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1.  From desk to bed: Computational simulations provide indication for rheumatoid arthritis clinical trials 
BMC Systems Biology  2013;7:10.
Background
Rheumatoid arthritis (RA) is among the most common human systemic autoimmune diseases, affecting approximately 1% of the population worldwide. To date, there is no cure for the disease and current treatments show undesirable side effects. As the disease affects a growing number of individuals, and during their working age, the gathering of all information able to improve therapies -by understanding their and the disease mechanisms of action- represents an important area of research, benefiting not only patients but also societies. In this direction, network analysis methods have been used in previous work to further our understanding of this complex disease, leading to the identification of CRKL as a potential drug target for treatment of RA. Here, we use computational methods to expand on this work, testing the hypothesis in silico.
Results
Analysis of the CRKL network -available at http://www.picb.ac.cn/ClinicalGenomicNTW/software.html- allows for investigation of the potential effect of perturbing genes of interest. Within the group of genes that are significantly affected by simulated perturbation of CRKL, we are lead to further investigate the importance of PXN. Our results allow us to (1) refine the hypothesis on CRKL as a novel drug target (2) indicate potential causes of side effects in on-going trials and (3) importantly, provide recommendations with impact on on-going clinical studies.
Conclusions
Based on a virtual network that collects and connects a large number of the molecules known to be involved in a disease, one can simulate the effects of controlling molecules, allowing for the observation of how this affects the rest of the network. This is important to mimic the effect of a drug, but also to be aware of -and possibly control- its side effects. Using this approach in RA research we have been able to contribute to the field by suggesting molecules to be targeted in new therapies and more importantly, to warrant efficacy, to hypothesise novel recommendations on existing drugs currently under test.
doi:10.1186/1752-0509-7-10
PMCID: PMC3653749  PMID: 23339423
Rheumatoid arthritis; Tyrosine kynase; Simulation modelling; BioLayout express
2.  An S-System Parameter Estimation Method (SPEM) for Biological Networks 
Journal of Computational Biology  2012;19(2):175-187.
Abstract
Advances in experimental biology, coupled with advances in computational power, bring new challenges to the interdisciplinary field of computational biology. One such broad challenge lies in the reverse engineering of gene networks, and goes from determining the structure of static networks, to reconstructing the dynamics of interactions from time series data. Here, we focus our attention on the latter area, and in particular, on parameterizing a dynamic network of oriented interactions between genes. By basing the parameterizing approach on a known power-law relationship model between connected genes (S-system), we are able to account for non-linearity in the network, without compromising the ability to analyze network characteristics. In this article, we introduce the S-System Parameter Estimation Method (SPEM). SPEM, a freely available R software package (http://www.picb.ac.cn/ClinicalGenomicNTW/temp3.html), takes gene expression data in time series and returns the network of interactions as a set of differential equations. The methods, which are presented and tested here, are shown to provide accurate results not only on synthetic data, but more importantly on real and therefore noisy by nature, biological data. In summary, SPEM shows high sensitivity and positive predicted values, as well as free availability and expansibility (because based on open source software). We expect these characteristics to make it a useful and broadly applicable software in the challenging reconstruction of dynamic gene networks.
doi:10.1089/cmb.2011.0269
PMCID: PMC3272242  PMID: 22300319
algorithms; biochemical networks; computational molecular biology; gene networks; graphs and networks; statistics
3.  Functional Dissection of Regulatory Models Using Gene Expression Data of Deletion Mutants 
PLoS Genetics  2013;9(9):e1003757.
Genome-wide gene expression profiles accumulate at an alarming rate, how to integrate these expression profiles generated by different laboratories to reverse engineer the cellular regulatory network has been a major challenge. To automatically infer gene regulatory pathways from genome-wide mRNA expression profiles before and after genetic perturbations, we introduced a new Bayesian network algorithm: Deletion Mutant Bayesian Network (DM_BN). We applied DM_BN to the expression profiles of 544 yeast single or double deletion mutants of transcription factors, chromatin remodeling machinery components, protein kinases and phosphatases in S. cerevisiae. The network inferred by this method identified causal regulatory and non-causal concurrent interactions among these regulators (genetically perturbed genes) that are strongly supported by the experimental evidence, and generated many new testable hypotheses. Compared to networks reconstructed by routine similarity measures or by alternative Bayesian network algorithms, the network inferred by DM_BN excels in both precision and recall. To facilitate its application in other systems, we packaged the algorithm into a user-friendly analysis tool that can be downloaded at http://www.picb.ac.cn/hanlab/DM_BN.html.
Author Summary
The complex functions of a living cell are carried out through hierarchically organized regulatory pathways composed of complex interactions between regulators themselves and between regulators and their targets. Here we developed a Bayesian network inference algorithm, Deletion Mutant Bayesian Network (DM_BN) to reverse engineer the yeast regulatory network based on the hypothesis that components of the same protein complexes or the same regulatory pathways share common target genes. We used this approach to analyze expression profiles of 544 single or double deletion mutants of transcription factors, chromatin remodeling machinery components, protein kinases and phosphatases in S. cerevisiae. The Bayesian network inferred by this method identified causal regulatory relationships and non-causal concurrent interactions among these regulators in different cellular processes, strongly supported by the experimental evidence and generated many testable hypotheses. Compared to networks reconstructed by routine similarity measures or by alternative Bayesian network algorithms, the network inferred by DM_BN excels in both precision and recall. To facilitate its application in other systems, we packaged the algorithm into a user-friendly analysis tool that can be downloaded at http://www.picb.ac.cn/hanlab/DM_BN.html.
doi:10.1371/journal.pgen.1003757
PMCID: PMC3764135  PMID: 24039601
4.  A comprehensive molecular interaction map for Hepatitis B virus and drug designing of a novel inhibitor for Hepatitis B X protein 
Bioinformation  2011;7(1):9-14.
Hepatitis B virus (HBV) infection is a leading source of liver diseases such as hepatitis, cirrhosis and hepatocellular carcinoma. In this study, we use computation methods in order to improve our understanding of the complex interactions that occur between molecules related to Hepatitis B virus (HBV). Due to the complexity of the disease and the numerous molecular players involved, we devised a method to construct a systemic network of interactions of the processes ongoing in patients affected by HBV. The network is based on high-throughput data, refined semi-automatically with carefully curated literature-based information. We find that some nodes in the network that prove to be topologically important, in particular HBx is also known to be important target protein used for the treatment of HBV. Therefore, HBx protein is the preferential choice for inhibition to stop the proteolytic processing. Hence, the 3D structure of HBx protein was downloaded from PDB. Ligands for the active site were designed using LIGBUILDER. The HBx protein's active site was explored to find out the critical interactions pattern for inhibitor binding using molecular docking methodology using AUTODOCK Vina. It should be noted that these predicted data should be validated using suitable assays for further consideration.
PMCID: PMC3163926  PMID: 21904432
Hepatitis B virus; HBx protein; PathVisio; Molecular-interaction map; Virtual screening; Docking; Inhibitor
5.  A Candidate Gene Approach Identifies the TRAF1/C5 Region as a Risk Factor for Rheumatoid Arthritis 
PLoS Medicine  2007;4(9):e278.
Background
Rheumatoid arthritis (RA) is a chronic autoimmune disorder affecting ∼1% of the population. The disease results from the interplay between an individual's genetic background and unknown environmental triggers. Although human leukocyte antigens (HLAs) account for ∼30% of the heritable risk, the identities of non-HLA genes explaining the remainder of the genetic component are largely unknown. Based on functional data in mice, we hypothesized that the immune-related genes complement component 5 (C5) and/or TNF receptor-associated factor 1 (TRAF1), located on Chromosome 9q33–34, would represent relevant candidate genes for RA. We therefore aimed to investigate whether this locus would play a role in RA.
Methods and Findings
We performed a multitiered case-control study using 40 single-nucleotide polymorphisms (SNPs) from the TRAF1 and C5 (TRAF1/C5) region in a set of 290 RA patients and 254 unaffected participants (controls) of Dutch origin. Stepwise replication of significant SNPs was performed in three independent sample sets from the Netherlands (ncases/controls = 454/270), Sweden (ncases/controls = 1,500/1,000) and US (ncases/controls = 475/475). We observed a significant association (p < 0.05) of SNPs located in a haplotype block that encompasses a 65 kb region including the 3′ end of C5 as well as TRAF1. A sliding window analysis revealed an association peak at an intergenic region located ∼10 kb from both C5 and TRAF1. This peak, defined by SNP14/rs10818488, was confirmed in a total of 2,719 RA patients and 1,999 controls (odds ratiocommon = 1.28, 95% confidence interval 1.17–1.39, pcombined = 1.40 × 10−8) with a population-attributable risk of 6.1%. The A (minor susceptibility) allele of this SNP also significantly correlates with increased disease progression as determined by radiographic damage over time in RA patients (p = 0.008).
Conclusions
Using a candidate-gene approach we have identified a novel genetic risk factor for RA. Our findings indicate that a polymorphism in the TRAF1/C5 region increases the susceptibility to and severity of RA, possibly by influencing the structure, function, and/or expression levels of TRAF1 and/or C5.
Using a candidate-gene approach, Rene Toes and colleagues identified a novel genetic risk factor for rheumatoid arthritis in theTRAF1/C5 region.
Editors' Summary
Background.
Rheumatoid arthritis is a very common chronic illness that affects around 1% of people in developed countries. It is caused by an abnormal immune reaction to various tissues within the body; as well as affecting joints and causing an inflammatory arthritis, it can also affect many other organs of the body. Severe rheumatoid arthritis can be life-threatening, but even mild forms of the disease cause substantial illness and disability. Current treatments aim to give symptomatic relief with the use of simple analgesics, or anti-inflammatory drugs. In addition, most patients are also treated with what are known as disease-modifying agents, which aim to prevent joint damage. Rheumatoid arthritis is known to have a genetic component. For example, an association has been shown with the part of the genome that contains the human leukocyte antigens (HLAs), which are involved in the immune response. Information on other genes involved would be helpful both for understanding the underlying cause of the disease and possibly for the discovery of new treatments.
Why Was This Study Done?
Previous work in mice that have a disease similar to human rheumatoid arthritis has identified a number of possible candidate genes. One of these genes, complement component 5 (C5) is involved in the complement system—a primitive system within the body that is involved in the defense against foreign molecules. In humans the gene for C5 is located on Chromosome 9 close to another gene involved in the inflammatory response, TNF receptor-associated factor 1 (TRAF1). A preliminary study in humans of this region had shown some evidence, albeit weak, to suggest that this region might be associated with rheumatoid arthritis. The authors set out to look in more detail, and in a larger group of individuals, to see if they could prove this association.
What Did the Researchers Do and Find?
The researchers took 40 genetic markers, known as single-nucleotide polymorphisms (SNPs), from across the region that included the C5 and TRAF1 genes. SNPs have each been assigned a unique reference number that specifies a point in the human genome, and each is present in alternate forms so can be differentiated. They compared which of the alternate forms were present in 290 patients with rheumatoid arthritis and 254 unaffected participants of Dutch origin. They then repeated the study in three other groups of patients and controls of Dutch, Swedish, and US origin. They found a consistent association with rheumatoid arthritis of one region of 65 kilobases (a small distance in genetic terms) that included one end of the C5 gene as well as the TRAF1 gene. They could refine the area of interest to a piece marked by one particular SNP that lay between the genes. They went on to show that the genetic region in which these genes are located may be involved in the binding of a protein that modifies the transcription of genes, thus providing a possible explanation for the association. Furthermore, they showed that one of the alternate versions of the marker in this region was associated with more aggressive disease.
What Do These Findings Mean?
The finding of a genetic association is the first step in identifying a genetic component of a disease. The strength of this study is that a novel genetic susceptibility factor for RA has been identified and that the overall result is consistent in four different populations as well as being associated with disease severity. Further work will need to be done to confirm the association in other populations and then to identify the precise genetic change involved. Hopefully this work will lead to new avenues of investigation for therapy.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0040278.
• Medline Plus, the health information site for patients from the US National Library of Medicine, has a page of resources on rheumatoid arthritis
• The UK's National Health Service online information site has information on rheumatoid arthritis
• The Arthritis Research Campaign, a UK charity that funds research on all types of arthritis, has a booklet with information for patients on rheumatoid arthritis
• Reumafonds, a Dutch arthritis foundation, gives information on rheumatoid arthritis (in Dutch)
• Autocure is an initiative whose objective is to transform knowledge obtained from molecular research into a cure for an increasing number of patients suffering from inflammatory rheumatic diseases
• The European league against Rheumatism, an organisation which represents the patient, health professionals, and scientific societies of rheumatology of all European nations
doi:10.1371/journal.pmed.0040278
PMCID: PMC1976626  PMID: 17880261
6.  Detecting and Removing Inconsistencies between Experimental Data and Signaling Network Topologies Using Integer Linear Programming on Interaction Graphs 
PLoS Computational Biology  2013;9(9):e1003204.
Cross-referencing experimental data with our current knowledge of signaling network topologies is one central goal of mathematical modeling of cellular signal transduction networks. We present a new methodology for data-driven interrogation and training of signaling networks. While most published methods for signaling network inference operate on Bayesian, Boolean, or ODE models, our approach uses integer linear programming (ILP) on interaction graphs to encode constraints on the qualitative behavior of the nodes. These constraints are posed by the network topology and their formulation as ILP allows us to predict the possible qualitative changes (up, down, no effect) of the activation levels of the nodes for a given stimulus. We provide four basic operations to detect and remove inconsistencies between measurements and predicted behavior: (i) find a topology-consistent explanation for responses of signaling nodes measured in a stimulus-response experiment (if none exists, find the closest explanation); (ii) determine a minimal set of nodes that need to be corrected to make an inconsistent scenario consistent; (iii) determine the optimal subgraph of the given network topology which can best reflect measurements from a set of experimental scenarios; (iv) find possibly missing edges that would improve the consistency of the graph with respect to a set of experimental scenarios the most. We demonstrate the applicability of the proposed approach by interrogating a manually curated interaction graph model of EGFR/ErbB signaling against a library of high-throughput phosphoproteomic data measured in primary hepatocytes. Our methods detect interactions that are likely to be inactive in hepatocytes and provide suggestions for new interactions that, if included, would significantly improve the goodness of fit. Our framework is highly flexible and the underlying model requires only easily accessible biological knowledge. All related algorithms were implemented in a freely available toolbox SigNetTrainer making it an appealing approach for various applications.
Author Summary
Cellular signal transduction is orchestrated by communication networks of signaling proteins commonly depicted on signaling pathway maps. However, each cell type may have distinct variants of signaling pathways, and wiring diagrams are often altered in disease states. The identification of truly active signaling topologies based on experimental data is therefore one key challenge in systems biology of cellular signaling. We present a new framework for training signaling networks based on interaction graphs (IG). In contrast to complex modeling formalisms, IG capture merely the known positive and negative edges between the components. This basic information, however, already sets hard constraints on the possible qualitative behaviors of the nodes when perturbing the network. Our approach uses Integer Linear Programming to encode these constraints and to predict the possible changes (down, neutral, up) of the activation levels of the involved players for a given experiment. Based on this formulation we developed several algorithms for detecting and removing inconsistencies between measurements and network topology. Demonstrated by EGFR/ErbB signaling in hepatocytes, our approach delivers direct conclusions on edges that are likely inactive or missing relative to canonical pathway maps. Such information drives the further elucidation of signaling network topologies under normal and pathological phenotypes.
doi:10.1371/journal.pcbi.1003204
PMCID: PMC3764019  PMID: 24039561
7.  A comprehensive map of the influenza A virus replication cycle 
BMC Systems Biology  2013;7:97.
Background
Influenza is a common infectious disease caused by influenza viruses. Annual epidemics cause severe illnesses, deaths, and economic loss around the world. To better defend against influenza viral infection, it is essential to understand its mechanisms and associated host responses. Many studies have been conducted to elucidate these mechanisms, however, the overall picture remains incompletely understood. A systematic understanding of influenza viral infection in host cells is needed to facilitate the identification of influential host response mechanisms and potential drug targets.
Description
We constructed a comprehensive map of the influenza A virus (‘IAV’) life cycle (‘FluMap’) by undertaking a literature-based, manual curation approach. Based on information obtained from publicly available pathway databases, updated with literature-based information and input from expert virologists and immunologists, FluMap is currently composed of 960 factors (i.e., proteins, mRNAs etc.) and 456 reactions, and is annotated with ~500 papers and curation comments. In addition to detailing the type of molecular interactions, isolate/strain specific data are also available. The FluMap was built with the pathway editor CellDesigner in standard SBML (Systems Biology Markup Language) format and visualized as an SBGN (Systems Biology Graphical Notation) diagram. It is also available as a web service (online map) based on the iPathways+ system to enable community discussion by influenza researchers. We also demonstrate computational network analyses to identify targets using the FluMap.
Conclusion
The FluMap is a comprehensive pathway map that can serve as a graphically presented knowledge-base and as a platform to analyze functional interactions between IAV and host factors. Publicly available webtools will allow continuous updating to ensure the most reliable representation of the host-virus interaction network. The FluMap is available at http://www.influenza-x.org/flumap/.
doi:10.1186/1752-0509-7-97
PMCID: PMC3819658  PMID: 24088197
Drug targets; FluMap; Host factors; Influenza virus; Pathways
8.  Understanding network concepts in modules 
BMC Systems Biology  2007;1:24.
Background
Network concepts are increasingly used in biology and genetics. For example, the clustering coefficient has been used to understand network architecture; the connectivity (also known as degree) has been used to screen for cancer targets; and the topological overlap matrix has been used to define modules and to annotate genes. Dozens of potentially useful network concepts are known from graph theory.
Results
Here we study network concepts in special types of networks, which we refer to as approximately factorizable networks. In these networks, the pairwise connection strength (adjacency) between 2 network nodes can be factored into node specific contributions, named node 'conformity'. The node conformity turns out to be highly related to the connectivity. To provide a formalism for relating network concepts to each other, we define three types of network concepts: fundamental-, conformity-based-, and approximate conformity-based concepts. Fundamental concepts include the standard definitions of connectivity, density, centralization, heterogeneity, clustering coefficient, and topological overlap. The approximate conformity-based analogs of fundamental network concepts have several theoretical advantages. First, they allow one to derive simple relationships between seemingly disparate networks concepts. For example, we derive simple relationships between the clustering coefficient, the heterogeneity, the density, the centralization, and the topological overlap. The second advantage of approximate conformity-based network concepts is that they allow one to show that fundamental network concepts can be approximated by simple functions of the connectivity in module networks.
Conclusion
Using protein-protein interaction, gene co-expression, and simulated data, we show that a) many networks comprised of module nodes are approximately factorizable and b) in these types of networks, simple relationships exist between seemingly disparate network concepts. Our results are implemented in freely available R software code, which can be downloaded from the following webpage: http://www.genetics.ucla.edu/labs/horvath/ModuleConformity/ModuleNetworks
doi:10.1186/1752-0509-1-24
PMCID: PMC3238286  PMID: 17547772
9.  Phylogenetic Molecular Ecological Network of Soil Microbial Communities in Response to Elevated CO2 
mBio  2011;2(4):e00122-11.
ABSTRACT
Understanding the interactions among different species and their responses to environmental changes, such as elevated atmospheric concentrations of CO2, is a central goal in ecology but is poorly understood in microbial ecology. Here we describe a novel random matrix theory (RMT)-based conceptual framework to discern phylogenetic molecular ecological networks using metagenomic sequencing data of 16S rRNA genes from grassland soil microbial communities, which were sampled from a long-term free-air CO2 enrichment experimental facility at the Cedar Creek Ecosystem Science Reserve in Minnesota. Our experimental results demonstrated that an RMT-based network approach is very useful in delineating phylogenetic molecular ecological networks of microbial communities based on high-throughput metagenomic sequencing data. The structure of the identified networks under ambient and elevated CO2 levels was substantially different in terms of overall network topology, network composition, node overlap, module preservation, module-based higher-order organization, topological roles of individual nodes, and network hubs, suggesting that the network interactions among different phylogenetic groups/populations were markedly changed. Also, the changes in network structure were significantly correlated with soil carbon and nitrogen contents, indicating the potential importance of network interactions in ecosystem functioning. In addition, based on network topology, microbial populations potentially most important to community structure and ecosystem functioning can be discerned. The novel approach described in this study is important not only for research on biodiversity, microbial ecology, and systems microbiology but also for microbial community studies in human health, global change, and environmental management.
IMPORTANCE
The interactions among different microbial populations in a community play critical roles in determining ecosystem functioning, but very little is known about the network interactions in a microbial community, owing to the lack of appropriate experimental data and computational analytic tools. High-throughput metagenomic technologies can rapidly produce a massive amount of data, but one of the greatest difficulties is deciding how to extract, analyze, synthesize, and transform such a vast amount of information into biological knowledge. This study provides a novel conceptual framework to identify microbial interactions and key populations based on high-throughput metagenomic sequencing data. This study is among the first to document that the network interactions among different phylogenetic populations in soil microbial communities were substantially changed by a global change such as an elevated CO2 level. The framework developed will allow microbiologists to address research questions which could not be approached previously, and hence, it could represent a new direction in microbial ecology research.
doi:10.1128/mBio.00122-11
PMCID: PMC3143843  PMID: 21791581
10.  A Genomewide Functional Network for the Laboratory Mouse 
PLoS Computational Biology  2008;4(9):e1000165.
Establishing a functional network is invaluable to our understanding of gene function, pathways, and systems-level properties of an organism and can be a powerful resource in directing targeted experiments. In this study, we present a functional network for the laboratory mouse based on a Bayesian integration of diverse genetic and functional genomic data. The resulting network includes probabilistic functional linkages among 20,581 protein-coding genes. We show that this network can accurately predict novel functional assignments and network components and present experimental evidence for predictions related to Nanog homeobox (Nanog), a critical gene in mouse embryonic stem cell pluripotency. An analysis of the global topology of the mouse functional network reveals multiple biologically relevant systems-level features of the mouse proteome. Specifically, we identify the clustering coefficient as a critical characteristic of central modulators that affect diverse pathways as well as genes associated with different phenotype traits and diseases. In addition, a cross-species comparison of functional interactomes on a genomic scale revealed distinct functional characteristics of conserved neighborhoods as compared to subnetworks specific to higher organisms. Thus, our global functional network for the laboratory mouse provides the community with a key resource for discovering protein functions and novel pathway components as well as a tool for exploring systems-level topological and evolutionary features of cellular interactomes. To facilitate exploration of this network by the biomedical research community, we illustrate its application in function and disease gene discovery through an interactive, Web-based, publicly available interface at http://mouseNET.princeton.edu.
Author Summary
Functionally related proteins interact in diverse ways to carry out biological processes, and each protein often participates in multiple pathways. Proteins are therefore organized into a complex network through which different functions of the cell are carried out. An accurate description of such a network is invaluable to our understanding of both the system-level features of a cell and those of an individual biological process. In this study, we used a probabilistic model to combine information from diverse genome-scale studies as well as individual investigations to generate a global functional network for mouse. Our analysis of the global topology of this network reveals biologically relevant systems-level characteristics of the mouse proteome, including conservation of functional neighborhoods and network features characteristic of known disease genes and key transcriptional regulators. We have made this network publicly available for search and dynamic exploration by researchers in the community. Our Web interface enables users to easily generate hypotheses regarding potential functional roles of uncharacterized proteins, investigate possible links between their proteins of interest and disease, and identify new players in specific biological processes.
doi:10.1371/journal.pcbi.1000165
PMCID: PMC2527685  PMID: 18818725
11.  SBMLsqueezer: A CellDesigner plug-in to generate kinetic rate equations for biochemical networks 
BMC Systems Biology  2008;2:39.
Background
The development of complex biochemical models has been facilitated through the standardization of machine-readable representations like SBML (Systems Biology Markup Language). This effort is accompanied by the ongoing development of the human-readable diagrammatic representation SBGN (Systems Biology Graphical Notation). The graphical SBML editor CellDesigner allows direct translation of SBGN into SBML, and vice versa. For the assignment of kinetic rate laws, however, this process is not straightforward, as it often requires manual assembly and specific knowledge of kinetic equations.
Results
SBMLsqueezer facilitates exactly this modeling step via automated equation generation, overcoming the highly error-prone and cumbersome process of manually assigning kinetic equations. For each reaction the kinetic equation is derived from the stoichiometry, the participating species (e.g., proteins, mRNA or simple molecules) as well as the regulatory relations (activation, inhibition or other modulations) of the SBGN diagram. Such information allows distinctions between, for example, translation, phosphorylation or state transitions. The types of kinetics considered are numerous, for instance generalized mass-action, Hill, convenience and several Michaelis-Menten-based kinetics, each including activation and inhibition. These kinetics allow SBMLsqueezer to cover metabolic, gene regulatory, signal transduction and mixed networks. Whenever multiple kinetics are applicable to one reaction, parameter settings allow for user-defined specifications. After invoking SBMLsqueezer, the kinetic formulas are generated and assigned to the model, which can then be simulated in CellDesigner or with external ODE solvers. Furthermore, the equations can be exported to SBML, LaTeX or plain text format.
Conclusion
SBMLsqueezer considers the annotation of all participating reactants, products and regulators when generating rate laws for reactions. Thus, for each reaction, only applicable kinetic formulas are considered. This modeling scheme creates kinetics in accordance with the diagrammatic representation. In contrast most previously published tools have relied on the stoichiometry and generic modulators of a reaction, thus ignoring and potentially conflicting with the information expressed through the process diagram. Additional material and the source code can be found at the project homepage (URL found in the Availability and requirements section).
doi:10.1186/1752-0509-2-39
PMCID: PMC2412839  PMID: 18447902
12.  Functional Inference of Complex Anatomical Tendinous Networks at a Macroscopic Scale via Sparse Experimentation 
PLoS Computational Biology  2012;8(11):e1002751.
In systems and computational biology, much effort is devoted to functional identification of systems and networks at the molecular-or cellular scale. However, similarly important networks exist at anatomical scales such as the tendon network of human fingers: the complex array of collagen fibers that transmits and distributes muscle forces to finger joints. This network is critical to the versatility of the human hand, and its function has been debated since at least the 16th century. Here, we experimentally infer the structure (both topology and parameter values) of this network through sparse interrogation with force inputs. A population of models representing this structure co-evolves in simulation with a population of informative future force inputs via the predator-prey estimation-exploration algorithm. Model fitness depends on their ability to explain experimental data, while the fitness of future force inputs depends on causing maximal functional discrepancy among current models. We validate our approach by inferring two known synthetic Latex networks, and one anatomical tendon network harvested from a cadaver's middle finger. We find that functionally similar but structurally diverse models can exist within a narrow range of the training set and cross-validation errors. For the Latex networks, models with low training set error [<4%] and resembling the known network have the smallest cross-validation errors [∼5%]. The low training set [<4%] and cross validation [<7.2%] errors for models for the cadaveric specimen demonstrate what, to our knowledge, is the first experimental inference of the functional structure of complex anatomical networks. This work expands current bioinformatics inference approaches by demonstrating that sparse, yet informative interrogation of biological specimens holds significant computational advantages in accurate and efficient inference over random testing, or assuming model topology and only inferring parameters values. These findings also hold clues to both our evolutionary history and the development of versatile machines.
Author Summary
In science and medicine alike, one of the critical steps to understand the working of organisms is to identify how a given individual is similar or different from others. Only then can the specific features of an individual be distinguished from the general properties of that species. However, doing enough input-output experiments on a given organism to obtain a reliable description of its function (i.e., a model) can often harm the organism, or require too much time when testing perishable tissues or human subjects. We have met this challenge by demonstrating that our novel algorithm can accelerate the extraction of accurate functional models in complex tissues by continually tailoring each successive experiment to be more informative. We apply this new method to the problem of describing how the tendons of the fingers interact, which has puzzled scientists and clinicians since the time of Da Vinci. This new computational-experimental method now enables fresh research directions in biological and medical research by allowing the experimental extraction of accurate functional models with minimal damage to the organism. For example, it will allow a better understanding of similarities and differences among related species, and the development of personalized medical treatment.
doi:10.1371/journal.pcbi.1002751
PMCID: PMC3493461  PMID: 23144601
13.  BiNoM 2.0, a Cytoscape plugin for accessing and analyzing pathways using standard systems biology formats 
BMC Systems Biology  2013;7:18.
Background
Public repositories of biological pathways and networks have greatly expanded in recent years. Such databases contain many pathways that facilitate the analysis of high-throughput experimental work and the formulation of new biological hypotheses to be tested, a fundamental principle of the systems biology approach. However, large-scale molecular maps are not always easy to mine and interpret.
Results
We have developed BiNoM (Biological Network Manager), a Cytoscape plugin, which provides functions for the import-export of some standard systems biology file formats (import from CellDesigner, BioPAX Level 3 and CSML; export to SBML, CellDesigner and BioPAX Level 3), and a set of algorithms to analyze and reduce the complexity of biological networks. BiNoM can be used to import and analyze files created with the CellDesigner software. BiNoM provides a set of functions allowing to import BioPAX files, but also to search and edit their content. As such, BiNoM is able to efficiently manage large BioPAX files such as whole pathway databases (e.g. Reactome). BiNoM also implements a collection of powerful graph-based functions and algorithms such as path analysis, decomposition by involvement of an entity or cyclic decomposition, subnetworks clustering and decomposition of a large network in modules.
Conclusions
Here, we provide an in-depth overview of the BiNoM functions, and we also detail novel aspects such as the support of the BioPAX Level 3 format and the implementation of a new algorithm for the quantification of pathways for influence networks. At last, we illustrate some of the BiNoM functions on a detailed biological case study of a network representing the G1/S transition of the cell cycle, a crucial cellular process disturbed in most human tumors.
doi:10.1186/1752-0509-7-18
PMCID: PMC3646686  PMID: 23453054
Systems biology; Cytoscape; Software; SBML; BioPAX; CellDesigner; Conversion; SBGN; Reactome; Network analysis; Path analysis; Molecular maps; Pathways
14.  Ct3d: tracking microglia motility in 3D using a novel cosegmentation approach 
Bioinformatics  2010;27(4):564-571.
Motivation: Cell tracking is an important method to quantitatively analyze time-lapse microscopy data. While numerous methods and tools exist for tracking cells in 2D time-lapse images, only few and very application-specific tracking tools are available for 3D time-lapse images, which is of high relevance in immunoimaging, in particular for studying the motility of microglia in vivo.
Results: We introduce a novel algorithm for tracking cells in 3D time-lapse microscopy data, based on computing cosegmentations between component trees representing individual time frames using the so-called tree-assignments. For the first time, our method allows to track microglia in three dimensional confocal time-lapse microscopy images. We also evaluate our method on synthetically generated data, demonstrating that our algorithm is robust even in the presence of different types of inhomogeneous background noise.
Availability: Our algorithm is implemented in the ct3d package, which is available under http://www.picb.ac.cn/patterns/Software/ct3d; supplementary videos are available from http://www.picb.ac.cn/patterns/Supplements/ct3d.
Contact: axel@picb.ac.cn; forestdu@ion.ac.cn
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btq691
PMCID: PMC3035800  PMID: 21186244
15.  Scale-space measures for graph topology link protein network architecture to function 
Bioinformatics  2014;30(12):i237-i245.
Motivation: The network architecture of physical protein interactions is an important determinant for the molecular functions that are carried out within each cell. To study this relation, the network architecture can be characterized by graph topological characteristics such as shortest paths and network hubs. These characteristics have an important shortcoming: they do not take into account that interactions occur across different scales. This is important because some cellular functions may involve a single direct protein interaction (small scale), whereas others require more and/or indirect interactions, such as protein complexes (medium scale) and interactions between large modules of proteins (large scale).
Results: In this work, we derive generalized scale-aware versions of known graph topological measures based on diffusion kernels. We apply these to characterize the topology of networks across all scales simultaneously, generating a so-called graph topological scale-space. The comprehensive physical interaction network in yeast is used to show that scale-space based measures consistently give superior performance when distinguishing protein functional categories and three major types of functional interactions—genetic interaction, co-expression and perturbation interactions. Moreover, we demonstrate that graph topological scale spaces capture biologically meaningful features that provide new insights into the link between function and protein network architecture.
Availability and implementation: MatlabTM code to calculate the scale-aware topological measures (STMs) is available at http://bioinformatics.tudelft.nl/TSSA
Contact: j.deridder@tudelft.nl
Supplementary information: Supplementary data are available at Bioinformatics online.
doi:10.1093/bioinformatics/btu283
PMCID: PMC4058939  PMID: 24931989
16.  Computational Identification of Phospho-Tyrosine Sub-Networks Related to Acanthocyte Generation in Neuroacanthocytosis 
PLoS ONE  2012;7(2):e31015.
Acanthocytes, abnormal thorny red blood cells (RBC), are one of the biological hallmarks of neuroacanthocytosis syndromes (NA), a group of rare hereditary neurodegenerative disorders. Since RBCs are easily accessible, the study of acanthocytes in NA may provide insights into potential mechanisms of neurodegeneration. Previous studies have shown that changes in RBC membrane protein phosphorylation state affect RBC membrane mechanical stability and morphology. Here, we coupled tyrosine-phosphoproteomic analysis to topological network analysis. We aimed to predict signaling sub-networks possibly involved in the generation of acanthocytes in patients affected by the two core NA disorders, namely McLeod syndrome (MLS, XK-related, Xk protein) and chorea-acanthocytosis (ChAc, VPS13A-related, chorein protein). The experimentally determined phosphoproteomic data-sets allowed us to relate the subsequent network analysis to the pathogenetic background. To reduce the network complexity, we combined several algorithms of topological network analysis including cluster determination by shortest path analysis, protein categorization based on centrality indexes, along with annotation-based node filtering. We first identified XK- and VPS13A-related protein-protein interaction networks by identifying all the interactomic shortest paths linking Xk and chorein to the corresponding set of proteins whose tyrosine phosphorylation was altered in patients. These networks include the most likely paths of functional influence of Xk and chorein on phosphorylated proteins. We further refined the analysis by extracting restricted sets of highly interacting signaling proteins representing a common molecular background bridging the generation of acanthocytes in MLS and ChAc. The final analysis pointed to a novel, very restricted, signaling module of 14 highly interconnected kinases, whose alteration is possibly involved in generation of acanthocytes in MLS and ChAc.
doi:10.1371/journal.pone.0031015
PMCID: PMC3280254  PMID: 22355334
17.  Network target for screening synergistic drug combinations with application to traditional Chinese medicine 
BMC Systems Biology  2011;5(Suppl 1):S10.
Background
Multicomponent therapeutics offer bright prospects for the control of complex diseases in a synergistic manner. However, finding ways to screen the synergistic combinations from numerous pharmacological agents is still an ongoing challenge.
Results
In this work, we proposed for the first time a “network target”-based paradigm instead of the traditional "single target"-based paradigm for virtual screening and established an algorithm termed NIMS (Network target-based Identification of Multicomponent Synergy) to prioritize synergistic agent combinations in a high throughput way. NIMS treats a disease-specific biological network as a therapeutic target and assumes that the relationship among agents can be transferred to network interactions among the molecular level entities (targets or responsive gene products) of agents. Then, two parameters in NIMS, Topology Score and Agent Score, are created to evaluate the synergistic relationship between each given agent combinations. Taking the empirical multicomponent system traditional Chinese medicine (TCM) as an illustrative case, we applied NIMS to prioritize synergistic agent pairs from 63 agents on a pathological process instanced by angiogenesis. The NIMS outputs can not only recover five known synergistic agent pairs, but also obtain experimental verification for synergistic candidates combined with, for example, a herbal ingredient Sinomenine, which outperforms the meet/min method. The robustness of NIMS was also showed regarding the background networks, agent genes and topological parameters, respectively. Finally, we characterized the potential mechanisms of multicomponent synergy from a network target perspective.
Conclusions
NIMS is a first-step computational approach towards identification of synergistic drug combinations at the molecular level. The network target-based approaches may adjust current virtual screen mode and provide a systematic paradigm for facilitating the development of multicomponent therapeutics as well as the modernization of TCM.
doi:10.1186/1752-0509-5-S1-S10
PMCID: PMC3121110  PMID: 21689469
18.  Ectopic Lymphoid Structures Support Ongoing Production of Class-Switched Autoantibodies in Rheumatoid Synovium 
PLoS Medicine  2009;6(1):e1.
Background
Follicular structures resembling germinal centres (GCs) that are characterized by follicular dendritic cell (FDC) networks have long been recognized in chronically inflamed tissues in autoimmune diseases, including the synovium of rheumatoid arthritis (RA). However, it is debated whether these ectopic structures promote autoimmunity and chronic inflammation driving the production of pathogenic autoantibodies. Anti-citrullinated protein/peptide antibodies (ACPA) are highly specific markers of RA, predict a poor prognosis, and have been suggested to be pathogenic. Therefore, the main study objectives were to determine whether ectopic lymphoid structures in RA synovium: (i) express activation-induced cytidine deaminase (AID), the enzyme required for somatic hypermutation and class-switch recombination (CSR) of Ig genes; (ii) support ongoing CSR and ACPA production; and (iii) remain functional in a RA/severe combined immunodeficiency (SCID) chimera model devoid of new immune cell influx into the synovium.
Methods and Findings
Using immunohistochemistry (IHC) and quantitative Taqman real-time PCR (QT-PCR) in synovial tissue from 55 patients with RA, we demonstrated that FDC+ structures invariably expressed AID with a distribution resembling secondary lymphoid organs. Further, AID+/CD21+ follicular structures were surrounded by ACPA+/CD138+ plasma cells, as demonstrated by immune reactivity to citrullinated fibrinogen. Moreover, we identified a novel subset of synovial AID+/CD20+ B cells outside GCs resembling interfollicular large B cells. In order to gain direct functional evidence that AID+ structures support CSR and in situ manufacturing of class-switched ACPA, 34 SCID mice were transplanted with RA synovium and humanely killed at 4 wk for harvesting of transplants and sera. Persistent expression of AID and Iγ-Cμ circular transcripts (identifying ongoing IgM-IgG class-switching) was observed in synovial grafts expressing FDCs/CD21L. Furthermore, synovial mRNA levels of AID were closely associated with circulating human IgG ACPA in mouse sera. Finally, the survival and proliferation of functional B cell niches was associated with persistent overexpression of genes regulating ectopic lymphoneogenesis.
Conclusions
Our demonstration that FDC+ follicular units invariably express AID and are surrounded by ACPA-producing plasma cells provides strong evidence that ectopic lymphoid structures in the RA synovium are functional and support autoantibody production. This concept is further confirmed by evidence of sustained AID expression, B cell proliferation, ongoing CSR, and production of human IgG ACPA from GC+ synovial tissue transplanted into SCID mice, independently of new B cell influx from the systemic circulation. These data identify AID as a potential therapeutic target in RA and suggest that survival of functional synovial B cell niches may profoundly influence chronic inflammation, autoimmunity, and response to B cell–depleting therapies.
Costantino Pitzalis and colleagues show that lymphoid structures in synovial tissue of patients with rheumatoid arthritis support production of anti-citrullinated peptide antibodies, which continues following transplantation into SCID mice.
Editors' Summary
Background.
More than 1 million people in the United States have rheumatoid arthritis, an “autoimmune” condition that affects the joints. Normally, the immune system provides protection against infection by responding to foreign antigens (molecules that are unique to invading organisms) while ignoring self-antigens present in the body's own tissues. In autoimmune diseases, this ability to discriminate between self and non-self fails for unknown reasons and the immune system begins to attack human tissues. In rheumatoid arthritis, the lining of the joints (the synovium) is attacked, it becomes inflamed and thickened, and chemicals are released that damage all the tissues in the joint. Eventually, the joint may become so scarred that movement is no longer possible. Rheumatoid arthritis usually starts in the small joints in the hands and feet, but larger joints and other tissues (including the heart and blood vessels) can be affected. Its symptoms, which tend to fluctuate, include early morning joint pain, swelling, and stiffness, and feeling generally unwell. Although the disease is not always easy to diagnose, the immune systems of many people with rheumatoid arthritis make “anti-citrullinated protein/peptide antibodies” (ACPA). These “autoantibodies” (which some experts believe can contribute to the joint damage in rheumatoid arthritis) recognize self-proteins that contain the unusual amino acid citrulline, and their detection on blood tests can help make the diagnosis. Although there is no cure for rheumatoid arthritis, the recently developed biologic drugs, often used together with the more traditional disease-modifying therapies, are able to halt its progression by specifically blocking the chemicals that cause joint damage. Painkillers and nonsteroidal anti-inflammatory drugs can reduce its symptoms, and badly damaged joints can sometimes be surgically replaced.
Why Was This Study Done?
Before scientists can develop a cure for rheumatoid arthritis, they need to know how and why autoantibodies are made that attack the joints in this common and disabling disease. B cells, the immune system cells that make antibodies, mature in structures known as “germinal centers” in the spleen and lymph nodes. In the germinal centers, immature B cells are exposed to antigens and undergo two genetic processes called “somatic hypermutation” and “class-switch recombination” that ensure that each B cell makes an antibody that sticks as tightly as possible to just one antigen. The B cells then multiply and enter the bloodstream where they help to deal with infections. Interestingly, the inflamed synovium of many patients with rheumatoid arthritis contains structures that resemble germinal centers. Could these ectopic (misplaced) lymphoid structures, which are characterized by networks of immune system cells called follicular dendritic cells (FDCs), promote autoimmunity and long-term inflammation by driving the production of autoantibodies within the joint itself? In this study, the researchers investigate this possibility.
What Did the Researchers Do and Find?
The researchers collected synovial tissue from 55 patients with rheumatoid arthritis and used two approaches, called immunohistochemistry and real-time PCR, to investigate whether FDC-containing structures in synovium expressed an enzyme called activation-induced cytidine deaminase (AID), which is needed for both somatic hypermutation and class-switch recombination. All the FDC-containing structures that the researchers found in their samples expressed AID. Furthermore, these AID-containing structures were surrounded by mature B cells making ACPAs. To test whether these B cells were derived from AID-expressing cells resident in the synovium rather than ACPA-expressing immune system cells coming into the synovium from elsewhere in the body, the researchers transplanted synovium from patients with rheumatoid arthritis under the skin of a special sort of mouse that largely lacks its own immune system. Four weeks later, the researchers found that the transplanted human lymphoid tissue was still making AID, that the level of AID expression correlated with the amount of human ACPA in the blood of the mice, and that the B cells in the transplant were proliferating.
What Do These Findings Mean?
These findings show that the ectopic lymphoid structures present in the synovium of some patients with rheumatoid arthritis are functional and are able to make ACPA. Because ACPA may be responsible for joint damage, the survival of these structures could, therefore, be involved in the development and progression of rheumatoid arthritis. More experiments are needed to confirm this idea, but these findings may explain why drugs that effectively clear B cells from the bloodstream do not always produce a marked clinical improvement in rheumatoid arthritis. Finally, they suggest that AID might provide a new target for the development of drugs to treat rheumatoid arthritis.
Additional Information.
Please access these Web sites via the online version of this summary at http://dx.doi.org/10.1371/journal.pmed.0060001.
This study is further discussed in a PLoS Medicine Perspective by Rene Toes and Tom Huizinga
The MedlinePlus Encyclopedia has a page on rheumatoid arthritis (in English and Spanish). MedlinePlus provides links to other information on rheumatoid arthritis (in English and Spanish)
The UK National Health Service Choices information service has detailed information on rheumatoid arthritis
The US National Institute of Arthritis and Musculoskeletal and Skin Diseases provides Fast Facts, an easy to read publication for the public, and a more detailed Handbook on rheumatoid arthritis
The US Centers for Disease Control and Prevention has an overview on rheumatoid arthritis that includes statistics about this disease and its impact on daily life
doi:10.1371/journal.pmed.0060001
PMCID: PMC2621263  PMID: 19143467
19.  Chemical combination effects predict connectivity in biological systems 
Chemical synergies can be novel probes of biological systems.Simulated response shapes depend on target connectivity in a pathway.Experiments with yeast and cancer cells confirm simulated effects.Profiles across many combinations yield target location information.
Living organisms are built of interacting components, whose function and dysfunction can be described through dynamic network models (Davidson et al, 2002). Systems Biology involves the iterative construction of such models (Ideker et al, 2001), and may eventually improve the understanding of diseases using in silico simulations. Such simulations may eventually permit drugs to be prioritized for clinical trials, reducing potential risks and increasing the likelihood of successful outcomes. Given the complexity of biological systems, constructing realistic models will require large and diverse sets of connectivity data.
Chemical combinations provide a new window into biological connectivity. Information gleaned from targeted combinations, such as paired mutations (Tong et al, 2004), has proven to be especially useful for revealing functional interactions between components. We have been screening chemical combinations for therapeutic synergies (Borisy et al, 2003; Zimmermann et al, 2007), collecting full-dose matrices where combinations are tested in all possible pairings of serially diluted single agent doses (Figure 1). Such screens yield a variety of response surfaces with distinct shapes for combinations that work through different known mechanisms, suggesting that combination effects may contain information on the nature of functional connections between drug targets.
Simulations of biological pathways predict synergistic responses to inhibitors that depend on target connectivity. We explored theoretical predictions by simulating a metabolic pathway with pairs of inhibitors aimed at different targets with varying doses. We found that the shape of each combination response depended on how the inhibitor pair's targets were connected in the pathway (Figure 2). The predicted response shapes were robust to plausible variations in the simulated pathway that did not affect the network topology (e.g., kinetic assumptions, parameter values, and nonlinear response functions), but were very sensitive to topological alterations in the modelled network (e.g., feedback regulation or changing the type of junction at a branch point). These findings suggest that connectivity of the inhibitor targets has a major influence on combination response morphology.
The predicted shapes were experimentally confirmed in yeast combination experiments. The proliferation experiment used drugs focused on the sterol biosynthesis pathway, which is mostly linear between the targets covered in this study, and is known to be regulated by negative feedback (Gardner et al, 2001). The combinations between sterol inhibitors confirmed expectations from our simulations, showing dose-additive responses for pairs targeting the same enzyme and strong synergies across enzymes of the shape predicted in our simulations for linear pathways under negative feedback. Combinations across pathways showed much more variable responses with a trend towards less synergy on average.
Further experimental support was obtained from human cells. A combination screen of 90 annotated drugs in a human tumour cell line (HCT116) proliferation assay produced strong synergies for combinations within pathways and more variable effects between targeted functions. Synergy profiles (sets of all synergy scores involving each drug) also showed a greater degree of similarity for pairs of drugs with related targets. Finally, the most extreme outliers were dominated by inhibitors of kinases that are especially critical for HCT116 proliferation (Awwad et al, 2003), with effects that are consistent across mechanistic replicates, showing that chemical combinations can highlight biologically relevant cellular processes.
This study demonstrates the potential of chemical combinations for exploring functional connectivity in biological systems. This information complements genetic studies by providing more details through variable dosing, by directly targeting single domains of multi-domain proteins, and by probing cell types that are not amenable to mutagenesis. Responses from large chemical combination screens can be used to identify molecular targets through chemical–genetic profiling (Macdonald et al, 2006), or to directly constrain network models by means of a prediction-validation procedure (Ideker et al, 2001). This initial exploration can be extended to cover a wider range of response shapes and network topologies, as well as to combinations of three or more chemical agents. Moreover, this approach may even be applicable to non-biological systems where responses to targeted perturbations can be measured.
Efforts to construct therapeutically useful models of biological systems require large and diverse sets of data on functional connections between their components. Here we show that cellular responses to combinations of chemicals reveal how their biological targets are connected. Simulations of pathways with pairs of inhibitors at varying doses predict distinct response surface shapes that are reproduced in a yeast experiment, with further support from a larger screen using human tumour cells. The response morphology yields detailed connectivity constraints between nearby targets, and synergy profiles across many combinations show relatedness between targets in the whole network. Constraints from chemical combinations complement genetic studies, because they probe different cellular components and can be applied to disease models that are not amenable to mutagenesis. Chemical probes also offer increased flexibility, as they can be continuously dosed, temporally controlled, and readily combined. After extending this initial study to cover a wider range of combination effects and pathway topologies, chemical combinations may be used to refine network models or to identify novel targets. This response surface methodology may even apply to non-biological systems where responses to targeted perturbations can be measured.
doi:10.1038/msb4100116
PMCID: PMC1828746  PMID: 17332758
chemical genetics; combinations and synergy; metabolic and regulatory networks; simulation and data analysis
20.  Refining Ensembles of Predicted Gene Regulatory Networks Based on Characteristic Interaction Sets 
PLoS ONE  2014;9(2):e84596.
Different ensemble voting approaches have been successfully applied for reverse-engineering of gene regulatory networks. They are based on the assumption that a good approximation of true network structure can be derived by considering the frequencies of individual interactions in a large number of predicted networks. Such approximations are typically superior in terms of prediction quality and robustness as compared to considering a single best scoring network only. Nevertheless, ensemble approaches only work well if the predicted gene regulatory networks are sufficiently similar to each other. If the topologies of predicted networks are considerably different, an ensemble of all networks obscures interesting individual characteristics. Instead, networks should be grouped according to local topological similarities and ensemble voting performed for each group separately. We argue that the presence of sets of co-occurring interactions is a suitable indicator for grouping predicted networks. A stepwise bottom-up procedure is proposed, where first mutual dependencies between pairs of interactions are derived from predicted networks. Pairs of co-occurring interactions are subsequently extended to derive characteristic interaction sets that distinguish groups of networks. Finally, ensemble voting is applied separately to the resulting topologically similar groups of networks to create distinct group-ensembles. Ensembles of topologically similar networks constitute distinct hypotheses about the reference network structure. Such group-ensembles are easier to interpret as their characteristic topology becomes clear and dependencies between interactions are known. The availability of distinct hypotheses facilitates the design of further experiments to distinguish between plausible network structures. The proposed procedure is a reasonable refinement step for non-deterministic reverse-engineering applications that produce a large number of candidate predictions for a gene regulatory network, e.g. due to probabilistic optimization or a cross-validation procedure.
doi:10.1371/journal.pone.0084596
PMCID: PMC3911903  PMID: 24498260
21.  Optimal Network Alignment with Graphlet Degree Vectors 
Cancer Informatics  2010;9:121-137.
Important biological information is encoded in the topology of biological networks. Comparative analyses of biological networks are proving to be valuable, as they can lead to transfer of knowledge between species and give deeper insights into biological function, disease, and evolution. We introduce a new method that uses the Hungarian algorithm to produce optimal global alignment between two networks using any cost function. We design a cost function based solely on network topology and use it in our network alignment. Our method can be applied to any two networks, not just biological ones, since it is based only on network topology. We use our new method to align protein-protein interaction networks of two eukaryotic species and demonstrate that our alignment exposes large and topologically complex regions of network similarity. At the same time, our alignment is biologically valid, since many of the aligned protein pairs perform the same biological function. From the alignment, we predict function of yet unannotated proteins, many of which we validate in the literature. Also, we apply our method to find topological similarities between metabolic networks of different species and build phylogenetic trees based on our network alignment score. The phylogenetic trees obtained in this way bear a striking resemblance to the ones obtained by sequence alignments. Our method detects topologically similar regions in large networks that are statistically significant. It does this independent of protein sequence or any other information external to network topology.
PMCID: PMC2901631  PMID: 20628593
network alignment; biological networks; network topology; protein function prediction; phylogeny
22.  The G Protein-Coupled Receptor Heterodimer Network (GPCR-HetNet) and Its Hub Components 
G protein-coupled receptors (GPCRs) oligomerization has emerged as a vital characteristic of receptor structure. Substantial experimental evidence supports the existence of GPCR-GPCR interactions in a coordinated and cooperative manner. However, despite the current development of experimental techniques for large-scale detection of GPCR heteromers, in order to understand their connectivity it is necessary to develop novel tools to study the global heteroreceptor networks. To provide insight into the overall topology of the GPCR heteromers and identify key players, a collective interaction network was constructed. Experimental interaction data for each of the individual human GPCR protomers was obtained manually from the STRING and SCOPUS databases. The interaction data were used to build and analyze the network using Cytoscape software. The network was treated as undirected throughout the study. It is comprised of 156 nodes, 260 edges and has a scale-free topology. Connectivity analysis reveals a significant dominance of intrafamily versus interfamily connections. Most of the receptors within the network are linked to each other by a small number of edges. DRD2, OPRM, ADRB2, AA2AR, AA1R, OPRK, OPRD and GHSR are identified as hubs. In a network representation 10 modules/clusters also appear as a highly interconnected group of nodes. Information on this GPCR network can improve our understanding of molecular integration. GPCR-HetNet has been implemented in Java and is freely available at http://www.iiia.csic.es/~ismel/GPCR-Nets/index.html.
doi:10.3390/ijms15058570
PMCID: PMC4057749  PMID: 24830558
G protein-coupled receptors; network; heterodimerization; heteromers; dimerization; oligomerization; hubs; receptor-receptor interactions; clusters; architecture
23.  Integrative Modelling of the Influence of MAPK Network on Cancer Cell Fate Decision 
PLoS Computational Biology  2013;9(10):e1003286.
The Mitogen-Activated Protein Kinase (MAPK) network consists of tightly interconnected signalling pathways involved in diverse cellular processes, such as cell cycle, survival, apoptosis and differentiation. Although several studies reported the involvement of these signalling cascades in cancer deregulations, the precise mechanisms underlying their influence on the balance between cell proliferation and cell death (cell fate decision) in pathological circumstances remain elusive. Based on an extensive analysis of published data, we have built a comprehensive and generic reaction map for the MAPK signalling network, using CellDesigner software. In order to explore the MAPK responses to different stimuli and better understand their contributions to cell fate decision, we have considered the most crucial components and interactions and encoded them into a logical model, using the software GINsim. Our logical model analysis particularly focuses on urinary bladder cancer, where MAPK network deregulations have often been associated with specific phenotypes. To cope with the combinatorial explosion of the number of states, we have applied novel algorithms for model reduction and for the compression of state transition graphs, both implemented into the software GINsim. The results of systematic simulations for different signal combinations and network perturbations were found globally coherent with published data. In silico experiments further enabled us to delineate the roles of specific components, cross-talks and regulatory feedbacks in cell fate decision. Finally, tentative proliferative or anti-proliferative mechanisms can be connected with established bladder cancer deregulations, namely Epidermal Growth Factor Receptor (EGFR) over-expression and Fibroblast Growth Factor Receptor 3 (FGFR3) activating mutations.
Author Summary
Depending on environmental conditions, strongly intertwined cellular signalling pathways are activated, involving activation/inactivation of proteins and genes in response to external and/or internal stimuli. Alterations of some components of these pathways can lead to wrong cell behaviours. For instance, cancer-related deregulations lead to high proliferation of malignant cells enabling sustained tumour growth. Understanding the precise mechanisms underlying these pathways is necessary to delineate efficient therapeutical approaches for each specific tumour type. We particularly focused on the Mitogen-Activated Protein Kinase (MAPK) signalling network, whose involvement in cancer is well established, although the precise conditions leading to its positive or negative influence on cell proliferation are still poorly understood. We tackled this problem by first collecting sparse published biological information into a comprehensive map describing the MAPK network in terms of stylised chemical reactions. This information source was then used to build a dynamical Boolean model recapitulating network responses to characteristic stimuli observed in selected bladder cancers. Systematic model simulations further allowed us to link specific network components and interactions with proliferative/anti-proliferative cell responses.
doi:10.1371/journal.pcbi.1003286
PMCID: PMC3821540  PMID: 24250280
24.  How Structure Determines Correlations in Neuronal Networks 
PLoS Computational Biology  2011;7(5):e1002059.
Networks are becoming a ubiquitous metaphor for the understanding of complex biological systems, spanning the range between molecular signalling pathways, neural networks in the brain, and interacting species in a food web. In many models, we face an intricate interplay between the topology of the network and the dynamics of the system, which is generally very hard to disentangle. A dynamical feature that has been subject of intense research in various fields are correlations between the noisy activity of nodes in a network. We consider a class of systems, where discrete signals are sent along the links of the network. Such systems are of particular relevance in neuroscience, because they provide models for networks of neurons that use action potentials for communication. We study correlations in dynamic networks with arbitrary topology, assuming linear pulse coupling. With our novel approach, we are able to understand in detail how specific structural motifs affect pairwise correlations. Based on a power series decomposition of the covariance matrix, we describe the conditions under which very indirect interactions will have a pronounced effect on correlations and population dynamics. In random networks, we find that indirect interactions may lead to a broad distribution of activation levels with low average but highly variable correlations. This phenomenon is even more pronounced in networks with distance dependent connectivity. In contrast, networks with highly connected hubs or patchy connections often exhibit strong average correlations. Our results are particularly relevant in view of new experimental techniques that enable the parallel recording of spiking activity from a large number of neurons, an appropriate interpretation of which is hampered by the currently limited understanding of structure-dynamics relations in complex networks.
Author Summary
Many biological systems have been described as networks whose complex properties influence the behaviour of the system. Correlations of activity in such networks are of interest in a variety of fields, from gene-regulatory networks to neuroscience. Due to novel experimental techniques allowing the recording of the activity of many pairs of neurons and their importance with respect to the functional interpretation of spike data, spike train correlations in neural networks have recently attracted a considerable amount of attention. Although origin and function of these correlations is not known in detail, they are believed to have a fundamental influence on information processing and learning. We present a detailed explanation of how recurrent connectivity induces correlations in local neural networks and how structural features affect their size and distribution. We examine under which conditions network characteristics like distance dependent connectivity, hubs or patches markedly influence correlations and population signals.
doi:10.1371/journal.pcbi.1002059
PMCID: PMC3098224  PMID: 21625580
25.  Topology of molecular interaction networks 
BMC Systems Biology  2013;7:90.
Molecular interactions are often represented as network models which have become the common language of many areas of biology. Graphs serve as convenient mathematical representations of network models and have themselves become objects of study. Their topology has been intensively researched over the last decade after evidence was found that they share underlying design principles with many other types of networks.
Initial studies suggested that molecular interaction network topology is related to biological function and evolution. However, further whole-network analyses did not lead to a unified view on what this relation may look like, with conclusions highly dependent on the type of molecular interactions considered and the metrics used to study them. It is unclear whether global network topology drives function, as suggested by some researchers, or whether it is simply a byproduct of evolution or even an artefact of representing complex molecular interaction networks as graphs.
Nevertheless, network biology has progressed significantly over the last years. We review the literature, focusing on two major developments. First, realizing that molecular interaction networks can be naturally decomposed into subsystems (such as modules and pathways), topology is increasingly studied locally rather than globally. Second, there is a move from a descriptive approach to a predictive one: rather than correlating biological network topology to generic properties such as robustness, it is used to predict specific functions or phenotypes.
Taken together, this change in focus from globally descriptive to locally predictive points to new avenues of research. In particular, multi-scale approaches are developments promising to drive the study of molecular interaction networks further.
doi:10.1186/1752-0509-7-90
PMCID: PMC4231395  PMID: 24041013
Network biology; Review; Molecular biology; Graph theory

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