A logic-based diagram of signalling pathways central to macrophage activation.

BACKGROUND: The complex yet flexible cellular response to pathogens is orchestrated by the interaction of multiple signalling and metabolic pathways. The molecular regulation of this response has been studied in great detail but comprehensive and unambiguous diagrams describing these events are generally unavailable. Four key signalling cascades triggered early-on in the innate immune response are the toll-like receptor, interferon, NF-kappaB and apoptotic pathways, which co-operate to defend cells against a given pathogen. However, these pathways are commonly viewed as separate entities rather than an integrated network of molecular interactions. RESULTS: Here we describe the construction of a logically represented pathway diagram which attempts to integrate these four pathways central to innate immunity using a modified version of the Edinburgh Pathway Notation. The pathway map is available in a number of electronic formats and editing is supported by yEd graph editor software. CONCLUSION: The map presents a powerful visual aid for interpreting the available pathway interaction knowledge and underscores the valuable contribution well constructed pathway diagrams make to communicating large amounts of molecular interaction data. Furthermore, we discuss issues with the limitations and scalability of pathways presented in this fashion, explore options for automated layout of large pathway networks and demonstrate how such maps can aid the interpretation of functional studies

Arcadia: a visualization tool for metabolic pathways

Summary: Arcadia translates text-based descriptions of biological networks (SBML files) into standardized diagrams (SBGN PD maps). Users can view the same model from different perspectives and easily alter the layout to emulate traditional textbook representations

Integrating biological pathways and genomic profiles with ChiBE 2

Cataloged from PDF version of article.Background: Dynamic visual exploration of detailed pathway information can help researchers digest and interpret complex mechanisms and genomic datasets. Results: ChiBE is a free, open-source software tool for visualizing, querying, and analyzing human biological pathways in BioPAX format. The recently released version 2 can search for neighborhoods, paths between molecules, and common regulators/targets of molecules, on large integrated cellular networks in the Pathway Commons database as well as in local BioPAX models. Resulting networks can be automatically laid out for visualization using a graphically rich, process-centric notation. Profiling data from the cBioPortal for Cancer Genomics and expression data from the Gene Expression Omnibus can be overlaid on these networks. Conclusions: ChiBE's new capabilities are organized around a genomics-oriented workflow and offer a unique comprehensive pathway analysis solution for genomics researchers

The mEPN scheme: an intuitive and flexible graphical system for rendering biological pathways

<p>Abstract</p> <p>Background</p> <p>There is general agreement amongst biologists about the need for good pathway diagrams and a need to formalize the way biological pathways are depicted. However, implementing and agreeing how best to do this is currently the subject of some debate.</p> <p>Results</p> <p>The modified Edinburgh Pathway Notation (mEPN) scheme is founded on a notation system originally devised a number of years ago and through use has now been refined extensively. This process has been primarily driven by the author's attempts to produce process diagrams for a diverse range of biological pathways, particularly with respect to immune signaling in mammals. Here we provide a specification of the mEPN notation, its symbols, rules for its use and a comparison to the proposed Systems Biology Graphical Notation (SBGN) scheme.</p> <p>Conclusions</p> <p>We hope this work will contribute to the on-going community effort to develop a standard for depicting pathways and will provide a coherent guide to those planning to construct pathway diagrams of their biological systems of interest.</p

Knowledge Management Approaches for predicting Biomarker and Assessing its Impact on Clinical Trials

The recent success of companion diagnostics along with the increasing regulatory pressure for better identification of the target population has created an unprecedented incentive for the drug discovery companies to invest into novel strategies for stratified biomarker discovery. Catching with this trend, trials with stratified biomarker in drug development have quadrupled in the last decade but represent a small part of all Interventional trials reflecting multiple co-developmental challenges of therapeutic compounds and companion diagnostics. To overcome the challenge, varied knowledge management and system biology approaches are adopted in the clinics to analyze/interpret an ever increasing collection of OMICS data. By semi-automatic screening of more than 150,000 trials, we filtered trials with stratified biomarker to analyse their therapeutic focus, major drivers and elucidated the impact of stratified biomarker programs on trial duration and completion. The analysis clearly shows that cancer is the major focus for trials with stratified biomarker. But targeted therapies in cancer require more accurate stratification of patient population. This can be augmented by a fresh approach of selecting a new class of biomolecules i.e. miRNA as candidate stratification biomarker. miRNA plays an important role in tumorgenesis in regulating expression of oncogenes and tumor suppressors; thus affecting cell proliferation, differentiation, apoptosis, invasion, angiogenesis. miRNAs are potential biomarkers in different cancer. However, the relationship between response of cancer patients towards targeted therapy and resulting modifications of the miRNA transcriptome in pathway regulation is poorly understood. With ever-increasing pathways and miRNA-mRNA interaction databases, freely available mRNA and miRNA expression data in multiple cancer therapy have created an unprecedented opportunity to decipher the role of miRNAs in early prediction of therapeutic efficacy in diseases. We present a novel SMARTmiR algorithm to predict the role of miRNA as therapeutic biomarker for an anti-EGFR monoclonal antibody i.e. cetuximab treatment in colorectal cancer. The application of an optimised and fully automated version of the algorithm has the potential to be used as clinical decision support tool. Moreover this research will also provide a comprehensive and valuable knowledge map demonstrating functional bimolecular interactions in colorectal cancer to scientific community. This research also detected seven miRNA i.e. hsa-miR-145, has-miR-27a, has- miR-155, hsa-miR-182, hsa-miR-15a, hsa-miR-96 and hsa-miR-106a as top stratified biomarker candidate for cetuximab therapy in CRC which were not reported previously. Finally a prospective plan on future scenario of biomarker research in cancer drug development has been drawn focusing to reduce the risk of most expensive phase III drug failures

Causality analysis in biological networks

Ankara : The Department of Computer Engineering and the Institute of Engineering and Science of Bilkent University, 2010.Thesis (Ph.D.) -- Bilkent University, 2010.Includes bibliographical references leaves 69-78.Systems biology is a rapidly emerging field, shaped in the last two decades or so, which promises understanding and curing several complex diseases such as cancer. In order to get an insight about the system – specifically the molecular network in the cell – we need to work on following four fundamental aspects: experimental and computational methods to gather knowledge about the system, mathematical models for representing the knowledge, analysis methods for answering questions on the model, and software tools for working on these. In this thesis, we propose new approaches related to all these aspects. In this thesis, we define new terms and concepts that helps us to analyze cellular processes, such as positive and negative paths, upstream and downstream relations, and distance in process graphs. We propose algorithms that will search for functional relations between molecules and will answer several biologically interesting questions related to the network, such as neighborhoods, paths of interest, and common targets or regulators of molecules. In addition, we introduce ChiBE, a pathway editor for visualizing and analyzing BioPAX networks. The tool converts BioPAX graphs to drawable process diagrams and provides the mentioned novel analysis algorithms. Users can query pathways in Pathway Commons database and create sub-networks that focus on specific relations of interest. We also describe a microarray data analysis component, PATIKAmad, built into ChiBE and PATIKAweb, which integrates expression experiment data with networks. PATIKAmad helps those tools to represent experiment values on network elements and to search for causal relations in the network that potentially explain dependent expressions. Causative path search depends on the presence of transcriptional relations in the model, which however is underrepresented in most of the databases. This is mainly due to insufficient knowledge in the literature. We finally propose a method for identifying and classifying modulators of transcription factors, to help complete the missing transcriptional relations in the pathway databases. The method works with large amount of expression data, and looks for evidence of modulation for triplets of genes, i.e. modulator - factor - target. Modulator candidates are chosen among the interacting proteins of transcription factors. We expect to observe that expression of the target gene depends on the interaction between factor and modulator. According to the observed dependency type, we further classify the modulation. When tested, our method finds modulators of Androgen Receptor; our top-scoring result modulators are supported by other evidence in the literature. We also observe that the modulation event and modulation type highly depend on the specific target gene. This finding contradicts with expectations of molecular biology community who often assume a modulator has one type of effect regardless of the target gene.Babur, ÖzgünPh.D

Rule-based Modeling of Cell Signaling: Advances in Model Construction, Visualization and Simulation

Rule-based modeling is a graph-based approach to specifying the kinetics of cell signaling systems. A reaction rule is a compact and explicit graph-based representation of a kinetic process, and it matches a class of reactions that involve identical sites and identical kinetics. Compact rule- based models have been used to generate large and combinatorially complex reaction networks, and rules have also been used to compile databases of kinetic interactions targeting specific cells and pathways. In this work, I address three technological challenges associated with rule-based modeling. First, I address the ability to generate an automated global visualization of a rule-based model as a network of signal flows. I showed how to analyze a reaction rule and extract a set of bipartite regulatory relationships, which can be aggregated across rules into a global network. I also provide a set of coarse-graining approaches to compress an automatically generated network into a compact pathway diagram, even for models with 100s of rules. Second, I resolved an incompatibility between two recent advances in rule-based modeling: network-free simulation (which enables simulation without generating a reaction network), and energy-based rule-based modeling (which enables specifying a model using cooperativity parameters and automated accounting of free energy). The incompatibility arose because calculating the reaction rate requires computing the reaction free energy in an energy-based model, and this requires knowledge of both reactants and products of the reaction, but the products are not available in a network-free simulation until after the reaction event has fired. This was resolved by expanding each energy- based rule into a number of normal reaction rules for which reaction free energies can be calculated unambiguously. Third, I demonstrated a particular type of modularization that is based on treating a set of rules as a module. This enables building models from combinations of modular hypotheses and supplements the other modularization strategies such as macros, types and energy-based compression

Visual representation of cellular networks

Development of advanced techniques for biological network visualisation is crucial for successful progress in the areas of systems-level biology and data-intensive bioinformatics. However, current techniques for biological network visualisation fall short of expectations for representing extensive biological networks. In order to provide really useful network visualisation tools, new approaches have to be proposed and applied alongside with those most powerful features of current visualisation systems. The resulting representation techniques have to be tested by applying to large-scale examples that would include metabolic, signaling and gene expression events. User survey should also be carried out to further prove the advantages of the new techniques. The present thesis describes an attempt to achieve the above objectives, by performing the following steps: 1) existing approaches in the area of network representation were analyzed and their shortcomings and advantages were defined; 2) new notation has been developed, in which, the defined best features of the existing systems were integrated with newly introduced potent features such as compact visualization, ‘functional gate’ and ‘identity gate’, 4) new framework was developed that allows managing large-scale networks that are represented on different levels of details and different levels of constrains, while keeping each diagram semantically unambiguous, 5) extensive examples, including genome-scaled human metabolic network and TNF-alpha receptor signalling network, were used to prove that the designed notation and the framework can be applied efficiently, and, finally, 6) a notation survey has been carried out to validate the advantages of the newly developed notation over the existing ones

Data integration with biological pathways

Biological experiments generate many data, but unfortunately these are not always optimally used. That is why BiGCaT, the Bio-informatics department of UM, has developed new software in collaboration with the Gladstone institute in San Francisco. This new software can link these data to dozens of online databases. Moreover, the data are attractively presented on illustrations that represent the processes in the cell, the so-called biological pathways. These illustrations are made by means of a specially developed wiki. With our software, two earlier studies into long-term food shortage were joined together. This reanalysis has led to new insights, without the need for an expensive experiment. The results will contribute to a better treatment of patients that have problems absorbing food due to illness