Computational Pipeline for Analysis of Biomedical Networks with BioNAR

In a living cell, proteins interact to assemble both transient and constant molecular complexes, which transfer signals/information around internal pathways. Modern proteomic techniques can identify the constituent components of these complexes, but more detailed analysis demands a network approach linking the molecules together and analyzing the emergent architectural properties. The Bioconductor package BioNAR combines a selection of existing R protocols for network analysis with newly designed original methodological features to support step-by-step analysis of biological/biomedical . Critically, BioNAR supports a pipeline approach whereby many networks and iterative analyses can be performed. Here we present a network analysis pipeline that starts from initiating a network model from a list of components/proteins and their interactions through to identifying its functional components based solely on network topology. We demonstrate that BioNAR can help users achieve a number of network analysis goals that are difficult to achieve anywhere else. This includes how users can choose the optimal clustering algorithm from a range of options based on independent annotation enrichment, and predict a protein's influence within and across multiple subcomplexes in the network and estimate the co-occurrence or linkage between metadata at the network level (e.g., diseases and functions across the network, identifying the clusters whose components are likely to share common function and mechanisms). The package is freely available in Bioconductor release 3.17: https://bioconductor.org/packages/3.17/bioc/html/BioNAR.html. © 2023 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Creating and annotating the network Support Protocol 1: Installing BioNAR from RStudio Support Protocol 2: Building the sample network from synaptome.db Basic Protocol 2: Network properties and centrality Basic Protocol 3: Network communities Basic protocol 4: Choosing the optimal clustering algorithm based on the enrichment with annotation terms Basic Protocol 5: Influencing network components and bridgeness Basic Protocol 6: Co-occurrence of the annotations.

Genomic analysis and assessment of pathogenic (toxicogenic) potential of Staphylococcus haemolyticus and Bacillus paranthracis consortia isolated from bovine mastitis in Russia

Three stable microbial consortia, each composed of Bacillus paranthracis and Staphylococcus haemolyticus strains, were isolated from milk of cows diagnosed with mastitis in three geographically remote regions of Russia. The composition of these consortia remained stable following multiple passages on culture media. Apparently, this stability is due to the structure of the microbial biofilms formed by the communities. The virulence of the consortia depended on the B. paranthracis strains. It seems plausible that the ability of the consortia to cause mastitis in cattle was affected by mutations of the cytK gene of B. paranthracis.

BioNAR: an integrated biological network analysis package in bioconductor

Motivation

Biological function in protein complexes emerges from more than just the sum of their parts: molecules interact in a range of different sub-complexes and transfer signals/information around internal pathways. Modern proteomic techniques are excellent at producing a parts-list for such complexes, but more detailed analysis demands a network approach linking the molecules together and analysing the emergent architectural properties. Methods developed for the analysis of networks in social sciences have proven very useful for splitting biological networks into communities leading to the discovery of sub-complexes enriched with molecules associated with specific diseases or molecular functions that are not apparent from the constituent components alone.

Results

Here, we present the Bioconductor package BioNAR, which supports step-by-step analysis of biological/biomedical networks with the aim of quantifying and ranking each of the network's vertices based on network topology and clustering. Examples demonstrate that while BioNAR is not restricted to proteomic networks, it can predict a protein's impact within multiple complexes, and enables estimation of the co-occurrence of metadata, i.e. diseases and functions across the network, identifying the clusters whose components are likely to share common function and mechanisms.

Availability and implementation

The package is available from Bioconductor release 3.17: https://bioconductor.org/packages/release/bioc/html/BioNAR.html.

FBA-PRCC. Partial Rank Correlation Coefficient (PRCC) Global Sensitivity Analysis (GSA) in Application to Constraint-Based Models

Background: Whole-genome models (GEMs) have become a versatile tool for systems biology, biotechnology, and medicine. GEMs created by automatic and semi-automatic approaches contain a lot of redundant reactions. At the same time, the nonlinearity of the model makes it difficult to evaluate the significance of the reaction for cell growth or metabolite production. Methods: We propose a new way to apply the global sensitivity analysis (GSA) to GEMs in a straightforward parallelizable fashion. Results: We have shown that Partial Rank Correlation Coefficient (PRCC) captures key steps in the metabolic network despite the network distance from the product synthesis reaction. Conclusions: FBA-PRCC is a fast, interpretable, and reliable metric to identify the sign and magnitude of the reaction contribution to various cellular functions.

Synaptome.db: a bioconductor package for synaptic proteomics data

Summary

The neuronal synapse is underpinned by a large and diverse proteome but the molecular evidence is spread across many primary datasets. These data were recently curated into a single dataset describing a landscape of ∼8000 proteins found in studies of mammalian synapses. Here, we describe programmatic access to the dataset via the R/Bioconductor package Synaptome.db, which enables convenient and in-depth data analysis from within the Bioconductor environment. Synaptome.db allows users to obtain the respective gene information, e.g. subcellular localization, brain region, gene ontology, disease association and construct custom protein-protein interaction network models for gene sets and entire subcellular compartments.

Availability and implementation

The package Synaptome.db is part of Bioconductor since release 3.14, https://bioconductor.org/packages/release/data/annotation/html/synaptome.db.html, it is open source and available under the Artistic license 2.0. The development version is maintained on GitHub (https://github.com/lptolik/synaptome.db). Full documentation including examples is provided in the form of vignettes on the package webpage.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Comparative Analysis of Milk Microbiomes and Their Association with Bovine Mastitis in Two Farms in Central Russia

Bovine mastitis is a widespread infectious disease. In addition to the economic damages associated with reduced milk yield due to mastitis, the problem of food contamination by microorganism metabolites, in particular toxins, is also a concern. Horizontal transfer of microorganisms from animal populations to humans can also be complicated by antibiotic resistance. Therefore, bovine mastitis is relevant to the study of microbiology and veterinary medicine. In this study, we investigated the microbiome of milk samples from healthy cows and cows with different forms of mastitis from individual quarters of the udder of cows during first and second lactation. Total DNA was extracted from milk samples. The V3-V4 regions of the bacterial 16S rRNA genes from each sample were amplified to generate a library via high-throughput sequencing. We revealed significant dominance of several operational taxonomic units (OTUs) corresponding mostly to groups of Staphylococcus aureus, Aerococcus spp., and Streptococcus spp. In addition, we unexpectedly identified Streptococcus thermophilus in samples with high SCC quantities. We found some infectious agents that characterized summer mastitis. We demonstrated that in Central Russia, mastitis is associated with a wide variety of causal organisms. We observed some differences in the diversity of the two investigated farms. However, we did not find any significant difference among healthy, mastitis and subclinical samples according to their SCC status from either farms by principal component analysis. Linear discriminant analysis effect size (LEfSe) confirmed the presence of several indicator genera in farms from Moscow and the Tula Region. These results confirm the complex bacterial etiology of bovine mastitis.

SBGN Bricks Ontology as a tool to describe recurring concepts in molecular networks

A comprehensible representation of a molecular network is key to communicating and understanding scientific results in systems biology. The Systems Biology Graphical Notation (SBGN) has emerged as the main standard to represent such networks graphically. It has been implemented by different software tools, and is now largely used to communicate maps in scientific publications. However, learning the standard, and using it to build large maps, can be tedious. Moreover, SBGN maps are not grounded on a formal semantic layer and therefore do not enable formal analysis. Here, we introduce a new set of patterns representing recurring concepts encountered in molecular networks, called SBGN bricks. The bricks are structured in a new ontology, the Bricks Ontology (BKO), to define clear semantics for each of the biological concepts they represent. We show the usefulness of the bricks and BKO for both the template-based construction and the semantic annotation of molecular networks. The SBGN bricks and BKO can be freely explored and downloaded at sbgnbricks.org.

Comparison of Dimensionality Reduction Methods in Mass Spectra of Astrocytoma and Glioblastoma Tissues

Recently developed methods of ambient ionization allow the collection of mass spectrometric datasets for biological and medical applications at an unprecedented pace. One of the areas that could employ such analysis is neurosurgery. The fast in situ identification of dissected tissues could assist the neurosurgery procedure. In this paper tumor tissues of astrocytoma and glioblastoma are compared. The vast majority of the data representation methods are hard to use, as the number of features is high and the amount of samples is limited. Furthermore, the ratio of features and samples number restricts the use of many machine learning methods. The number of features could be reduced through feature selection algorithms or dimensionality reduction methods. Different algorithms of dimensionality reduction are considered along with the traditional noise thresholding for the mass spectra. From our analysis, the Isomap algorithm appears to be the most effective dimensionality reduction algorithm for negative mode, whereas the positive mode could be processed with a simple noise reduction by a threshold. Also, negative and positive mode correspond to different sample properties: negative mode is responsible for the inner variability and the details of the sample, whereas positive mode describes measurement in general.

Virulence Factors and Phylogeny of Staphylococcus aureus Associated With Bovine Mastitis in Russia Based on Genome Sequences

Staphylococcus aureus is a causative agent of different infectious processes, food poisoning, and autoimmune disorders. The horizontal transfer of pathogenic strains can occur from animal to human under both house and farm conditions, and the spread of strains with antibiotic resistance is an existing problem. In addition to the spread of antibiotic-resistant strains in clinics, this problem also exists in veterinary medicine. It is especially important to monitor antibiotic resistance on farms where antibiotics are the standard treatment of animals, which may trigger the spread of antibiotic-resistant strains among animals and to the human population, and these strains can also be distributed in milk products produced by these farms (milk, cheese, and butter). In this work, we investigated 21 S. aureus isolates using whole-genome sequence analysis and tried to establish a relationship between these isolates with the development of bovine mastitis in seven regions of Western Russia. An S. aureus virulence profile was identified. We identified two groups of S. aureus associated with subclinical mastitis, namely, the enterotoxin-positive and enterotoxin-negative groups. The most prevalent factor associated with bovine mastitis in Russia was cytotoxins, including hemolysins and leukocidins. Multidrug resistance strains were investigated, and antibiotic resistance genes were identified. We identified S. aureus ST 97 type as the most common type in the regions in Western Russia. To the best of our knowledge, this is the first in-depth study of a range S. aureus isolates originating from cattle infections in Russia.

Triple blockade of EGFR, MEK and PD-L1 has antitumor activity in colorectal cancer models with constitutive activation of MAPK signaling and PD-L1 overexpression

BACKGROUND

Molecular mechanisms driving acquired resistance to anti-EGFR therapies in metastatic colorectal cancer (mCRC) are complex but generally involve the activation of the downstream RAS-RAF-MEK-MAPK pathway. Nevertheless, even if inhibition of EGFR and MEK could be a strategy for overcoming anti-EGFR resistance, its use is limited by the development of MEK inhibitor (MEKi) resistance.

METHODS

We have generated in vitro and in vivo different CRC models in order to underline the mechanisms of MEKi resistance.

RESULTS

The three different in vitro MEKi resistant models, two generated by human CRC cells quadruple wild type for KRAS, NRAS, BRAF, PI3KCA genes (SW48-MR and LIM1215-MR) and one by human CRC cells harboring KRAS mutation (HCT116-MR) showed features related to the gene signature of colorectal cancer CMS4 with up-regulation of immune pathway as confirmed by microarray and western blot analysis. In particular, the MEKi phenotype was associated with the loss of epithelial features and acquisition of mesenchymal markers and morphology. The change in morphology was accompanied by up-regulation of PD-L1 expression and activation of EGFR and its downstream pathway, independently to RAS mutation status. To extend these in vitro findings, we have obtained mouse colon cancer MC38- and CT26-MEKi resistant syngeneic models (MC38-MR and CT26-MR). Combined treatment with MEKi, EGFR inhibitor (EGFRi) and PD-L1 inhibitor (PD-L1i) resulted in a marked inhibition of tumor growth in both models.

CONCLUSIONS

These results suggest a strategy to potentially improve the efficacy of MEK inhibition by co-treatment with EGFR and PD-L1 inhibitors via modulation of host immune responses.

Untangling the Metabolic Reprogramming in Brain Cancer: Discovering Key Molecular Players Using Mass Spectrometry

Cells metabolism alteration is the new hallmark of cancer, as well as an important method for carcinogenesis investigation. It is well known that the malignant cells switch to aerobic glycolysis pathway occurring also in healthy proliferating cells. Recently, it was shown that in malignant cells de novo synthesis of the intracellular fatty acid replaces dietary fatty acids which change the lipid composition of cancer cells noticeably. These alterations in energy metabolism and structural lipid production explain the high proliferation rate of malignant tissues. However, metabolic reprogramming affects not only lipid metabolism but many of the metabolic pathways in the cell. 2-hydroxyglutarate was considered as cancer cell biomarker and its presence is associated with oxidative stress influencing the mitochondria functions. Among the variety of metabolite detection methods, mass spectrometry stands out as the most effective method for simultaneous identification and quantification of the metabolites. As the metabolic reprogramming is tightly connected with epigenetics and signaling modifications, the evaluation of metabolite alterations in cells is a promising approach to investigate the carcinogenesis which is necessary for improving current diagnostic capabilities and therapeutic capabilities. In this paper, we overview recent studies on metabolic alteration and oncometabolites, especially concerning brain cancer and mass spectrometry approaches which are now in use for the investigation of the metabolic pathway.

Systems Biology Graphical Notation: Process Description language Level 1 Version 2.0

The Systems Biology Graphical Notation (SBGN) is an international community effort that aims to standardise the visualisation of pathways and networks for readers with diverse scientific backgrounds as well as to support an efficient and accurate exchange of biological knowledge between disparate research communities, industry, and other players in systems biology. SBGN comprises the three languages Entity Relationship, Activity Flow, and Process Description (PD) to cover biological and biochemical systems at distinct levels of detail. PD is closest to metabolic and regulatory pathways found in biological literature and textbooks. Its well-defined semantics offer a superior precision in expressing biological knowledge. PD represents mechanistic and temporal dependencies of biological interactions and transformations as a graph. Its different types of nodes include entity pools (e.g. metabolites, proteins, genes and complexes) and processes (e.g. reactions, associations and influences). The edges describe relationships between the nodes (e.g. consumption, production, stimulation and inhibition). This document details Level 1 Version 2.0 of the PD specification, including several improvements, in particular: 1) the addition of the equivalence operator, subunit, and annotation glyphs, 2) modification to the usage of submaps, and 3) updates to clarify the use of various glyphs (i.e. multimer, empty set, and state variable).

RKappa: Software for Analyzing Rule-Based Models

RKappa is a framework for the development, simulation, and analysis of rule-based models within the mature statistically empowered R environment. It is designed for model editing, parameter identification, simulation, sensitivity analysis, and visualization. The framework is optimized for high-performance computing platforms and facilitates analysis of large-scale systems biology models where knowledge of exact mechanisms is limited and parameter values are uncertain.The RKappa software is an open-source (GLP3 license) package for R, which is freely available online ( https://github.com/lptolik/R4Kappa ).

ASAR: visual analysis of metagenomes in R

Motivation

Functional and taxonomic analyses are critical steps in understanding interspecific interactions within microbial communities. Currently, such analyses are run separately, which complicates interpretation of results. Here we present the ASAR interactive tool for simultaneous analysis of metagenomic data in three dimensions: taxonomy, function, metagenome.

Results

An interactive data analysis tool for selection, aggregation and visualization of metagenomic data is presented. Functional analysis with a SEED hierarchy and pathway diagram based on KEGG orthology based upon MG-RAST annotation results is available.

Availability and implementation

Source code of the ASAR is accessible at GitHub (https://github.com/Askarbek-orakov/ASAR).

Comparative Analysis of Mycoplasma gallisepticum vlhA Promoters

Mycoplasma gallisepticum is an intracellular parasite affecting respiratory tract of poultry that belongs to class Mollicutes. M. gallisepticum features numerous variable lipoprotein hemagglutinin genes (vlhA) that play a role in immune escape. The vlhA promoters have a set of distinct properties in comparison to promoters of the other genes. The vlhA promoters carry a variable GAA repeats region at approximately 40 nts upstream of transcription start site. The promoters have been considered active only in the presence of exactly 12 GAA repeats. The mechanisms of vlhA expression regulation and GAA number variation are not described. Here we tried to understand these mechanisms using different computational methods. We conducted a comparative analysis among several M. gallisepticum strains. Nucleotide sequences analysis showed the presence of highly conserved regions flanking repeated trinucleotides that are not linked to GAA number variation. VlhA genes with 12 GAA repeats and their orthologs in 12 M. gallisepticum strains are more conserved than other vlhA genes and have narrower GAA number distribution. We conducted comparative analysis of physicochemical profiles of M. gallisepticum vlhA and sigma-70 promoters. Stress-induced duplex destabilization (SIDD) profiles showed that sigma-70 group is characterized by the common to prokaryotic promoters sharp maxima while vlhA promoters are hardly destabilized with the region between GAA repeats and transcription start site having zero opening probability. Electrostatic potential profiles of vlhA promoters indicate the presence of the distinct patterns that appear to govern initial stages of specific DNA-protein recognition. Open state dynamics profiles of vlhA demonstrate the pattern that might facilitate transcription bubble formation. Obtained data could be the basis for experimental identification of mechanisms of phase variation in M. gallisepticum.

Bacterial promoter prediction: Selection of dynamic and static physical properties of DNA for reliable sequence classification

Predicting promoter activity of DNA fragment is an important task for computational biology. Approaches using physical properties of DNA to predict bacterial promoters have recently gained a lot of attention. To select an adequate set of physical properties for training a classifier, various characteristics of DNA molecule should be taken into consideration. Here, we present a systematic approach that allows us to select less correlated properties for classification by means of both correlation and cophenetic coefficients as well as concordance matrices. To prove this concept, we have developed the first classifier that uses not only sequence and static physical properties of DNA fragment, but also dynamic properties of DNA open states. Therefore, the best performing models with accuracy values up to 90% for all types of sequences were obtained. Furthermore, we have demonstrated that the classifier can serve as a reliable tool enabling promoter DNA fragments to be distinguished from promoter islands despite the similarity of their nucleotide sequences.

Feature selection algorithm for spray-from-tissue mass spectrometry

Detection of the brain tumor margins is one of the most significant problems in neurosurgery. Several mass spectrometry-based approaches have been proposed recently for tumor boundary detection. One of them, spray from tissue does not require sample preparation but needs special algorithms for analysis of its spectra. Here we proposed the feature selection algorithm designed for analysis of spray-from-tissue data.

Multi-slit triode ion optical system with ballistic beam focusing

Multi-slit triode ion-optical systems with spherical electrodes are of interest for formation of intense focused neutral beams for plasma heating. At present, two versions of focusing multi-slit triode ion optical system are developed. The first ion optical system forms the proton beam with 15 keV energy, 140 A current, and 30 ms duration. The second ion optical system is intended for heating neutral beam injector of Tokamak Configuration Variable (TCV). The injector produces focused deuterium neutral beam with 35 keV energy, 1 MW power, and 2 s duration. In the later case, the angular beam divergence of the neutral beam is 20-22 mrad in the direction across the slits of the ion optical system and 12 mrad in the direction along the slits.

Low energy, high power hydrogen neutral beam for plasma heating

A high power, relatively low energy neutral beam injector was developed to upgrade of the neutral beam system of the gas dynamic trap device and C2-U experiment. The ion source of the injector produces a proton beam with the particle energy of 15 keV, current of up to 175 A, and pulse duration of a few milliseconds. The plasma emitter of the ion source is produced by superimposing highly ionized plasma jets from an array of four arc-discharge plasma generators. A multipole magnetic field produced with permanent magnets at the periphery of the plasma box is used to increase the efficiency and improve the uniformity of the plasma emitter. Multi-slit grids with 48% transparency are fabricated from bronze plates, which are spherically shaped to provide geometrical beam focusing. The focal length of the Ion Optical System (IOS) is 3.5 m and the initial beam diameter is 34 cm. The IOS geometry and grid potentials were optimized numerically to ensure accurate beam formation. The measured angular divergences of the beam are ±0.01 rad parallel to the slits and ±0.03 rad in the transverse direction.

Systems Biology Graphical Notation: Activity Flow language Level 1 Version 1.2

The Systems Biological Graphical Notation (SBGN) is an international community effort for standardized graphical representations of biological pathways and networks. The goal of SBGN is to provide unambiguous pathway and network maps for readers with different scientific backgrounds as well as to support efficient and accurate exchange of biological knowledge between different research communities, industry, and other players in systems biology. Three SBGN languages, Process Description (PD), Entity Relationship (ER) and Activity Flow (AF), allow for the representation of different aspects of biological and biochemical systems at different levels of detail.

The SBGN Activity Flow language represents the influences of activities among various entities within a network. Unlike SBGN PD and ER that focus on the entities and their relationships with others, SBGN AF puts the emphasis on the functions (or activities) performed by the entities, and their effects to the functions of the same or other entities. The nodes (elements) describe the biological activities of the entities, such as protein kinase activity, binding activity or receptor activity, which can be easily mapped to Gene Ontology molecular function terms. The edges (connections) provide descriptions of relationships (or influences) between the activities, e.g., positive influence and negative influence. Among all three languages of SBGN, AF is the closest to signaling pathways in biological literature and textbooks, but its well-defined semantics offer a superior precision in expressing biological knowledge.

Systems Biology Graphical Notation: Entity Relationship language Level 1 Version 2

The Systems Biological Graphical Notation (SBGN) is an international community effort for standardized graphical representations of biological pathways and networks. The goal of SBGN is to provide unambiguous pathway and network maps for readers with different scientific backgrounds as well as to support efficient and accurate exchange of biological knowledge between different research communities, industry, and other players in systems biology. Three SBGN languages, Process Description (PD), Entity Relationship (ER) and Activity Flow (AF), allow for the representation of different aspects of biological and biochemical systems at different levels of detail.

The SBGN Entity Relationship language (ER) represents biological entities and their interactions and relationships within a network. SBGN ER focuses on all potential relationships between entities without considering temporal aspects. The nodes (elements) describe biological entities, such as proteins and complexes. The edges (connections) provide descriptions of interactions and relationships (or influences), e.g., complex formation, stimulation and inhibition. Among all three languages of SBGN, ER is the closest to protein interaction networks in biological literature and textbooks, but its well-defined semantics offer a superior precision in expressing biological knowledge.

Neurons expressing individual enzymes of dopamine synthesis in the mediobasal hypothalamus of adult rats: functional significance and topographic interrelations

Besides dopaminergic (DA-ergic) neurons having all enzymes of DA synthesis, tyrosine hydroxylase (TH) and aromatic l-amino acid decarboxylase (AADC), "monoenzymatic" neurons expressing only one of them were found in the brain, mostly in the mediobasal hypothalamus (MBH). The aim of this study was to test our hypothesis that DA is synthesized by monoenzymatic neurons, i.e. l-3,4-dihydroxyphenylalanine (l-DOPA), which produced in the monoenzymatic TH neurons is transported in the monoenzymatic AADC neurons for DA synthesis. Incubation of MBH in Krebs-Ringer solution with l-leucine, a competitive inhibitor of l-DOPA uptake, was used to prevent a hypothetical l-DOPA capture into AADC-containing neurons. Incubation of the substantia nigra containing DA-ergic neurons under the same conditions served as the control. According to our data, the l-leucine administration provoked a decrease of DA concentration in MBH and in the incubation medium but not in the substantia nigra and respective incubation medium, showing a decrease of cooperative synthesis of DA in MBH. This conclusion was supported by an observation of higher concentration of l-DOPA in the incubation medium under perfusion of MBH with Krebs-Ringer solution containing tolcapone, an inhibitor of catechol-O-methyltransferase, and l-leucine than under perfusion with the same solution, but without l-leucine. Functional interaction between monoenzymatic TH and AADC neurons was indirectly confirmed by finding in electron microscopy their close relations in MBH. Besides monoenzymatic AADC neurons, any AADC-possessing neurons, catecholaminergic and serotoninergic, apparently, could participate in DA synthesis together with monoenzymatic TH neurons. This idea was confirmed by the observation of close topographic relations between monoenzymatic TH neurons and those containing both enzymes, i.e. DA-ergic, noradrenergic or adrenergic. Thus, monoenzymatic neurons possessing TH or AADC and being in close topographic relations can synthesize DA in cooperation.

A simulator for spatially extended kappa models

Spatial Kappa is a simulator of models written in a variant of the rule-based stochastic modelling language Kappa, with spatial extensions.

SBSI: an extensible distributed software infrastructure for parameter estimation in systems biology

SUMMARY

Complex computational experiments in Systems Biology, such as fitting model parameters to experimental data, can be challenging to perform. Not only do they frequently require a high level of computational power, but the software needed to run the experiment needs to be usable by scientists with varying levels of computational expertise, and modellers need to be able to obtain up-to-date experimental data resources easily. We have developed a software suite, the Systems Biology Software Infrastructure (SBSI), to facilitate the parameter-fitting process. SBSI is a modular software suite composed of three major components: SBSINumerics, a high-performance library containing parallelized algorithms for performing parameter fitting; SBSIDispatcher, a middleware application to track experiments and submit jobs to back-end servers; and SBSIVisual, an extensible client application used to configure optimization experiments and view results. Furthermore, we have created a plugin infrastructure to enable project-specific modules to be easily installed. Plugin developers can take advantage of the existing user-interface and application framework to customize SBSI for their own uses, facilitated by SBSI's use of standard data formats.

AVAILABILITY AND IMPLEMENTATION

All SBSI binaries and source-code are freely available from http://sourceforge.net/projects/sbsi under an Apache 2 open-source license. The server-side SBSINumerics runs on any Unix-based operating system; both SBSIVisual and SBSIDispatcher are written in Java and are platform independent, allowing use on Windows, Linux and Mac OS X. The SBSI project website at http://www.sbsi.ed.ac.uk provides documentation and tutorials.

Software support for SBGN maps: SBGN-ML and LibSBGN

Motivation: LibSBGN is a software library for reading, writing and manipulating Systems Biology Graphical Notation (SBGN) maps stored using the recently developed SBGN-ML file format. The library (available in C++ and Java) makes it easy for developers to add SBGN support to their tools, whereas the file format facilitates the exchange of maps between compatible software applications. The library also supports validation of maps, which simplifies the task of ensuring compliance with the detailed SBGN specifications. With this effort we hope to increase the adoption of SBGN in bioinformatics tools, ultimately enabling more researchers to visualize biological knowledge in a precise and unambiguous manner.

Availability and implementation: Milestone 2 was released in December 2011. Source code, example files and binaries are freely available under the terms of either the LGPL v2.1+ or Apache v2.0 open source licenses from http://libsbgn.sourceforge.net.

Contact:sbgn-libsbgn@lists.sourceforge.net

Commissioning of heating neutral beams for COMPASS-D tokamak

Two neutral beam injectors have been developed for plasma heating on COMPASS-D tokamak (Institute of Plasma Physics, Prague). The 4-electrodes multihole ion-optical system with beam focusing was chosen to provide the low divergence 300 kW power in both deuterium and hydrogen atoms. The accelerating voltage is 40 kV at extracted ion current up to 15 A. The power supply system provides the continuous and modulated mode of the beam injection at a maximal pulse length 300 ms. The optimal arrangement of the cryopanels and the beam duct elements provides sufficiently short-length beamline which reduces the beam losses. The evolution of the impurities and molecular fraction content is studied in the process of the high voltage conditioning of the newly made ion sources. Two injectors of the same type have been successfully tested and are ready for operation at tokamak in IPP, Prague.

Meeting report from the first meetings of the Computational Modeling in Biology Network (COMBINE)

The Computational Modeling in Biology Network (COMBINE), is an initiative to coordinate the development of the various community standards and formats in computational systems biology and related fields. This report summarizes the activities pursued at the first annual COMBINE meeting held in Edinburgh on October 6-9 2010 and the first HARMONY hackathon, held in New York on April 18-22 2011. The first of those meetings hosted 81 attendees. Discussions covered both official COMBINE standards-(BioPAX, SBGN and SBML), as well as emerging efforts and interoperability between different formats. The second meeting, oriented towards software developers, welcomed 59 participants and witnessed many technical discussions, development of improved standards support in community software systems and conversion between the standards. Both meetings were resounding successes and showed that the field is now mature enough to develop representation formats and related standards in a coordinated manner.

Model-based global sensitivity analysis as applied to identification of anti-cancer drug targets and biomarkers of drug resistance in the ErbB2/3 network

High levels of variability in cancer-related cellular signalling networks and a lack of parameter identifiability in large-scale network models hamper translation of the results of modelling studies into the process of anti-cancer drug development. Recently global sensitivity analysis (GSA) has been recognised as a useful technique, capable of addressing the uncertainty of the model parameters and generating valid predictions on parametric sensitivities. Here we propose a novel implementation of model-based GSA specially designed to explore how multi-parametric network perturbations affect signal propagation through cancer-related networks. We use area-under-the-curve for time course of changes in phosphorylation of proteins as a characteristic for sensitivity analysis and rank network parameters with regard to their impact on the level of key cancer-related outputs, separating strong inhibitory from stimulatory effects. This allows interpretation of the results in terms which can incorporate the effects of potential anti-cancer drugs on targets and the associated biological markers of cancer. To illustrate the method we applied it to an ErbB signalling network model and explored the sensitivity profile of its key model readout, phosphorylated Akt, in the absence and presence of the ErbB2 inhibitor pertuzumab. The method successfully identified the parameters associated with elevation or suppression of Akt phosphorylation in the ErbB2/3 network. From analysis and comparison of the sensitivity profiles of pAkt in the absence and presence of targeted drugs we derived predictions of drug targets, cancer-related biomarkers and generated hypotheses for combinatorial therapy. Several key predictions have been confirmed in experiments using human ovarian carcinoma cell lines. We also compared GSA-derived predictions with the results of local sensitivity analysis and discuss the applicability of both methods. We propose that the developed GSA procedure can serve as a refining tool in combinatorial anti-cancer drug discovery.

Towards a quantitative model of the post-synaptic proteome

The postsynaptic compartment of the excitatory glutamatergic synapse contains hundreds of distinct polypeptides with a wide range of functions (signalling, trafficking, cell-adhesion, etc.). Structural dynamics in the post-synaptic density (PSD) are believed to underpin cognitive processes. Although functionally and morphologically diverse, PSD proteins are generally enriched with specific domains, which precisely define the mode of clustering essential for signal processing. We applied a stochastic calculus of domain binding provided by a rule-based modelling approach to formalise the highly combinatorial signalling pathway in the PSD and perform the numerical analysis of the relative distribution of protein complexes and their sizes. We specified the combinatorics of protein interactions in the PSD by rules, taking into account protein domain structure, specific domain affinity and relative protein availability. With this model we interrogated the critical conditions for the protein aggregation into large complexes and distribution of both size and composition. The presented approach extends existing qualitative protein-protein interaction maps by considering the quantitative information for stoichiometry and binding properties for the elements of the network. This results in a more realistic view of the postsynaptic proteome at the molecular level.

Microarray data can predict diurnal changes of starch content in the picoalga Ostreococcus

Background

The storage of photosynthetic carbohydrate products such as starch is subject to complex regulation, effected at both transcriptional and post-translational levels. The relevant genes in plants show pronounced daily regulation. Their temporal RNA expression profiles, however, do not predict the dynamics of metabolite levels, due to the divergence of enzyme activity from the RNA profiles.

Unicellular phytoplankton retains the complexity of plant carbohydrate metabolism, and recent transcriptomic profiling suggests a major input of transcriptional regulation.

Results

We used a quasi-steady-state, constraint-based modelling approach to infer the dynamics of starch content during the 12 h light/12 h dark cycle in the model alga Ostreococcus tauri. Measured RNA expression datasets from microarray analysis were integrated with a detailed stoichiometric reconstruction of starch metabolism in O. tauri in order to predict the optimal flux distribution and the dynamics of the starch content in the light/dark cycle. The predicted starch profile was validated by experimental data over the 24 h cycle. The main genetic regulatory targets within the pathway were predicted by in silico analysis.

Conclusions

A single-reaction description of starch production is not able to account for the observed variability of diurnal activity profiles of starch-related enzymes. We developed a detailed reaction model of starch metabolism, which, to our knowledge, is the first attempt to describe this polysaccharide polymerization while preserving the mass balance relationships. Our model and method demonstrate the utility of a quasi-steady-state approach for inferring dynamic metabolic information in O. tauri directly from time-series gene expression data.

Efficient oxidative dechlorination and aromatic ring cleavage of chlorinated phenols catalyzed by iron sulfophthalocyanine

An efficient method has been developed for the catalytic oxidation of pollutants that are not easily degraded. The products of the hydrogen peroxide (H(2)O(2)) oxidation of 2,4,6,-trichlorophenol (TCP) catalyzed by the iron complex 2,9,16,23-tetrasulfophthalocyanine (FePcS) were observed to be chloromaleic, chlorofumaric, maleic, and fumaric acids from dechlorination and aromatic cycle cleavage, as well as additional products that resulted from oxidative coupling. Quantitative analysis of the TCP oxidation reaction revealed that up to two chloride ions were released per TCP molecule. This chemical system, consisting of an environmentally safe oxidant (H(2)O(2)) and an easily accessible catalyst (FePcS), can perform several key steps in the oxidative mineralization of TCP, a paradigm of recalcitrant pollutants.

Characterization of 1 MW, 40 keV, 1 s neutral beam for plasma heating

Neutral beam with geometrical focusing for plasma heating in moderate-size plasma devices has been developed in Budker Institute of Nuclear Physics, Novosibirsk. When operated with hydrogen, the neutral beam power is 1 MW, pulse duration is 1 s, beam energy is 40 keV, and angular divergence is 1.2 degrees. Initial ion beam is extracted and accelerated by triode multiapertures ion-optical system. To produce 1 MW neutral beam, about 40 A proton current is extracted with nominal current density of 320 mA/cm(2). Ion-optical system has 200 mm diameter grids with 44% transparency. The grids have inertia cooling and heat is removed between the pulses by water flowing in channels placed on periphery of the grids. A plasma emitter for ion extraction is produced by rf-plasma box. Ion species mix of rf plasma source amounts to 70%, 20%, and 10% of H(+), H(2)(+), and H(3)(+) ions, respectively, by current. Heavy impurities contribute less than 0.3%.

The Systems Biology Graphical Notation

Circuit diagrams and Unified Modeling Language diagrams are just two examples of standard visual languages that help accelerate work by promoting regularity, removing ambiguity and enabling software tool support for communication of complex information. Ironically, despite having one of the highest ratios of graphical to textual information, biology still lacks standard graphical notations. The recent deluge of biological knowledge makes addressing this deficit a pressing concern. Toward this goal, we present the Systems Biology Graphical Notation (SBGN), a visual language developed by a community of biochemists, modelers and computer scientists. SBGN consists of three complementary languages: process diagram, entity relationship diagram and activity flow diagram. Together they enable scientists to represent networks of biochemical interactions in a standard, unambiguous way. We believe that SBGN will foster efficient and accurate representation, visualization, storage, exchange and reuse of information on all kinds of biological knowledge, from gene regulation, to metabolism, to cellular signaling.

The Edinburgh human metabolic network reconstruction and its functional analysis

A better understanding of human metabolism and its relationship with diseases is an important task in human systems biology studies. In this paper, we present a high-quality human metabolic network manually reconstructed by integrating genome annotation information from different databases and metabolic reaction information from literature. The network contains nearly 3000 metabolic reactions, which were reorganized into about 70 human-specific metabolic pathways according to their functional relationships. By analysis of the functional connectivity of the metabolites in the network, the bow-tie structure, which was found previously by structure analysis, is reconfirmed. Furthermore, the distribution of the disease related genes in the network suggests that the IN (substrates) subset of the bow-tie structure has more flexibility than other parts.