the "disconnection syndrome" as proposed for schizophrenia. Specifically, we analyzed multichannel EEG data from twenty stabilized patients with schizophrenia and controls in an experiment of working memory (WM) using the gamma band (i.e., the EEG frequency of ca. 40 Hz), which is activated during the connecting activity (i.e., the "binding" of the neurons). The results are in accordance with the disturbance of connections between the neurons giving additional information related to the localization of most prominent disconnection.