BioNetVisA workshop
From biological network reconstruction to datavisualization and analysis in molecular biology
and medicine
Stavros Niarchos Foundation Cultural Center (SNFCC)
Leof. Andrea Siggrou 364, Kallithea 176 74
Athens, Greece

9 September 2018

The BioNetVisA workshop will bring together different actors of network biology fromdatabase providers, networks creators, computational biologists, biotech companies involved
in data analysis and modeling to experimental biologists, clinicians that use systems biology
approaches. The participants will be exposed to the different paradigms of network biology
and the latest achievements in the field.
The goal of BioNetVisA workshop is to build a discussion around various approaches forbiological knowledge formalisation, data integration and analysis; compatibility between
different methods and biological networks resources available the field; applicability forconcrete research and clinical projects depending on scientific question and type of highthroughput data.
The BioNetVisA workshop aims at identifying bottlenecks and proposing short- and longterm objectives for the community as discussing questions about accessibility of available
tools for wide range of user in every-day standalone application in biological and clinical labs.
In addition, the possibilities for collective efforts by academic researchers, clinicians, biotech
companies and future development directions in the field will be discussed.

Organizers

Inna Kuperstein (Institut Curie, France)
Emmanuel Barillot (Institut Curie, France)
Andrei Zinovyev (Institut Curie, France)
Luis Cristobal Monraz Gomez (Institut Curie, France)
Hiroaki Kitano (Okinawa Institute of Science and Technology Graduate University, RIKEN
Center for Integrative Medical Sciences, Japan)
Minoru Kanehisa (Institute for Chemical Research, Kyoto University, Japan)
Samik Ghosh (Systems Biology Institute, Tokyo, Japan)
Robin Haw (Ontario Institute for Cancer Research, Canada)
Alfonso Valencia (Spanish National Bioinformatics Institute, Madrid, Stain)
Lodewyk Wessels (Netherlands Cancer Institute, Amsterdam, Netherlands)
Patrick Kemmeren (Princess Maxima Center for Pediatric Oncology, Utrecht, Netherlands)
Web site
http://eccb18.org/workshop-2/
https://bionetvisa.github.io/

Venue
https://www.snfcc.org/default.aspx

Contactbionetvisa@curie.fr

Sponsor

BioNetVisA workshop program
09:00 - 10:30 Session 1: Signalling and metabolic network resources
Chair: Vassili Soumelis
09.00-09.20 Talk 1
Antonio Fabregat (EMBL-EBI, Hinxton, UK)
The Reactome Pathway Knowledgebase
09.20-09.40 Talk 2
Cristobal Monraz Gomez (Institute Curie, Paris, France)
Atlas of Cancer Signaling Network: a resource of multi-scale biological maps tostudy disease mechanisms
09.35-10.50 Talk 3
Joaquin Dopazo (Fundación Progreso y Salud, Seville, Spain)
Navigating through disease maps
10.50-10.05 Talk 4
Augustin Luna (Dana-Farber Cancer Institute/Harvard University, Boston, UnitedStates)Access and Discover Biological Pathway Information from Pathway Commons
10.30-11.00 Coffee break

Session 2: Platforms and methods for analysis of complex networks
Chair: Yvan Saeys
11.00-11.25 Talk 5
Georgios Pavlopoulos (Biomedical Sciences Research Center "Alexander Fleming",
Athens, Greece)
Using HipMCL, a high-performance parallel implementation of the Markov
clustering algorithm, to understand microbial diversity
11.25-11.50 Talk 6
Anna Niarakis (GenHotel EA3886, Univ Evry, Université Paris-Saclay, France)
Disease Networks - Reconstruction, Topology, Dynamics. Towards anautomated pipeline from static representations to executable disease models
11.55-12.15 Talk 7
Barbara Shih (The Roslin Institute and Royal (Dick) School of Veterinary Studies, theUniversity of Edinburgh, Easter Bush, Midlothian, Edinburgh, UK)
A platform for the network assembly and visual analysis of transcript isoforms
from short-read RNA-sequencing data
12.15-12.30 Talk 8
Warren W. Kretzschmar (KTH Royal Institute of Technology, Stockholm, Sweden)
Multicolored De Bruijn graph visualization of first, second, and third
generation RNA-seq offers deeper insight into transcript isoforms in Picea abies
12.30-13.30 Lunch

Session 3: Biological networks in single-cell data analysis
Chair: Augustin Luna
13.30-13.55 Talk 9
Yvan Saeys ISAC Lecture (VIB-Ghent University, Gent, Belgium)
Inferring dynamic regulatory networks from single cell data
13.55-14.20 Talk 10
Andrei Zinovyev (Institute Curie, Paris, France)
Biological networks help unraveling tumoral heterogeneity at single cell level
14.20-14.40 Talk 11
Anirudh Patir (The University of Edinburgh, Edinburgh, UK)
Evaluation of network methods for the analysis scRNA-seq data anddevelopment of a new KNN-based method based on identified caveats
14.40-15.00 Talk 12
Vassili Soumelis (Institute Curie, FR)
Immune cell diversification in response to a single stimulus: from single cells to
subsets and back15.00-15.30 Coffee break

Session 4: Biological networks modelling in drug repositioning anddisease comorbidity
Chair: Joaquin Dopazo
15.30-16.15 Talk 13
Alfonso Valencia Keynote Lecturer (Barcelona Supercomputing Center, Barcelona,
Spain)
Disease networks for the study of the molecular basis of disease comorbidity
16.15-16.35 Talk 14
Celine Hernandez (Institut de Biologie de l’Ecole Normale Supérieure, Paris, France)
Dynamical modelling of T cell co-inhibitory pathways to predict anti-tumour
responses to checkpoint inhibitors
16.35-16.55 Talk 15
Sven Bergmann (University of Lausanne & Swiss Institute of Bioinformatics,
Switzerland)
Open Community Challenge Reveals Molecular Network Modules with Key Rolesin Diseases
16.25-16.45 Talk 16
Josephine Daub (Princess Máxima Center for Pediatric Oncology, Utrecht, The
Netherlands)
Genetic interactions in childhood cancer

BioNetVisA workshop abstracts
Talk 1The Reactome Pathway Knowledgebase
Antonio Fabregat1, Konstantinos Sidiropoulos1, Guilherme Viteri1, Pascual Lorente1, Cristoffer Sevilla1,
Thawfeek Varusai1, Guanming Wu2, Lincoln Stein3, Peter D'Eustachio4 and Henning Hermjakob1
1

2

3

4

EMBL-EBI, Hinxton, UK1, OHSU, Portland, USA , OIRC, Toronto, Canada, NYUMC, New York, USA

The Reactome Knowledgebase (https://reactome.org) provides molecular details of signaltransduction, transport, DNA replication, metabolism, and other cellular processes as an ordered
network of molecular transformations—an extended version of a classic metabolic map, in a single
consistent data model. Reactome functions both as an archive of biological processes and as a toolfor discovering unexpected functional relationships in data. To support the continued brisk growth inits size and complexity, we have implemented a series of improvements.
Aiming to reduce the complexity of the represented knowledgebase and to enable a morestraightforward access to its content, Reactome provides its data in a Neo4j graph database(available at https://reactome.org/download-data). Neo4j’s query language, Cypher, allows queries
to be written in a more intuitive way and reduces the average response time per query by 93%.
Additionally, Reactome has improved the performance of its data analysis tools, and adopted new
data structures and strategies to boost diagram viewer performance. To make our website moreaccessible to human users, we have improved our multiscale pathway visualisation by implementing
interactive, textbook-style illustrations. To ensure consistency in the visual representation of pathway
diagrams we created a freely accessible (under a CC-BY 4.0 licence) Icon Library(https://reactome.org/icon-lib) that includes icons ranging from simple protein labels torepresentations of organelles, receptors and cell types.
In response to user feedback, the Reactome knowledgebase portal has been simplified andreorganised following a cleaner and responsive design that allows seamless browsing on a variety of
portable
devices.
To
encourage
re-useofourcontent,
ourwebservice(https://reactome.org/ContentService) allows exporting of pathway diagrams to raster and vector
images, as well as to ‘PowerPoint’ files. Also, analysis results can be downloaded in easy to follow PDF
documents.

Talk 2Atlas of Cancer Signaling Network: a resource of multi-scale biological maps to study
disease mechanisms
L. Cristobal Monraz Gomez, Emmanuel Barillot, Andrei Zinovyev and Inna Kuperstein
Institut Curie, Paris, France

We present here the second edition of Atlas of Cancer Signaling Network (ACSN2.0,
https://acsn.curie.fr). ACSN is a web-based resource of multi-scale biological maps depicting molecular
processes in cancer cell and tumor microenvironment. The core of the Atlas is a set of interconnectedcancer-related signaling and metabolic network maps. Molecular mechanisms are depicted on the maps
at the level of biochemical interactions, forming a large seamless network of above 8000 reactions
covering close to 3000 proteins and 800 genes and based on more than 4500 scientific publications.
Constructing and updating ACSN involves careful manual curation of molecular biology literature andthe participation of experts in the corresponding fields.
The maps of ACSN2.0 are interconnected, the regulatory loops within cancer cell and between cancer
cell and tumor microenvironment are systematically depicted. The cross-talk between signalingmechanisms and metabolic processes in the cancer cells is explicitly depicted thanks to new feature of
the Atlas: ACSN2.0 is now connected to RECON metabolic network, the largest graphical representationof human metabolism.
The Atlas is a "geographic-like" interactive "world map" of molecular interactions leading the hallmarksof cancer as described by Hanahan and Weinberg. The Atlas is created with the use of systems biology
standards and amenable for computational analysis. As of today, ACSN2.0 is composed of 13
comprehensive maps of molecular interactions. There are six maps covering signalling processes
involved in cancer cell and four maps describing tumor microenvironment. In addition, there are 3 cell
type-specific maps describing signaling within different cells types frequently surrounding andinteracting with cancer cells. This feature of ACSN2.0 reflects the complexity of tumor
microenvironment.
The resource includes tools for map navigation, visualization and analysis of molecular data in thecontext of signaling network maps.

Talk 3
Navigating through disease maps
Joaquín Dopazo
Clinical Bioinformatics Area, Fundacion Progreso y Salud, Seville, Spain

Despite the increasing availability of genomic and transcriptomic data, there is still a gap between thedetection of perturbations in gene expression and the understanding of their contribution to themolecular mechanisms that ultimately account for the phenotype studied. Disease maps
(http://disease-maps.org/projects) and other generic maps that recapitulate cell signaling, metabolism
and functionality (e.g. KEGG, WikiPathways, etc.) offer a detailed picture on the complex network ofinterrelationships among genes that result in cell activity decisions.
Alterations in the in the functioning of such networks are behind the initiation and progression of many
diseases, including cancer. The wealth of available knowledge on biological networks can therefore beused to derive mechanistic models that link gene expression perturbations to changes in metabolic,signaling, etc. activities that provide relevant clues on molecular mechanisms of disease and drug modesof action (MoA).
Here we present simple mechanistic models of signaling (hipathia) and metabolic (Metabolizer) activity
based on modules defined as functionally substantiated circuits within pathways. The models have been
implemented as web-based applications, which offer intuitive, easy-to-use interactive interfaces toanalyze differences in pathway activities that can also be used for class prediction and in silico predictionof Knock-Out (KO) effects. We provide different types of validations of some of the predictions made by
the models.
Metabolizer can be found at: http://metabolizer.babelomics.org
Hipathia can be found at: http://hipathia.babelomics.org A Bioconductor/R package can be found at:
http://bioconductor.org/packages/devel/bioc/html/hipathia.html

Talk 4
Access and Discover Biological Pathway Information from Pathway Commons
Augustin Luna1,2, Emek Demir3, Igor Rodchenkov4, Özgün Babur3, Jeffrey V Wong4, Chris Sander1,2 andGary Bader4
1

2

Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston MA, USA, Cell Biology, Harvard Medical
3
4School, Boston MA, USA, Oregon Health & Science University, Portland OR, USA, University of Toronto, Toronto, Canada

Pathway Commons (pathwaycommons.org) serves researchers by integrating data from public pathway
and interaction databases and disseminating this data in a uniform fashion. The knowledge base iscomprised of metabolic pathways, genetic interactions, gene regulatory networks and physicalinteractions involving proteins, nucleic acids, small molecules and drugs. Alongside attempts to increase
the scope and types of data, a major focus has been the creation of user-focused tools and resourcesthat facilitate access, discovery and application of existing pathway information to aid day-to-day
activities of biological researchers. Pathway Commons offers a number of tools for accessing andsearching the integrated datasets. File downloads are available in Biological Pathway Exchange (BioPAX),
Simple Interaction Format (SIF) and gene set (GMT) formats. Provided web services allow for integration
with external tools and support full-text search of available data. Results from the web services isprovided in the aforementioned formats, as well as, the Systems Biology Graphical Notation Markup
Language (SBGNML) and Javascript Object Notation for Linked Data (JSON-LD) formats. Pathway
Commons is also accessible via a number of web applications built on reusable Javascript components,
including: 1) PCViz that allows the visualization of interaction data as simplified binary interaction
networks from input gene names and 2) a web-based ‘Search’ application that enables users to query
pathways by keyword and visualize returned pathways using SBGN with an automated layout. These
web applications complement existing desktop software add-ons linking Pathway Commons to the
Cytoscape (CyPath2) network analysis tool and the R (paxtoolsr) programming language. We additionallyprovide an online guide for those wishing to learn more about pathway resources and analysis withpublished case studies and guided workflows. Ongoing development of web applications will enhance
the accessibility to pathways and integrate support for visualization and interpretation of experimental
data.

Talk 5Using HipMCL, a high-performance parallel implementation of the Markov clustering
algorithm, to understand microbial diversity
Georgios A. Pavlopoulos
Biomedical Sciences Research Center "Alexander Fleming", Athens, Greece

While various clustering algorithms have been proposed to find highly connected regions within abiological network, Markov Clustering (MCL) has been one of the most successful approaches. Despite
its popularity, MCL’s scalability suffers from high running times and memory demands. Here, we present
the High-performance MCL (HipMCL), a parallel implementation of the original MCL algorithm that canrun on distributed-memory computers. HipMCL can efficiently utilize 2000 compute nodes and cluster a
network of ∼70 million nodes with ∼68 billion edges in ∼2.4 h. To demonstrate its capabilities, we show
how we use HipMCL to understand the biological diversity and discover novel protein clusters.

Talk 6
Disease Networks - Reconstruction, Topology, Dynamics. Towards an automatedpipeline from static representations to executable disease models.
Anna Niarakis
GenHotel EA3886, Univ Evry, Université Paris-Saclay, France

Disease maps have been an emerging concept as a useful and intuitive way of describing diseasemechanisms in a systematic fashion. Based on information mining, human curation and experts’ advice,
they summarize current biological pathway knowledge in a standard, comprehensive representationthat is both human and machine readable. Disease maps can serve as templates for visualization andanalysis of omic datasets, or they can be analyzed in terms of their underlying network structure.
However, their static nature provides relatively limited understanding concerning the emerging behaviorof the system under different conditions. Computational modelling can reveal dynamical properties of
the network by in silico simulations and perturbations and can be further used for hypotheses testingand predictions. In order to address the lack of kinetic data and the large size of the biological pathways
described in a Disease map, Boolean modelling can be employed to study the system’s qualitative
dynamic behavior.
In this talk I will present our efforts to establish an automated pipeline starting from a fully detailed
Disease map and its analysis as a complex network, all the way to the automated inference of a
dynamical (Boolean) model, based on network topology and semantics, creating thus “executable”
disease networks. I will use Rheumatoid Arthritis as case study.

Talk 7
A platform for the network assembly and visual analysis of transcript isoforms fromshort-read RNA-sequencing data
Barbara Shih1, Neil A. Mabbott1, Tom C. Freeman1,2
1

The Roslin Institute and Royal (Dick) School of Veterinary Studies, the University of Edinburgh, Easter Bush, Midlothian,2
Edinburgh EH25 9RG, UK, Kajeka Ltd., Roslin Innovation Building, Easter Bush, Midlothian, Edinburgh EH25 9RG, UK

RNA-sequencing (RNA-seq) data describes both transcript abundance and exon usage. However, splicing
events can be complex, and therefore difficult to visualise and interpret.
Here, we describe a new data pipeline/visualisation platform developed to support the representationof RNA-seq data as networks (RNA-assembly graphs), thereby providing a visual representation of data
structure that facilitates interpretation of exon/intron use. The pipeline requires the following files:
input BAM files, the corresponding GTF file, and genes of interest. Two approaches are available within
the pipeline for the graph generation. The first is based on comparing sequence similarity, e.g. by Blast,whereby the similarity scores are used to define edges between reads (nodes). However, generation ofgraphs using this approach can be computationally expensive (due to the blast step) and graphs can bevery large. In order to circumvent these issues, a second approach is based on mapping reads to specificloci, i.e. regions of the genome, which are represented as nodes, and edges are defined by the numberof reads spanning across regions. To visualise both RNA-assembly graph types we have also beendeveloping a new network analysis platform, called Graphia (Kajeka Ltd). This platform not only supports
the visualisation of massive graphs (millions of nodes of edges), but supports the overlay of otherinformation, e.g. gene or exon IDs, dynamic filtering on edge weight or source as well as a range of otherfunctionalities.
Using real and simulated short-read RNA-seq data we have demonstrated that different splicing events(alternative start/end, exon skipping and mutually exclusive exons) produce distinct network structuresby both approaches. However, the implementation of the loci-based approach drastically simplifies thegraphs, enabling direct comparison of exon usage across different samples. We believe this approachsignificantly improves our ability to interpret the often complex splicing captured by RNA-seq analysis.

Talk 8Multicolored De Bruijn graph visualization of first, second, and third generation RNAseq offers deeper insight into transcript isoforms in Picea abies
Warren W. Kretzschmar1, Shirin Akhter2, Veronika Nordal2, Nicolas Delhomme2, Nathaniel R. Street2,
Ove Nilsson3, Olof Emanuelsson1, Jens F. Sundström2
1

2

3

KTH Royal Institute of Technology, Stockholm, Sweden, Swedish University of Agricultural Sciences, Sweden, UmeåUniversity, Umeå, Sweden

Third-generation sequencing technologies offer cheap, long reads, but with higher error rates thanreads from first or second generation technologies. In transcriptome assembly, these technologies arecomplementary. However, useful approaches to combining data from all these technologies are needed.
De Bruijn graphs, a staple of transcriptome assemblers, offer a natural way of combining low-error reads
into a graph. Multicolored De Bruijn graphs allow the joint representation of read informationfrom multiple sequencing technologies.
We present a visualization of Picea abies transcript isoforms assembled from Sanger, Illumina, and PacBio IsoSeq reads based on a multicolored De Bruijn graph. This visualization allows close assessment ofread support for transcript isoforms from all three technologies simultaneously.

Talk 9Inferring dynamic regulatory networks from single cell data
Yvan Sayes
VIB-Ghent University, Gent, Belgium

High-throughput methods for measuring proteins and RNA at the single-cell level are currently
revolutionizing the life sciences. Examples include multicolor flow cytometry, mass cytometry, imaging
flow/mass cytometry and single-cell transcriptomics. However, the success of these novel technologies
crucially depends on computational methods to interpret, visualize, and model these novel data types.
In this talk, I will present a novel class of techniques to infer dynamic regulatory networks from singlecell data, as well as a novel benchmarking framework that can be used to evaluate dynamic regulatory
networks and stimulate their further developments. Case studies will be presented on immune celldifferentiation.

Talk 10
Biological networks help unraveling tumoral heterogeneity at single cell level
Andrei Zinovyev
Institut Curie/INSERM, Paris, France

Single cell measurements change the modern biology due to bringing the ‘Big Data’-related
approaches and challenges to the studies of normal physiology and diseases such as cancer. A
number of novel computational methods and paradigms have emerged to deal with complexity ofsingle cell genomic and epigenomic data. I will present our recent single cell study of heterogeneity oftumors of Ewing sarcoma, starting from characterizing the cell cycle-independent transcriptional
program of EWS/FLI-1 oncogene in inducible cell line and finishing by the analysis of patient-derived
xenografts profiled with 10x Genomics platform. Our study shows that the tumors of Ewing sarcoma
are characterized by intratumoral heterogeneity strongly associated with activity of the EWS/FLI-1
oncogene, with existence of tumor cell subpopulations characterized by specific and unexpectedbiological properties. Interestingly, projecting the results of single cell data analysis on top of thebiological network maps allowed us to obtain insights into the mechanisms shaping intratumoral
heterogeneity at single cell level.

Talk 11
Evaluation of network methods for the analysis scRNA-seq data and development of a
new KNN-based method based on identified caveats
Anirudh Patir and Tom Freeman
The University of Edinburgh, Edinburgh, UK

Advances in single-cell RNA-seq (scRNA-seq) analyses have revolutionised the ability to characterise
cellular heterogeneity and opened up new opportunities to study cell and tissue biology indevelopment or disease, e.g. tracking differentiation trajectories of cells (pseudotime analysis).
However, the data generated is large and technically noisy, making several methods for analysing
bulk RNA-seq data unsuitable. As a result, a plethora of tools have been developed for thevisualisation and analysis of scRNA-seq data, with the lack of a comprehensive comparison. Hence, inthis study, we compare the non-stochastic network-based methods that are gaining popularity in thefield, including Phenograph, correlation analysis, KNN, and SNN. These methods have been applied toreal datasets of variable cell numbers and evaluated for their sensitivity and stability in identifying
cell-subtypes of varying proportions, and in detecting outliers. Conventional correlation analysis
easily identifies outliers, though unable to distinguish cell-subtypes. In contrast, KNN & SNN based
approaches can detect these subtypes, however, without addressing outliers and with the stability ofclusters relying on the parameter ‘k’. In all methods, datasets containing variable proportions of cell
populations resulted in misclassification of subtypes. To address these two limitations, we developed
a modified KNN algorithm. Firstly, outliers are revealed, as a variable set of edges are assigned to anode, depending on the respective correlations. This is in contrast to the KNN based algorithms,
which does not distinguish between poor and strong correlations considered. Secondly, the current
algorithm can identify cell subtypes of variable proportions by constructing the network based on thedensity of cell-types. Additionally, by visualising the relevant structure of the data, differentiation
trajectories can also be described. Together this study identifies the caveats associated with current
network approaches and proposes an algorithm to address these limitations, in particular highlighting
outliers and distinguishing cell-types of varied proportions.

Talk 12
Immune cell diversification in response to a single stimulus: from single cells to subsets
and back
Vassili Soumelis1, Nathalie Lehmann2, Morgane Thomas-Chollier2, Philémon Sirven1, Denis Thieffry2
1

2

Institut Curie, Cancer Immunotherapy Center, Paris, France, Institut de Biologie de l’Ecole Normale Supérieure, IBENS, Paris,
FranceCells change state in response to environmental stimuli, a process called adjustment. When a seemingly
homogeneous cell population receives a stimulus, it collectively and progressively switches to a different
state. In a recent study of innate immune cells, i.e plasmacytoid pre-dendritic cells (pDC), we could showthat primary human pDC diversify over 24h into three distinct subpopulations (P1, P2, P3) in response toa single microbial stimulus, such as influenza virus or CpG oligonucleotide (Alculumbre et al, Nat
Immunol, 2018). This was revealed by single cell analysis using multicolor flow cytometry. P1-, P2-, andP3-pDC harbor distinct morphology, phenotype, transcriptional signatures for coding and non-coding
RNA, as well as function. Subsequently, we turned back to single cell RNAseq analysis of pDC
diversification in order to precisely quantify transcriptomic level diversity of pDC over time following
stimulation, and search for putative molecular mechanisms underlying the stepwise diversification
process, helped by pseudo time analysis. We propose that cell diversification may be a generalmechanisms generating diversity following cellular adjustment to a single stimulus, driven by stochastic
as well as deterministic events characterizing each specific cellular system.

Talk 13A patient centred disease comorbidity network
Alfonso Valencia
Barcelona Supercomputing Center, Barcelona, Spain

Even if concomitant presence of two or more diseases represent a significant problem in preventive
medicine the underlying molecular basis are poorly understood. Based on the analysis of patient specificexpression signatures, we have developed a network based approach for the prediction of the risk ofdeveloping secondary diseases at the individual level (1).
In the first place, we build a network of patients based on the similarity of their expression profiles andestimate relative disease co-ocurrence risks. The network overlaps and complements the previously
general trends detected in EHR by the Barabási and Brunak’s groups. In a second step, we include in the
network inverse relations between gene expression profiles that correspond to known inverse
comorbidity relationships, such as the protection Alzheimer’s disease provides to lung cancer patients.
Inverse comorbidities add a new orthogonal dimension to the previous disease networks. Third, we
deconstruct the network in patient subgroups. Subgroups reflect more accurately the inherent
heterogeneity of human diseases and help to explain the differences in tendencies to acquire secondary
diseases observed in real clinical cases. Finally, we associate drugs to the patient subgroup to dissect
possible molecular causes of comorbidities and to explore possible drug repositioning options.
In the long run, our intention is to explore the use of the patient-to-patient disease networks to preventthe possible incidence of secondary diseases at the personal level in real clinical scenarios.

(1) Unveiling the molecular basis of disease co-occurrence towards personalized comorbidity profiles. by
J Sánchez-Valle, H Tejero, JM Fernández, D Juan, S Capella-Gutiérrez, F AlShahrour, R Tabarés-Seisdedos,
V Pancaldi and A. Valencia. submitted.

Talk 14
Dynamical modelling of T cell co-inhibitory pathways to predict anti-tumour responses
to checkpoint inhibitors
Céline Hernandez1, Aurélien Naldi1, Wassim Abou-Jaoudé1, Guillaume Voisinne2, Romain Roncagalli2,
Bernard Malissen2, Morgane Thomas-Chollier1, Denis Thieffry1
1

Computational Systems Biology team, Institut de Biologie de l’Ecole Normale Supérieure (IBENS), CNRS UMR8197, INSERM
2
U1024, Ecole Normale Supérieure, PSL Université, 75005 Paris, France, Centre d'Immunologie de Marseille-Luminy, Aix
Marseille Université, INSERM U1104, CNRS UMR7280, 13288 Marseille, France

In recent years, T cells were recognized to often display a reduced ability to eliminate cancer cells,
caused by the expression of co-inhibitors at their surface. Antibodies blocking these co-inhibitors(checkpoint inhibitors) have become standard treatment for metastatic melanoma (Simpson et al. 2013),
thus leading to a revival in the study of T cell co-inhibitors. However, our understanding of theirimmunobiology and their harmful role during anti-tumour responses remains fragmentary. Particularly,
a mechanistic understanding at the systems-level of T cell function modulation by co-inhibitors hasremained elusive.
To overcome these limitations, we aim to delineate the mechanisms through which co-inhibitory
molecules, such as PD-1 and CTLA-4, impede T cell functions at the systems-level. To reach this goal, weuse computational methods to map and model TCR co signalling pathways, and ultimately predict cell
responses to perturbations. First, we developed comprehensive annotated molecular maps (using thesoftware CellDesigner, http://www.celldesigner.org) by curated scientific literature, automated queries
to public databases and protein-protein graph reconstruction. Next, using the software GINsim(http://www.ginsim.org), these maps and protein networks were translated into a regulatory graphintegrating current knowledge. The major challenge was then to properly model concurrent intracellular
processes, along with feedback control mechanisms. To cope with this complexity, we explored network
modules using a Rule-based formalism (Feret et al. 2009), in order to evaluate concurrent biologicalhypotheses and specify logical rules that recapitulate observed component behaviour into the logicalmodel. The resulting integrative model will be used to predict cell response to single or multiple
perturbations, thus paving the way to delineate novel experiments, which in turn will be used to refine
the maps and model. This integrated systems-level view of the action mechanisms of key T cell
coinhibitors will provide a further rationale for designing and evaluating drugs targeting T cell coinhibitory pathways in anti-cancer immunotherapy.

Talk 15
Open Community Challenge Reveals Molecular Network Modules with Key Roles in
Diseases
Sven Bergmann
Department of Computational Biology, University of Lausanne & Swiss Institute of Bioinformatics, Switzerland

Identification of modules in molecular networks is at the core of many current analysis methods inbiomedical research. However, how well different approaches identify disease-relevant modules indifferent 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 thechallenge submissions performed best. We find that most of these modules correspond to coredisease-relevant pathways, which often comprise therapeutic targets and correctly prioritize
candidate disease genes. This community challenge establishes benchmarks, tools and guidelines formolecular network analysis to study human disease biology.
The full manuscript describing this work is available at:https://www.biorxiv.org/content/early/2018/02/15/265553

Talk 16
Genetic interactions in childhood cancer
Josephine Daub, Saman Amini, Frank Holstege and Patrick Kemmeren
Princess Máxima Center for Pediatric Oncology, Utrecht, The Netherlands

Even though the survival rate of childhood cancer has increased in the last decades to around 80%
today, it is still the major cause of death in children in developed countries. Cancer develops throughthe acquisition of multiple mutations, and it is assumed that genetic interactions between mutatedgenes play an important role in cancer onset and progression. One approach to find genetic
interactions in cancer is to search for pairs of mutated genes that occur more (or less) often than
expected given the frequency of the individual mutated genes. Highly co-occurring mutated genes
suggest a cooperative role of these altered genes in cancer development. Mutually exclusive genepairs can be a signal of synthetic lethality and could therefore point to possible cancer treatments.
We developed a pipeline, based on two genetic interaction tests [1,2], to detect significant cases ofco-occurrence and mutual exclusivity in two pediatric cancer data sets [3,4], comprising over 2,500
tumors from 24 cancer types. In total we detect twelve co-occurring and 42 mutually exclusive
genetic interactions. We not only confirm previously detected genetic interactions between
significantly mutated genes (SMGs), but also find many interactions that involve non-driver genes.
This suggests that the inclusion of the whole set of genes instead of the set of SMGs, can lead tonovel discoveries.
[1] Park & Lehner. Mol Syst Biol 11, 2015.
[2] Kim, Madan & Przytycka. Bioinformatics 33; 814–821, 2017.
[3] Gröbner et al. Nature 555;321-327, 2018.
[4] Ma et al. Nature 555;317-376, 2018.