• Sushant Patkar, University of Maryland, United States
• Roded Sharan, Blavatnik School of Computer Science, Israel

A chief goal of systems biology is the reconstruction of large-scale executable models of cellular processes of interest. While accurate continuous models are still beyond reach, a powerful alternative is to learn a logical model of the processes under study, which predicts the logical state of any node of the model as a Boolean function of its incoming nodes. Key to learning such models is the functional annotation of the underlying physical interactions with activation/repression (sign) effects. Such annotations are pretty common for a few well studied biological pathways. Here we present a novel optimization framework for large-scale sign annotation that employs
different models of signaling and combines them in a rigorous manner. We apply our framework to two large scale knockout datasets in yeast and evaluate its different components as well as the combined model on different subsets of physical interactions. Overall, we obtain an accurate predictor that outperforms previous work by a considerable margin.

• Jeff Piotrowski, RIKEN Center for Sustainable Resource Science, Japan
• Sheena Li, RIKEN Center for Sustainable Resource Science, Japan
• Raamesh Deshpande, University of Minnesota, United States
• Scott Simpkins, University of Minnesota, United States
• Justin Nelson, University of Minnesota, United States
• Yoko Yashiroda, RIKEN Center for Sustainable Resource Science, Japan
• Jacqueline Barber, RIKEN Center for Sustainable Resource Science, Japan
• Hamid Safizadeh, University of Minnesota, United States
• Erin Wilson, University of Minnesota, United States
• Hiroki Okada, The University of Tokyo, Japan
• Abraham Gebre, The University of Tokyo, Japan
• Karen Kubo, The University of Tokyo, Japan
• Nikko Torres, University of Toronto, Canada
• Marissa Leblanc, RIKEN Center for Sustainable Resource Science, Japan
• Kerry Andrusiak, University of Toronto, Canada
• Reika Okamoto, RIKEN Center for Sustainable Resource Science, Japan
• Mami Yoshimura, RIKEN Center for Sustainable Resource Science, Japan
• Eva Derango-Adem, University of Toronto, Canada
• Jolanda van Leeuwen, University of Toronto, Canada
• Katsuhiko Shirahige, The University of Tokyo, Japan
• Anastasia Baryshnikova, Princeton University, United States
• Grant Brown, University of Toronto, Canada
• Hiroyuki Hirano, RIKEN Center for Sustainable Resource Science, Japan
• Michael Costanzo, University of Toronto, Canada
• Brenda Andrews, University of Toronto, Canada
• Yoshikazu Ohya, The University of Tokyo, Japan
• Hiroyuki Osada, RIKEN Center for Sustainable Resource Science, Japan
• Minoru Yoshida, RIKEN Center for Sustainable Resource Science, Japan
• Charles Boone, University of Toronto, Canada
• Chad Myers, University of Minnesota, United States

Screening chemical compounds against genome-wide mutant arrays identifies genetic perturbations that cause sensitivity or resistance to compounds of interest. We developed a chemical-genetic interaction screening pipeline in Saccharomyces cerevisiae to functionally annotate chemical compounds in a high throughput manner. This pipeline leveraged a diagnostic collection of ~300 genetically-barcoded mutants in a drug-sensitized background, a high degree of sample multiplexing, and a genome-wide compendium of genetic interaction profiles to predict modes of action for compounds across seven diverse compound libraries. These libraries contained natural products and derivatives, combinatorially-synthesized compounds, and several hundred approved drugs. Two key components of our pipeline were the computational tools we developed to score chemical-genetic interactions from pooled screens of barcoded mutants and to integrate chemical-genetic and genetic interaction datasets for mode-of-action prediction. We used the latter component to identify a set of ~1500 out of the ~14,000 screened compounds that possessed high-confidence mode-of-action predictions, which we experimentally validated across multiple diverse functions. More broadly, we observed functional biases in the different compound libraries, with the larger and presumably least structurally- and mechanistically-biased collections showing a depletion for compounds perturbing nuclear processes.

• Aaron Baker, University of Wisconsin-Madison, United States
• Anthony Gitter, University of Wisconsin-Madison, United States

Non-negative matrix factorization is a popular tool for decomposing high-dimensional data into its constitutive parts. Recent research has incorporated manifold regularization to select parts which are consistent on a manifold, a mathematical structure that describes how features in the data are related to one another. One source of hidden structure in genomic data relates to the effect of interactions among groups of genes in the cell. Applications of manifold-regularized matrix factorization to these datasets have revealed cancer sub-types with different biomarkers and effective treatments. We propose a more focused version of this method which reformulates the global gene interaction network manifold as a set of sub-manifolds, each associated with a biological pathway. Biological pathways are important sets of interactions because they define how pairs of interacting genes influence broader physiological processes. These processes also capture tissue- and context-specific relationships among the genes under investigation. By constraining matrix factorization techniques to respect these underlying structures and emphasizing pathway edges instead of nodes, we gain biological insight when examining the factors.

• Phan Nguyen, Northwestern University, United States
• Rosemary Braun, Northwestern University, United States

Gene regulatory networks (GRNs) are important abstractions of the complex regulatory interplays between genes, proteins, metabolites, and other molecular-level entities. Comprehensive GRNs provide high-level overviews of the topology of gene-gene interactions and their purposes, thereby enabling a comprehensive understanding of their role in phenotypic variation, disease mechanisms, and other biological processes and how they may be perturbed for therapeutic purposes. However, constructing accurate GRNs from gene expression data remains a challenge, complicated by problems such as small sample sizes, gene expression stochasticity, and incomplete characterizations of gene regulatory dynamics.

We adapted the time-lagged ordered lasso (TL-OL), a regularized regression method with temporal monotonicity constraints, for de novo reconstruction from time-course gene expression data. We assume that a gene's expression linearly depends on that of its regulators at multiple preceding time points and that a predictor's regulatory strength diminishes with increasing temporal distance. By combining these modeling assumptions with a canonical feature selection procedure, we can produce accurate networks that reflect the dynamics and assumptions of the TL-OL. Since partial knowledge of the regulatory dependencies between genes is available, we also describe a semi-supervised method that embeds prior network information into the TL-OL to facilitate novel edge discovery in existing pathways.

• Sarvenaz Choobdar, University of Lausanne, Switzerland
• Mehmet Eren Ahsen, Icahn School of Medicine at Mount Sinai, United States
• Jake Crawford, Tufts University, United States
• Mattia Tomasoni, University of Lausanne, Switzerland
• David Lamparter, University of Lausanne, Switzerland
• Junyuan Lin, Tufts University, United States
• Benjamin Hescott, Northeastern University, United States
• Xiaozhe Hu, Tufts University, United States
• Johnathan Mercer, Harvard University, United States
• Ted Natoli, Harvard University, United States
• Rajiv Narayan, Harvard University, United States
• Aravind Subramanian, Harvard University, United States
• Gustavo Stolovitzky, IBM, United States
• Zoltan Kutalik, University of Lausanne, Switzerland
• Kasper Lage, Harvard University, United States
• Donna Slonim, Tufts University, United States
• Julio Saez-Rodriguez, EMBL-EBI, United States
• Lenore Cowen, Tufts University, United States
• Sven Bergmann, University of Lausanne, Switzerland
• Daniel Marbach, University of Lausanne, Switzerland

Identification of modules in molecular networks is at the core of many current analysis methods in biomedical research. However, how well different approaches identify disease-relevant modules in different types of networks remains poorly understood. We launched the “Disease Module Identification DREAM Challenge”, an open competition to comprehensively assess module identification methods across diverse gene, protein and signaling networks. Predicted network modules were tested for association with complex traits and diseases using a unique collection of 180 genome-wide association studies (GWAS). While a number of approaches were successful in terms of discovering complementary trait-associated modules, consensus predictions derived from the challenge submissions performed best. We find that most of these modules correspond to core disease-relevant pathways, which often comprise therapeutic targets and correctly prioritize candidate disease genes. This community challenge establishes benchmarks, tools and guidelines for molecular network analysis to study human disease biology (https://synapse.org/modulechallenge).

• Mehmet Eren Ahsen, Icahn School of Medicine at Mount Sinai, United States
• Jennifer E.L. Diaz, Icahn School of Medicine at Mount Sinai Hospital, United States
• Thomas Schaffter, IBM, United States
• Xintong Chen, Icahn School of Medicine at Mount Sinai Hospital, United States
• Bojan Losic, Icahn School of Medicine at Mount Sinai Hospital, United States
• Gustavo Stolovitzky, IBM, United States

Combination therapies have become an important part of cancer treatment in recent decades. In this work, we suggest possible mechanisms whereby transcriptional responses combine to give rise to synergistic or additive responses to combined therapies. Specifically, we used RNAseq to study the transcriptional response over time and for three drugs and their combinations in MCF‐7 breast cancer cells. We defined a novel measure for molecular synergy and showed that molecular synergy correlates with phenotypic synergy as measured by EOB (Excess over Bliss). We used network analysis to trace the transcriptional cascade that triggers the synergistic effects and identify a dominant mechanism for the synergy in which transcription factors get activated over a threshold of activity when the two drugs are applied but not when the individual drugs are applied. This observation suggests that a pair of drugs with correlated transcriptional programs is likely to trigger a synergistic effect. We developed a novel algorithm denoted as CASCADE that can accurately predict drug synergy. When applied to the independent NCI-DREAM Drug Synergy Prediction Challenge data, CASCADE robustly predicted synergy in the DREAM data with an AUC of 0.95 significantly over-performing the best algorithm in the challenge which had an AUC of 0.8.

• Anna Goldenberg, University of Toronto, Canada

Discovering genetic mechanisms driving complex diseases is hard. Existing methods often lack power to identify the set of associated genes. I will present a novel Bayesian framework which improves the power of gene detection in several ways. First, our new method integrates rare variants using gene network as a prior. There are two main advantages to being Bayesian when using networks: i) networks are noisy and incomplete, a problem that is in part mitigated by not relying on the presence or absence of each edge explicitly; ii) we can keep track of the uncertainty of each gene being associated with the phenotype and also examine the extent to which a given gene is likely to be ‘responsible’ for the disease in a given patient. Second, our novel framework allows to integrate coding and regulatory (transcriptomic) aberrations improving the power in the presence of patient heterogeneity. I will show that using networks clearly improves gene detection compared to individual gene testing. I will then show consistently improved performance of our method compared to the state-of-the-art diffusion network-based approach, using a variety of randomly generated and literature-reported gene sets. I will finally give an example of using our new Bayesian framework towards understanding disease mechanisms at a personalized patient level.

One poster, one slide, one minute each.

• Karel Kalecky, Baylor University, United States
• Young-Rae Cho, Baylor University, United States

Motivation: Cross-species analysis of large-scale protein-protein interaction (PPI) networks has played a significant role in understanding the principles deriving evolution of cellular organizations and functions. Recently, network alignment algorithms have been proposed to predict conserved in-teractions and functions of proteins. These approaches are based on the notion that orthologous pro-teins across species are sequentially similar and that topology of PPIs between orthologs is often conserved. However, high accuracy and scalability of network alignment are still a challenge.
Results: We propose a novel pairwise global network alignment algorithm, called PrimAlign, which is modeled as a Markov chain and iteratively transited until convergence. The proposed algorithm also incorporates the principles of PageRank. This approach is evaluated on tasks with human, yeast, and fruit fly PPI networks. The experimental results demonstrate that PrimAlign outperforms several prev-alent methods with statistically significant differences in multiple evaluation measures. PrimAlign, which is multiplatform, achieves superior performance in runtime with its linear asymptotic time com-plexity. Further evaluation is done with synthetic networks and results suggest that popular topologi-cal measures do not reflect real precision of alignments.
Availability: The source code is available at http://web.ecs.baylor.edu/faculty/cho/PrimAlign

• Shawn Gu, University of Notre Dame, United States
• John Johnson, University of Notre Dame, United States
• Fazle Faisal, University of Notre Dame, United States
• Tijana Milenkovic, University of Notre Dame, United States

Biological network alignment (NA) aims to uncover similar regions between protein-protein interaction (PPI) networks of different species. Then, analogous to genomic sequence alignment, NA can be used to transfer functional knowledge across species between their conserved PPI network (rather than sequence) regions. For example, if we align the PPI network of baker's yeast, a well-studied species, to the PPI network of human, a poorly-studied species, we can infer the function of human proteins based the function of their aligned partners in the yeast network. Existing NA methods are homogeneous, i.e., they can deal only with networks containing nodes and edges of one type. However, analyzing heterogeneous multi-node- or multi-edge-type network data (such as genes/proteins, phenotypes, diseases, or drugs, and various types of interactions that can exist between them) can lead to deeper insights into cellular function. So, we introduce several algorithmic novelties to allow three recent state-of-the-art NA methods, WAVE, MAGNA++, and SANA, to perform heterogeneous NA for the first time. In evaluations on synthetic, PPI, and protein-Gene Ontology term networks, our proposed heterogeneous NA methods lead to higher-quality alignments and better robustness to noise in the data than their homogeneous counterparts.

• Omer Basha, Ben-Gurion University of the Negev, Israel
• Chanan Argov, Ben-Gurion University of the Negev, Israel
• Raviv Artzy, Ben-Gurion University of the Negev, Israel
• Yazeed Zoabi, Ben-Gurion University of the Negev, Israel
• Idan Hekselman, Ben-Gurion University of the Negev, Israel
• Liad Alfandari, Ben-Gurion University of the Negev, Israel
• Vered Chalifa-Caspi, Ben-Gurion University of the Negev, Israel
• Esti Yeger-Lotem, Ben-Gurion University of the Negev, Israel

Differential network analysis that highlights interaction changes between conditions is a rising paradigm in network biology. However, differential network analysis is usually applied to compare between few conditions, and a large-scale assessment of different methods has been lacking. Here, we developed approaches that extend this paradigm toward differential network analysis of multiple conditions, which we applied to analyze protein interaction networks (interactomes) of 34 human tissues. We assessed the performance of these approaches by their ability to expose physiological differences among human tissues relevant in health and disease. To this end, we manually associated 6,019 biological processes and 1,185 hereditary diseases with their relevant tissue. Our analysis revealed the advantage of differential tissue interactomes over node-weighted tissue interactomes in highlighting tissue-specific processes. We then tested whether differential tissue interactomes can highlight tissue-selective disease-related genes. Indeed, in 28% of the 1,527 cases we studied, the top 1% differential sub-network surrounding a causal gene was enriched significantly for additional disease-causing genes, a fraction much higher than expected by chance. Altogether, the expansive resources that we created and our analyses demonstrate that differential analysis of human tissue interactomes is a powerful tool for highlighting processes and genes with tissue-specific functionality and impact.

• Nadezhda T. Doncheva, University of Copenhagen, Denmark
• Lars Juhl Jensen, NNF Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
• Jan Gorodkin, University of Copenhagen, Denmark

An important aspect of studying human diseases and developing new treatment strategies is the use of animal models. However, it is crucial to take into account the intrinsic differences in regulation between human and animal models as well as the mechanisms underlying the specific phenotype in order to generate reliable hypotheses. Moreover, we still need to overcome the challenge of identifying the regulatory genes and pathways that are responsible for a phenotype of interest both in human and in animal models.
Therefore, we have designed a unique comparison framework for investigating pathways in mammalian organisms on different levels of detail based on data integration and network analysis techniques. The orthology relationships between different organisms as indicated by the eggNOG database (http://eggnogdb.embl.de) were used to identify and visualize the pathway conservation. We show that all KEGG pathways overlap more than 50% between human, mouse, rat and pig. Furthermore, we integrated these pathways with tissue expression data from the recently updated TISSUES database (http://tissues.jensenlab.org/) to enable the identification of expression patterns across mammalian tissues and organisms. Finally, we discuss how our comparison framework can be used to suggest the most suitable model organism for the relevant human disease (sub)pathways.

• Jake Chen, University of Alabama at Birmingham, Informatics Institute, United States
• Zongliang Yue, University of Alabama at Birmingham, Informatics Institute, United States

Advances in next-generation sequencing technology have led to surging development of bioinformatics tools to process and characterize genomics and functional genomics data. We developed an integrated online application called PAGER for PAGs (pathway, annotated list, and gene signature)--a representation for super gene sets--to help biomedical researchers to perform one-stop analysis equivalent to previously independent characterization of enrichment analysis of gene ontology, pathways, and networks for omics data. The resource is located at http://discovery.informatics.uab.edu/PAGER/, currently in version 2.0 (Yue et. 2018, Nucleic Acids Res. 2018 Jan 4;46(D1):D668-D676. doi: 10.1093/nar/gkx1040) The PAGER online application contains 84 282 PAGs, 601 164 gene–gene relationships, and 7 538 275 PAG-to-PAG relationships. We will demonstrate through several case studies how this tool enables network biology characterization of omics data at the super gene set scale.

• Wei Zhang, University of California San Diego, United States
• Jianzhu Ma, University of California San Diego, United States
• Trey Ideker, University of California San Diego, United States

Motivation: Network propagation has been widely used to aggregate and amplify the effects of tumor mutations using knowledge of molecular interaction networks. However, propagating mutations through interactions irrelevant to cancer leads to erosion of pathway signals and complicates the identification of cancer subtypes.
Results: To address this problem we introduce a propagation algorithm, Network-Based Supervised Stratification (NBS2), which learns the mutated subnetworks underlying tumor subtypes using a super- vised approach. Given an annotated molecular network and reference tumor mutation profiles for which subtypes have been predefined, NBS2 is trained by adjusting the weights on interaction features such that network propagation best recovers the provided subtypes. After training, weights are fixed such that mutation profiles of new tumors can be accurately classified. We evaluate NBS2 on breast and glioblastoma tumors, demonstrating that it outperforms the best network-based approaches in classi- fying tumors to known subtypes for these diseases. By interpreting the interaction weights, we highlight characteristic molecular pathways driving selected subtypes.
Availability: The NBS2 package is freely available at: https://github.com/wzhang1984/NBSS

• Shuzhao Li, Emory University, United States
• Luiz Gardinassi, Emory University, United States
• Lu Xiong, Emory University, United States
• Yating Wang, Emory University, United States

Human immunology relies heavily on high-throughput data to infer the underlying mechanisms. A common set of computational tools are needed to model the immune responses to infectious diseases, vaccines, autoimmune diseases, inflammation and cancer immunotherapy. Building upon our previous works on blood transcription modules for transcriptomics analysis, and mummichog for metabolomics pathway/network analysis, we have developed a method of multi-omics, multiscale networks to integrate big data immunology. The approach combines meaningful dimension reduction, hierarchical networks and partial least square regression to explain individual differences in immune response. We will demonstrate the application to a herpes zoster vaccine study (Li et al, 2017. Cell 169:862), where important metabolic phenotypes are identified through the integration of transcriptomics, metabolomics, cytokines and cell populations. New results will be reported on a study of controlled malaria infection in humans, and on continued effort in software development.

• Mohamed Hamed Fahmy, Rostock University Medical Center, Germany
• Georg Fuellen, Rostock University Medical Center, Germany

Background
Parkinson’s disease (PD) is the second most common neurodegenerative disorder after Alzheimer’s disease. Despite extensive but separate analyses of gene and miRNA expression in PD, the regulatory interplay between deregulated genes and miRNAs triggering PD remains largely uncharacterized. We present here a comprehensive integrative analysis of gene expression profiles, miRNA signatures, and other publicly available regulatory databases in order to better understand the collaborative functional role between miRNAs and genes in driving PD.

Results
A PD-specific transcription factor (TF)-miRNA regulatory network was generated and its topology and functional impacts upon PD was analyzed. We identified six driver genetic elements (2 genes and 4 miRNAs) and various functional network modules that could conceivably trigger PD etiology. Moreover, neuroprotective agents and other small molecule signatures were predicted to reverse the transcriptional changes caused by the identified functional modules.

Conclusion
Our joint regulatory analysis of coding and non-coding RNA, has the potential to yield clinically as well as biologically relevant information. These results, on the one hand, enrich our knowledge base of prospective genomic drivers and essential network modules in PD progression and, on the other hand, open new opportunities towards novel therapeutic leads and better treatment for PD patients.

• George Acquaah-Mensah, Massachusetts College of Pharmacy & Health Sciences, United States
• Athreya Ramesh, Tufts University, United States

The Human epidermal growth factor receptor 2 (ERBB2/ HER2-Neu) has expanded expression in 15% of breast cancer samples. HER2-enriched samples are associated with poor prognoses. Resistance to therapy with the HER2-targeting monoclonal antibody, Trastuzumab, remains a challenge in treatment of HER2-enriched breast cancers. We explore the transcriptional regulation of HER2 expression across intrinsic subtypes of breast cancer to help identify targets for adjunctive therapy. The PAM50 algorithm was applied to data from breast invasive carcinoma (BRCA) RNAseq version 2 samples from the Cancer Genome Atlas (TCGA). Using the Algorithm for the Reconstruction of Accurate Cellular networks (ARACNe), transcriptional regulatory networks were constructed using basal-like samples only, HER2-enriched samples only, luminal A samples only, luminal B samples only, and all samples. GEne Network Inference with Ensemble of trees (GENIE3), as well as Passing Attributes between Networks for Data Assimilation (PANDA) were also used for that purpose. In HER2-enriched samples, HER2 is uniquely dependent on E2F2, HOXA5 and CRY2. The results bespeak differences in regulation of HER2 across subtypes. Activities/expressions of E2F2, HOXA5, and CRY2 are thus candidates for targeting as potential adjunctive therapy in the treatment of Trastuzumab-resistant HER2-enriched breast cancers.

• Alice Yu, Stanford University, United States
• Andrew Gentles, Stanford University, United States
• Joseph Shrager, Stanford University, United States
• Maximilian Diehn, Stanford University, United States
• Sylvia Plevritis, Stanford University, United States

Tumor heterogeneity is the leading cause of drug resistance and metastatic progression. One aspect of tumor heterogeneity is driven by interactions between different cell types within the tumor microenvironment that enable malignant cells to adapt and proliferate. An active research area is to identify cell-cell signaling interactions that are specific to tumor maintenance. To date, few such cell-cell signaling interactions have been found for therapeutic interactions. Analyzing the tumor microenvironment by dissociating the tumor-stromal microenvironment and generating transcriptomic data by cell type promises to reveal these cell-cell interactions. Current network-based analytic approaches that leverage such data to identify TME-specific interactions are largely based on correlation analysis, but these can lead to high false positive rates. Instead, we aim to identify potential ligand-receptor interactions by enriching for consistent downstream biological processes. We developed a novel network-based algorithm to impute these interactions that captures the effect of various cell types interacting simultaneously. This algorithm was applied to RNA-Seq data from individually sorted stromal and epithelial cells from the primary human non-small cell lung cancer. The results show both novel, and confirm known, TME interactions, aid with the interpretation of prognostic signatures, and enable identification of potential tumor-stromal interaction drug targets.

• Robin Haw, Ontario Institute for Cancer Research - Reactome, Canada
• Reactome Team, Reactome, Canada
• Guanming Wu, OHSU - Reactome, United States
• Henning Hermjakob, EMBL-EBI - Reactome, United Kingdom
• Peter D'Eustachio, NYULMC - Reactome, United States
• Lincoln Stein, Ontario Institute for Cancer Research, Canada

Reactome (https://reactome.org) is one of the few open access curated biological pathway knowledgebases. Its authoritative and detailed content has directly and indirectly supported basic and translational research studies with over-representation analysis and network-building tools to discover patterns in high-throughput data. With user experience firmly in mind, we have created a new web interface. An improved version of the Pathway Diagram Viewer provides faster data loading, diagram rendering and element seeking. Furthermore, to increase the quality of the graphics used to represent pathways, we have integrated Enhanced High-Level Diagrams (EHLDs). These illustrations look like overviews of biological processes found in textbooks and review articles, with a consistent iconography with embedded navigation and data overlay functionality. We have expanded our current Probabilistic Graphical Models (PGM) for Reactome pathways to support inference of pathway impacts of somatic mutations affecting protein structures in tumours. We’ve developed new factor functions to integrate drug impact on pathway activities. We have established an approach to convert Reactome pathways into Boolean networks (BN) automatically and perform pathway simulations to infer drug and somatic mutation impact. Both PGM- and BN-based pathway modelling approaches and new user-intuitive visualization features have been implemented in ReactomeFIViz, the Reactome Cytoscape app.

• Aurora Blucher, Oregon Health and Science University, United States
• Shannon McWeeney, Oregon Health and Science University, United States
• Lincoln Stein, Ontario Institute for Cancer Research, Canada
• Guanming Wu, Oregon Health and Science University, United States

The precision medicine paradigm is centered on targeted therapies, or drugs designed to interact with particular molecular entities and elicit an anticipated and controlled therapeutic response. To better understand the effects of targeted therapies in patients, we need computational approaches, which are inadequate at present, to map drug targets into a picture providing greater context, such as pathways or networks. To address this need, we have developed a user-friendly intuitive computational tool by enhancing a widely used Cytoscape app, ReactomeFIViz, to assist researchers to visualize, model, and analyze drug and target interactions by facilitating high quality manually curated pathways and a genome-wide human functional interaction network from Reactome, the most comprehensive open source biological pathway knowledgebase. We describe several examples demonstrating the application of our tool for visualizing cancer drugs in the contexts of pathways and networks, and simulating the effect of drug perturbation on pathways based on fuzzy logic models. This pathway- and network-based tool will allow researchers to identify subgroups of patients with shared dysregulated pathways, the commonalities that can provide greater understanding into patient subgroup attributes such as drug sensitivity or resistance, drug toxicity or adverse side effects, and potential opportunities for targeting by combination therapies.

• Florian Auer, University Medical Center Göttingen, Germany
• Frank Kramer, University Medical Center Göttingen, Germany
• Tim Beissbarth, University Medicine Göttingen, Germany

Integration of biological networks into data analyses are common techniques within bioinformatics workflows, but still face problems in reproducibility, particularly in situations that build upon collaborative work. Within the process, the visualization of networks has huge impact on the interpretation, and therefore is an essential step in communicating even intermediate results. Furthermore, the shift of bioinformatics towards systems medicine, and its application within a clinical setting demand the establishment of interconnected and interchangeable components and, moreover, a standardized interface for information exchange with healthcare systems.
We demonstrate a course from data acquisition to the visualization, while providing an interface for healthcare systems by exposing Fast Healthcare Interoperability Resources (FHIR®). Thereby the network data exchange (NDEx) platform and the Cytoscape project form the core components.
We use our R package ndexr to retrieve networks from public and private NDEx installations, and also to store the results for collaboration and publication. Cytoscape is a major tool for the visual exploration of biomedical networks. Beside the graphical interface, it can be accessed by the R package RCy3. To reduce the complexity of software installations, we utilize docker to compose the different components, illustrating their interchangeability and flexibility furthermore.

• Trey Ideker, University of California San Diego, United States

Although cancer is governed by complex molecular systems, the composition and modular organization of these systems remains poorly understood. I will describe efforts by the Cancer Cell Map Initiative (CCMI) to generate large protein interaction maps of tumor cells, which we are integrating with existing molecular and structural data to assemble a comprehensive multiscale map of cancer cell biology. The current draft map contains a hierarchy of ~250 systems covering both known hallmarks and unexpected components. Integration with tumor mutation profiles suggests that the modules under strongest selective pressure in cancer are often not genes, but extend upwards in scale to include protein complexes, broad cellular processes, and organelles. This observation suggests a classification of cancer based on convergence of mutations at key bottlenecks in the hierarchy of cellular systems.

Wrap-up discussions and feedback