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Schedule subject to change
Wednesday, July 15th
10:40 AM-11:20 AM
NetBio Keynote: Network Medicine: From Cellular Networks to the Human Diseasome
Format: Live-stream

  • Albert-Laszlo Barabasi

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Given the functional interdependencies between the molecular components in a human cell, a disease is rarely a consequence of an abnormality in a single gene, but reflects the perturbations of the complex intracellular network. The emerging tools of network medicine offer a platform to explore systematically not only the molecular complexity of a particular disease, leading to the identification of disease modules and pathways, but also the molecular relationships between apparently distinct (patho) phenotypes. Advances in this direction are essential to identify new disease genes, to uncover the biological significance of disease-associated mutations identified by genome-wide association studies and full genome sequencing, and to identify drug targets and biomarkers for complex diseases.

11:20 AM-11:40 AM
Proceedings Presentation: Chromatin network markers of leukemia
Format: Pre-recorded with live Q&A

  • Natasa Przulj, Barcelona Supercomputing Center (BSC), Spain
  • Alfonso Valencia, Barcelona Supercomputing Center (BSC), Spain
  • Noel Malod-Dognin, Barcelona Supercomputing Center (BSC), Spain
  • Vera Pancaldi, Centre de Recherches en Cancerology de Toulouse (CRCT), France

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Motivation: The structure of chromatin impacts gene expression. Its alteration has been shown to coincide with the occurrence of cancer. A key challenge is in understanding the role of chromatin structure in cellular processes and its implications in diseases.

Results: We propose a comparative pipeline to analyze chromatin structures and apply it to study chronic lymphocytic leukemia (CLL). We model the chromatin of the affected and control cells as networks and analyze the network topology by state-of-the-art methods.
Our results show that chromatin structures are a rich source of new biological and functional information about DNA elements and cells that can complement protein-protein and co-expression data. Importantly, we show the existence of structural markers of cancer-related DNA elements in the chromatin. Surprisingly, CLL driver genes are characterized by specific local wiring patterns not only in the chromatin structure network of CLL cells, but also of healthy cells. This allows us to successfully predict new CLL-related DNA elements. Importantly, this shows that we can identify cancer-related DNA elements in other cancer types by investigating the chromatin structure network of the healthy cell of origin, a key new insight paving the road to new therapeutic strategies. This gives us an opportunity to exploit chromosome conformation data in healthy cells to predict new drivers.

12:00 PM-12:20 PM
Proceedings Presentation: Prediction of cancer driver genes through network-based moment propagation of mutation scores
Format: Pre-recorded with live Q&A

  • Karsten Borgwardt, Eidgenoessische Technische Hochschule Zuerich (ETH), Switzerland
  • Anja Gumpinger, Eidgenoessische Technische Hochschule Zuerich (ETH), Switzerland
  • Kasper Lage, Havard Medical School, United States
  • Heiko Horn, BROAD Institute, United States

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Motivation: Gaining a comprehensive understanding of the genetics underlying cancer development and progression is a central goal of biomedical research. Its accomplishment promises key mechanistic, diagnostic and therapeutic insights. One major step in its direction is the identification of genes that drive the emergence of tumors upon mutation. Recent advances in the field of computational biology have shown the potential of combining genetic summary statistics that represent the mutational burden in genes with biological networks, such as protein-protein interaction networks, to identify cancer driver genes. Those approaches superimpose the summary statistics on the nodes in the network, followed by an unsupervised propagation of the node scores through the network. However, this unsupervised setting does not leverage any knowledge on well-established cancer genes, a potentially valuable resource to improve the identification of novel cancer drivers.
Results: We develop a novel node embedding that enables classification of cancer driver genes in a supervised setting. The embedding combines a representation of the mutation score distribution in a node’s local neighborhood with network propagtion. We leverage the knowledge of well-established cancer driver genes to define a positive class, resulting in a partially-labeled data set, and develop a cross validation scheme to enable supervised prediction. The proposed node embedding followed by a supervised classification improves the predictive performance compared to baseline methods, and yields a set of promising genes that constitute candidates for further biological validation.

12:20 PM-12:40 PM
Proceedings Presentation: Network-principled deep generative models for designing drug combinations as graph sets
Format: Pre-recorded with live Q&A

  • Mostafa Karimi, Texas A&M University, United States
  • Arman Hasanzadeh, Texas A&M University, United States
  • Yang Shen, Texas A&M University, United States

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Motivation: Combination therapy has shown to improve therapeutic efficacy while reducing side effects. Importantly, it has become a indispensable strategy to overcome resistance in antibiotics, anti-microbials, and anti-cancer drugs. Facing enormous chemical space and unclear design principles for small-molecule combinations, computational drug-combination design has not seen generative models to meet its potential to accelerate resistance-overcoming drug combination discovery.

Results: We have developed the first deep generative model for drug combination design, by jointly embedding graph-structured domain knowledge and iteratively learning a reinforcement learning-based chemical graph-set designer. First, we have developed Hierarchical Variational Graph Auto-Encoders (HVGAE) trained end-to-end to jointly embed gene-gene, disease-disease and gene-disease networks. Novel attentional pooling is introduced here for learning disease-representations from associated genes' representations. Second, targeting diseases in learned representations, we have recast the drug-combination design problem as graph-set generation and developed a deep learning-based model with novel rewards. Specifically, besides chemical validity rewards, we have introduced novel generative adversarial award, being generalized sliced Wasserstein, for chemically diverse but distributionally similar molecules to known drug-like compounds or drugs. We have also designed a network principle-based reward for drug combinations. Numerical results indicate that, compared to state-of-the-art graph embedding methods, HVGAE learns more generalizable and informative disease representations in disease-disease graph reconstruction. Results also show that the deep generative models generate drug combinations following the principle across diseases. A case study on melanoma shows that generated drug combinations collectively cover the disease module similar to FDA-approved drug combinations and could also suggest promising novel systems-pharmacology strategies. Our method allows for examining and following network-based principle or hypothesis to efficiently generate disease-specific drug
combinations in a vast chemical combinatorial space.

2:00 PM-2:40 PM
NetBio Keynote: Genome-wide phenotypic screens: the total is greater than the sum of the parts
Format: Live-stream

  • Anastasia Baryshnikova

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Connecting genotypes to phenotypes is critical for uncovering gene functions, mapping biological networks and understanding the causes of rare and common diseases. Scalable experimental approaches, including gene editing, silencing and knockout, allow to systematically examine how genetic variation affects an organism, and open the door to new ideas in integrative modeling. Here, I describe the assembly and analysis of the largest set of systematic genetic perturbations to date -- the Yeast Phenome. This dataset combines ~11,000 phenotypic screens of the genome-wide collection of knock-out mutants in budding yeast Saccharomyces cerevisiae, and integrates the work of 280 laboratories and 380 publications. The Yeast Phenome currently provides the largest, richest and most systematic phenotypic description of an organism, and enables a multitude of enquires into the nature of gene-gene, phenotype-phenotype and gene-phenotype networks.

2:40 PM-2:50 PM
BiCoN: Network-constrained biclustering of patients and omics data
Format: Pre-recorded with live Q&A

  • David B. Blumenthal, Technical University of Munich, Germany
  • Markus List, Technical University of Munich,, Germany
  • Jan Baumbach, Technical University of Munich, Germany
  • Tim Kacprowski, Technical University Munich, Germany
  • Olga Lazareva, Technical University of Munich, Germany
  • Van Hoan Do, Gene Center Munich, LMU Munich, Germany
  • Stefan Canzar, Gene Center, LMU, Germany
  • Kevin Yuan, Technical University of Munich, Germany
  • Paolo Tieri, Consiglio Nazionale delle Ricerche, Italy

Presentation Overview: Show

Unsupervised learning approaches are frequently employed to stratify patients into clinically relevant subgroups and to identify biomarkers such as disease-associated genes. However, clustering and biclustering techniques are not suitable to unravel molecular mechanisms along with patient subgroups.

We developed the network-constrained biclustering approach BiCoN (Biclustering Constrained by Networks) which (i) restricts biclusters to functionally related genes connected in molecular interaction networks and (ii) maximizes the difference in gene expression between two subgroups of patients. This allows BiCoN to simultaneously pinpoint molecular mechanisms responsible for the patient grouping. Network-constrained clustering of genes makes BiCoN more robust to noise and batch effects than typical clustering and biclustering methods. BiCoN can faithfully reproduce known disease subtypes as well as novel, clinically relevant patient subgroups, as we could demonstrate using breast and lung cancer datasets.

In summary, BiCoN is a novel systems medicine tool that combines several heuristic optimization strategies for robust disease mechanism extraction. BiCoN is well-documented and freely available as a python package or a web-interface.

Availability: PyPI package: https://pypi.org/project/bicon
Web interface: https://exbio.wzw.tum.de/bicon
Preprint: https://doi.org/10.1101/2020.01.31.926345

2:50 PM-3:00 PM
PhenoGeneRanker: A Tool for Gene Prioritization Using Complete Multiplex Heterogeneous Networks
Format: Pre-recorded with live Q&A

  • Cagatay Dursun, Medical College of Wisconsin and Marquette University, United States
  • Naoki Shimoyama, Marquette University, United States
  • Mary Shimoyama, Medical College of Wisconsin, United States
  • Michael Schläppi, Marquette University, United States
  • Serdar Bozdag, Marquette University, United States

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Identification of specific complex-trait genes is a challenging process as the etiology of those traits involve multiple genes, multiple layers of molecular interactions and environmental factors. Gene prioritization is an important step to make a manageable short list of high likely complex-trait genes. Integration of biological datasets through networks is a promising approach to identify the complex-trait genes by providing natural way of integration of different, complementary genotypic and phenotypic datasets. Integration of different datasets alleviates the effects of missing data, low signal and noisy nature of biomedical datasets. In this study, we present PhenoGeneRanker, a gene prioritization tool which utilizes multi-layer gene and phenotype networks by combining them in a heterogeneous biological network. PhenoGeneRanker enables integration of weighted/unweighted and undirected gene and phenotype networks for wholistic and comprehensive prioritization of genes. It calculates empirical p-values of gene ranking using random stratified sampling of genes based on their degree of centrality in the network to address potential bias toward high degree nodes in the network. To assess PhenoGeneRanker, we applied it on a rice dataset to rank cold tolerance-related genes. Our results showed PhenoGeneRanker successfully ranked genes such that the top ranked genes were enriched in cold tolerance-related GO terms.

3:20 PM-3:40 PM
Understanding tissue-specific gene regulation by miRNAs
Format: Pre-recorded with live Q&A

  • Marieke Kuijjer, Centre for Molecular Medicine Norway (NCMM), University of Oslo, Norway
  • Maud Fagny, Institut National de la Recherche agronomique (INRA), Université Paris-Sud, France
  • Alessandro Marin, Department of Physics, University of Oslo, Norway, Norway
  • John Quackenbush, Harvard Chan School of Public Health, United States
  • Kimberly Glass, Brigham and Women's Hospital, United States

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Conventional methods to analyze genomic data do not make use of the interplay between multiple factors, such as between microRNAs (miRNAs) and the mRNA transcripts they regulate, and thereby often fail to identify the cellular processes that are unique to specific tissues. We developed PUMA (PANDA Using MicroRNA Associations), a computational tool that uses message passing to integrate a prior network of miRNA target predictions with protein-protein interaction and target gene co-expression information to model genome-wide gene regulation by miRNAs. We applied PUMA to 38 tissues from the Genotype-Tissue Expression project, integrating RNA-Seq data with two different miRNA target predictions priors, built on predictions from TargetScan and miRanda, respectively. We found that while target predictions obtained from these two different resources are considerably different, PUMA captures similar tissue-specific miRNA-target gene regulatory interactions in the different network models. Furthermore, tissue-specific functions of miRNAs, which we identified by analyzing their regulatory profiles, are highly similar between networks modeled on the two target prediction resources. This indicates that PUMA consistently captures important tissue-specific regulatory processes of miRNAs. In addition, using PUMA we identified miRNAs regulating important tissue-specific processes that, when mutated, may result in disease development in the same tissue.

3:40 PM-3:50 PM
The Reactome Pathway Knowledgebase: Variants, Dark Proteins and Functional Interactions
Format: Pre-recorded with live Q&A

  • Robin Haw, Ontario Institute for Cancer Research, Canada
  • Lincoln Stein, Ontario Institute for Cancer Research, Canada
  • Peter D'Eustachio, New York University School of Medicine, United States
  • Guanming Wu, Oregon Health and Science University, United States
  • Reactome Consortium, Reactome, Canada
  • Henning Hermjakob, EMBL - European Bioinformatics Institute, United Kingdom

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Reactome is an open access, open source pathway knowledgebase. Its holdings now comprise 12,986 human reactions organized into 2,362 pathways involving 10,908 proteins, 1,865 small molecules, 237 drugs, and 12,206 complexes. 31,237 literature references support these annotations. The roles of variant forms of some proteins, both germline and somatically arising, have been annotated into disease-variant types of reactions and additional reactions that capture the effects of small molecule drugs on these disease processes. To support different visualization and analysis approaches, we implemented several new features through our website, tools, and ReactomeFIViz-Cytoscape app, such as gene set analysis (GSA), an R interface, a Python client, and an intuitive genome-wide results overview based on Voronoi maps. Furthermore, to increase Reactome adoption within the research community, we developed portals and web services for specific user communities. As part of the Illuminating the Druggable (IDG) program, we have undertaken the role to project understudied (Tdark) proteins into the Reactome pathway context, providing useful contextual information for these understudied proteins for experimental biologists to design experiments to understand these proteins’ functions. Reactome thus provides dominant pathway- and network-based tools for analyzing multiple data sets and types.

3:50 PM-4:00 PM
Multiscale Co-expression in the Brain
Format: Pre-recorded with live Q&A

  • Jesse Gillis, Cold Spring Harbor Laboratory, United States
  • Benjamin Harris, Cold Spring Harbor Laboratory, United States
  • Megan Crow, Cold Spring Harbor Laboratory, United States
  • Stephan Fischer, Cold Spring Harbor Laboratory, United States

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The Brain Initiative Cell Census Network (BICCN) single-cell RNA-sequencing datasets provide an unparalleled opportunity to understand how gene-gene relationships shape cell identity. We study gene-gene relationships by measuring the co-variation of gene expression across samples. Because shared expression patterns are thought to reflect shared function, co-expression networks describe the functional relationships between all genes. The heterogeneity of cell types in bulk RNAseq samples creates connections in co-expression networks that potentially obscure identification of co-regulatory modules. Comparison of a bulk RNAseq network built from over 2,000 mouse brain samples from 52 studies to aggregate scRNAseq co-expression networks, made from the 500,000 cells/nuclei across the 7 BICCN datasets, shows consistent topology and co-regulatory signal of reference gene sets and marker gene sets. Differential signals between broad cell classes persist in driving variation at finer levels, indicating that convergent regulatory processes affect cell phenotype at multiple scales.

4:00 PM-4:10 PM
A comparison of normalization and transformation techniques for constructing gene co-expression networks from RNA-seq data
Format: Pre-recorded with live Q&A

  • Arjun Krishnan, Michigan State University, United States
  • Kayla Johnson, Michigan State University, United States

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Constructing gene co-expression networks is a powerful approach for analyzing high-throughput gene expression data towards module finding, gene function prediction, and disease-gene prioritization. While optimal workflows for constructing co-expression networks – including good choices for data pre-processing, normalization, and network transformation – have been developed for microarray-based expression data, such well-tested choices do not exist for RNA-seq data. Almost all studies that compare data processing/normalization methods for RNA-seq focus on the end goal of determining differential gene expression. Here, we present a comprehensive benchmarking of 30 different workflows, each with a unique set of normalization and network transformation methods, for constructing co-expression networks from RNA-seq datasets. We test all these workflows on both large, homogenous data (Genotype-Tissue Expression project) and small, heterogeneous datasets from various labs (submitted to the Sequence Read Archive). Our results demonstrate that choosing the between-sample normalization method has the biggest impact, with trimmed mean of M-values or upper quartile normalization producing networks that most accurately recapitulates known tissue-naive and tissue-specific gene functional relationships. Furthermore, we provide insights as to when other methods should be used and which experimental factors, including sample size, noticeably affect network accuracy.

4:10 PM-4:20 PM
Supervised prediction of aging-related genes from a dynamic context-specific protein interaction subnetwork
Format: Pre-recorded with live Q&A

  • Tijana Milenkovic, University of Notre Dame, United States
  • Qi Li, University of Notre Dame, United States

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Human aging is linked to many diseases. Because the aging process is influenced by genetic factors, it is important to identify human aging-related genes. We focus on supervised prediction of such genes. Gene expression-based methods for this purpose study genes in isolation from each other. While protein-protein interaction (PPI) network-based methods account for interactions between genes' protein products, current PPI network data are context-unspecific, spanning different biological conditions. Instead, we analyze an aging-specific subnetwork of the entire context-unspecific PPI network, obtained by integrating aging-specific gene expression data and PPI network data. We are the first to propose a supervised learning method for predicting aging-related genes from an aging-specific PPI subnetwork. In a comprehensive evaluation, we find that: (i) using an aging-specific subnetwork yields more accurate aging-related gene predictions than using the entire context-unspecific network, (ii) using a dynamic aging-specific subentwork is superior to using all static aging-specific subnetworks, and (iii) predictive methods that we propose outperform existing methods for the same purpose. Our best method achieves impressively high accuracy of 90\%-95\% (depending on the measure), compared to 72\%-80\% by the next best method. Our method could guide with high confidence the discovery of novel aging-related genes for wet lab validation.

4:20 PM-4:40 PM
Proceedings Presentation: GLIDE: Combining Local Methods and Diffusion State Embeddings to Predict Missing Interactions in Biological Networks
Format: Pre-recorded with live Q&A

  • Kapil Devkota, Tufts University, United States
  • James Murphy, Tufts University, United States
  • Lenore Cowen, Tufts University, United States

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Motivation: One of the core problems in the analysis of biological networks is the link prediction problem. In particular, existing interactions networks are noisy and incomplete samples of the true network, with many true links missing because those interactions have not yet been experimentally observed. Methods to predict missing links have been more extensively studied for social than for biological networks; a recent paper of Kovacs et al. argued that there is some special structure in PPI network data that might mean that alternate methods may outperform the best methods for social networks.
Based on a generalization of the diffusion state distance (DSD), we design a new embedding-based link prediction method called GLIDE (Global and Local Integrated Diffusion Embedding). GLIDE is designed to effectively capture global network structure, combined with alternative network type-specific customized measures that capture local network structure. We test GLIDE on a classical version of the yeast PPI network as well as a collection of three recently curated human biological networks derived from the 2016 DREAM disease module identification challenge in rigorous cross validation experiments.

Results: We indeed find that different local network structure is dominant in different types of biological networks. We find that the simple local network measures are dominant in the highly connected network core between hub genes, but that GLIDE's global embedding measure adds value in the rest of the network. For example, we make GLIDE-based link predictions from genes known to be involved in Crohn's disease, to genes that are not known to have an association, and make some new predictions, finding support in other network data and the literature.

Availability: GLIDE can be downloaded at:

5:00 PM-5:20 PM
Proceedings Presentation: Combining phenome-driven drug target prediction with patients’ electronic health records-based clinical corroboration towards drug discovery
Format: Pre-recorded with live Q&A

  • Mengshi Zhou, Case Western Reserve University, United States
  • Chunlei Zheng, Case Western Reserve University, United States
  • Rong Xu, Case Western Reserve University, United States

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Predicting drug-target interaction (DTIs) using human phenotypic data has the potential in eliminating the translational gap between animal experiments and clinical outcomes in humans. One challenge in human phenome-driven DTI predictions is integrating and modeling diverse drug and disease phenotypic relationships. Leveraging large amounts of clinical observed phenotypes of drugs and diseases and electronic health records (EHRs) of 72 million patients, we developed a novel integrated computational drug discovery approach by seamlessly combining DTI prediction and clinical corroboration.
We developed a network-based DTI prediction system (TargetPredict) by modeling 855,904 phenotypic and genetic relationships among 1430 drugs, 4251 side effects,1059 diseases, and 17,860 genes. We systematically evaluated TargetPredict in de novo cross-validation and compared it to a state-of-art phenome-driven DTI prediction approach. We applied TargetPredict in identifying novel repositioned candidate drugs for Alzheimer’s disease (AD), a disease affecting over 5.8 million people in the United States. We evaluated the clinical efficiency of top repositioned drug candidates using EHRs of over 72 million patients.
Results: The area under the receiver operating characteristic (ROC) curve was 0.97 in the de novo cross-validation when evaluated using 910 drugs. TargetPredict outperformed a state-of-art phenome-driven DTI prediction system as measured by precision-recall curves (MAP: 0.28 versus 0.23, p-value<0.0001). The EHR-based case-control studies identified top-ranked repositioned drugs that are significantly associated with lower odds of AD. For example, we showed that liraglutide, a type 2 diabetes drug, is significantly associated with decreased risk of AD (AOR: 0.76; 95% CI (0.70,0.82), p-value < 0.0001).
In summary, our integrated approach that seamlessly combines computational DTI prediction and large-scale patient EHRs-based clinical corroboration has high potential in rapidly identifying novel drug targets and drug candidates for complex diseases.

5:20 PM-5:40 PM
Proceedings Presentation: Network-based characterization of disease–disease relationships in terms of drugs and therapeutic targets
Format: Pre-recorded with live Q&A

  • Yoshihiro Yamanishi, Kyushu Institute of Technology, Japan
  • Midori Iida, Kyushu Institute of Technology, Japan
  • Michio Iwata, Kyushu Institute of Technology, Japan

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Pathogenesis is generally considered as disease-specific, yet characteristic molecular features are often common to various diseases. Disease states are often characterized by altered gene expression levels. Thus, similarities between diseases can be explained by characteristic gene expression patterns. However, most disease-disease relationships remain uncharacterized. In this study, we proposed a novel approach for network-based characterization of disease–disease relationships in terms of drugs and therapeutic targets. We performed large-scale analyses of omics data and molecular interaction networks for 79 diseases, including adrenoleukodystrophy, leukemia, Alzheimer's disease, asthma, atopic dermatitis, breast cancer, cystic fibrosis, and inflammatory bowel disease. We quantified disease–disease similarities based on proximities of abnormally expressed genes in various molecular networks and showed that similarities between diseases could be explained by characteristic molecular net-work topologies. Furthermore, we developed a kernel matrix regression algorithm to predict the commonalities of drugs and therapeutic targets among diseases. Our comprehensive prediction strategy indicated many new associations among phenotypically diverse diseases.

5:40 PM-6:00 PM
Membrane protein-regulated networks across human cancers
Format: Pre-recorded with live Q&A

  • Chun-Yu Lin, Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Taiwan
  • Chia-Hwa Lee, School of Medical Laboratory Science and Biotechnology, Taipei Medical University, Taiwan
  • Yi-Hsuan Chuang, Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Taiwan
  • Jung-Yu Lee, Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Taiwan
  • Yi-Yuan Chiu, Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Taiwan
  • Yan-Hwa Wu Lee, Department of Biological Science and Technology, National Chiao Tung University, Taiwan
  • Yuh-Jyh Jong, Graduate Institute of Medicine, College of Medicine, Kaohsiung Medical University, Taiwan
  • Jenn-Kang Hwang, Warshel Institute for Computational Biology, The Chinese University of Hong Kong, Hong Kong
  • Sing-Han Huang, Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Taiwan
  • Li-Ching Chen, TMU Research Center of Cancer Translational Medicine, Taipei Medical University, Taiwan
  • Chih-Hsiung Wu, Department of Surgery, School of Medicine, College of Medicine, Taipei Medical University, Taiwan
  • Shih-Hsin Tu, TMU Research Center of Cancer Translational Medicine, Taipei Medical University, Taiwan
  • Yuan-Soon Ho, School of Medical Laboratory Science and Biotechnology, Taipei Medical University, Taiwan
  • Jinn-Moon Yang, Institute of Bioinformatics and Systems Biology, National Chiao Tung University, Taiwan

Presentation Overview: Show

Alterations in membrane proteins (MPs) and their regulated pathways have been established as cancer hallmarks and extensively targeted in clinical applications. However, the analysis of MP-interacting proteins and downstream pathways across human malignancies remains challenging. Here, we present a systematically integrated method to generate a resource of cancer membrane protein-regulated networks (CaMPNets), containing 63,746 high confidence protein–protein interactions (PPIs) for 1962 MPs, using expression profiles from 5922 tumors with overall survival outcomes across 15 human cancers. Comprehensive analysis of CaMPNets links MP partner communities and regulated pathways to provide MP-based gene sets for identifying prognostic biomarkers and druggable targets. For example, we identify CHRNA9 with 12 PPIs (e.g., ERBB2) can be a therapeutic target and find its anti-metastasis agent, bupropion, for treatment in nicotine-induced breast cancer. This resource is a study to systematically integrate MP interactions, genomics, and clinical outcomes for helping illuminate cancer-wide atlas and prognostic landscapes in tumor homo/heterogeneity.

Thursday, July 16th
10:40 AM-11:20 AM
NetBio Keynote: A systematic approach to orient the human protein-protein interaction network
Format: Live-stream

  • Roded Sharan

Presentation Overview: Show

The protein-protein interaction (PPI) network of an organism serves as a skeleton for its signaling circuitry, which mediates cellular response to environmental and genetic cues. Understanding this circuitry could improve the prediction of gene function and cellular behavior in response to diverse signals. To realize this potential, one has to comprehensively map PPIs and their directions of signal flow. While the quality and the volume of identified human PPIs improved dramatically over the last decade, the directions of these interactions are still mostly unknown, thus precluding subsequent prediction and modeling efforts. In this talk I will review early work by my group to orient the PPI network of yeast, present the challenge of generalizing these orientation efforts to human, and describe a recent diffusion-based approach to orient the human PPI network using drug response and cancer genomic data. I will show how the oriented network leads to improved prioritization of cancer driver genes and drug targets compared to the state-of-the-art unoriented network.

11:20 AM-11:40 AM
Proceedings Presentation: Identifiability and experimental design in perturbation studies
Format: Pre-recorded with live Q&A

  • Nils Blüthgen, Charité – Universitätsmedizin Berlin, Germany
  • Torsten Gross, IRI Life Sciences, Humboldt University, Berlin, Germany, Germany

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Motivation: A common strategy to infer and quantify interactions between components of a biological system is to deduce them from the network’s response to targeted perturbations. Such perturbation experiments are often challenging and costly. Therefore, optimising the experimental design is essential to achieve a meaningful characterisation of biological networks. However, it remains difficult to predict which combination of perturbations allows to infer specific interaction strengths in a given network topology. Yet, such a description of identifiability is necessary to select perturbations that maximize the number of inferable parameters.
Results: We show analytically that the identifiability of network parameters can be determined by an intuitive maximum flow problem. Furthermore, we used the theory of matroids to describe identifiability relationships between sets of parameters in order to build identifiable effective network models. Collectively, these results allowed to device strategies for an optimal design of the perturbation experiments. We benchmarked these strategies on a database of human pathways. Remarkably, full network identifiability was achieved with on average less than a third of the perturbations that are needed in a random experimental design. Moreover, we determined perturbation combinations that additionally decreased experimental effort compared to single-target perturbations. In summary, we provide a framework that allows to infer a maximal number of interaction strengths with a minimal number of perturbation experiments.
Availability: IdentiFlow is available at github.com/GrossTor/IdentiFlow.

12:00 PM-12:20 PM
Estimating Dispensable Content in the Human Interactome
Format: Pre-recorded with live Q&A

  • Yu Xia, McGill University, Canada
  • Mohamed Ghadie, McGill University, Canada

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Protein-protein interaction (PPI) networks (interactome networks) have successfully advanced our knowledge of molecular function, disease and evolution. While much progress has been made in quantifying errors and biases in experimental PPI datasets, it remains unknown what fraction of the error-free PPIs in the cell are completely dispensable, i.e., effectively neutral upon disruption. Here, we estimate dispensable content in the human interactome by calculating the fractions of PPIs disrupted by neutral and non-neutral mutations. Starting with the human reference interactome determined by experiments, we construct a human structural interactome by building homology-based three-dimensional structural models for PPIs. Next, we map common mutations from healthy individuals as well as Mendelian disease-causing mutations onto the human structural interactome, and perform structure-based calculations of how these mutations perturb the interactome. Third, we integrate these results to calculate the probabilities for common mutations (assumed to be neutral) and disease-causing mutations (assumed to be non-neutral) to disrupt human PPIs. Finally, we apply Bayes’ theorem to calculate the probabilities for human PPIs to be neutral or non-neutral upon disruption. Using our predicted as well as experimentally-determined interactome perturbation patterns by common and disease mutations, we estimate that <~20% of the human interactome is completely dispensable.

12:20 PM-12:30 PM
Identifying Regulators of Infection in Virus-Host Networks
Format: Pre-recorded with live Q&A

  • Emily Ackerman, University of Pittsburgh, United States
  • Jason Shoemaker, University of Pittsburgh, United States

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Though treatment is predictably needed for viral outbreaks such as influenza A, there are only three CDC approved influenza virus treatments. Identification of disrupted cellular regulators during infection provides candidates for development efforts. Here, two novel methods of PPI network analysis have been analyzed for their ability to predict host factors of influenza A virus replication. First, a novel disease subnetwork was extracted and studied for its enrichment of virus replication host factors. Replication assays show that subnetwork proteins are significantly more enriched for host factors compared to virus interacting proteins (p=0.024), are significantly enriched in 6 previous siRNA screens (p=1.1 × 10−05) and are functionally from virus interacting proteins. The second method uses two network adaptations of classic controllability analysis to identify key regulators of healthy and infected network behavior. A set of 24 proteins are predicted as indicators of infected state regulatory roles: results which agree with siRNA studies of host factors involved in influenza replication as well as hold NF-kB pathway and infection significance. Lastly, similar drug targeting predictions for the novel SARS-CoV-2 virus will be discussed. In total, network methods are a successful, efficient way to identify possible targets for drug development efforts.

12:30 PM-12:40 PM
Modeling and Identifying Regulatory Patterns within Chaotic Metabolic Networks
Format: Pre-recorded with live Q&A

  • Jordan Berg, Department of Biochemistry, University of Utah, United States
  • Youjia Zhou, School of Computing, University of Utah; Scientific Computing and Imaging Institute, University of Utah, United States
  • Bei Wang, School of Computing, University of Utah; Scientific Computing and Imaging Institute, University of Utah, United States
  • Jared Rutter, Department of Biochemistry, University of Utah; Howard Hughes Medical Institute, University of Utah, United States

Presentation Overview: Show

Metabolism-related -omics dataset analysis has historically relied on limited approaches in an effort to transition from reductionist to systematic analytical methods. Systematic approaches are necessary due to the scale and complexity of metabolism. However, current conventions in systems approaches often drive scientists to focus their efforts on independent, high-magnitude components from their datasets. By improving holistic approaches, each component’s contribution to the metabolic system and the consequences of their behavior and interactions with one another will be better discerned. We developed Metaboverse, a user-friendly, interactive, and cross-platform analysis and visualization tool for metabolic networks across model organisms. Metaboverse offers the ability to integrate various -omics datasets to aid scientists in rapidly generating new hypotheses that may have been missed by available approaches. In Metaboverse, we introduce two novel contributions to network biology that handle the identification of network-centric regulatory patterns in the user data and address data sparsity in the metabolic network data contextualization. This framework will be foundational in allowing users to analyze their data in a way that holistically integrates all measured information of their biological system to rapidly identify interesting regulatory patterns within the global biological network.