COMBINE 2019 Talks
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Registration NumberTalk NumberAuthorAffiliationTitleAbstract
21Pedro Fontanarrosa (notified)University of Utah (USA)Analyzing Genetic Circuits for Hazards and GlitchesA hazard is the possibility of an unwanted or unexpected variation of the output of a combinational logic network before it reaches steady-state. A glitch is the actual observance of such a problem. These terms are used mostly for electronic circuits, though glitches have been observed for genetic regulatory networks (GRNs) as well. A glitch is a transient behavior that corrects itself as the system reaches a steady-state. Nonetheless, this glitching behavior can have drastic consequences if this transient output of the GRN causes an irreversible change in the cell such as a cascade of responses within or with other cells, or if it induces apoptosis. Therefore, avoiding glitching behavior can be crucial for safe operation of a genetic circuit.To better understand glitching behavior in genetic circuits, this work utilizes a version of our dynamic model generator that automatically generates a mathematical model composed of a set of ordinary differential equations (ODEs) that are parameterized using characterization data found in a Cello genetic gate library. Simulation of a dynamic model allows for the prediction of glitches that cannot be observed with steady-state analysis. This work is done using data-standards such as SBOL, SBML and SED-ML
42Mante, Jeanet (notified)University of Utah (USA)Visualization of Part Use in SynBioHubWe present a visualization plugin to assist designers in finding components for their designs from SynbioHub instances. In particular, our plugin displays up to four graphs per part page. First, there is a Sankey diagram that shows other components that are commonly used with this component sort by their type. Second, there is another Sankey diagram that indicates which of these parts commonly come before or after the component of interest. Third, there is a histogram that shows how commonly used this component is relative to most commonly used components. Finally, there is a histogram that shows the commonality of this component relative to other commonly used components of the same type.
53Roth, Yosef (notified)Icahn School of Medicine (USA)Datanator: Tools for Aggregating Data for Large-Scale BiomodelingSystems biology aims to understand how genotype influences phenotype. Comprehensive mechanistic models, such as whole-cell models, can be used to explore cell dynamics. However, the data required to construct these models are contained across many different databases -- often with inconsistent identifiers and formats. In addition, the time required to manually collect this data impedes the creation of large-scale models. To accelerate whole-cell modeling, we developed Datanator, an integrated database and search engine. Datanator allows a modeler to input a desired biological parameter (e.g. molecular concentration, reaction rate, etc.) with desired search criteria (e.g. organism, environmental conditions, etc.), and Datanator returns a list of the most relevant observations together with a single consensus value that can be directly used in a model. To accomplish this, Datanator integrates genomic data (RefSeq, Ensembl), transcriptomic data (ArrayExpress), proteomic data (Pax-DB), kinetic data (SABIO-RK), and metabolite concentration data (ECMDB, YMDB). Datanator’s search engine can identify the most relevant data from a combination of search criteria: genetic similarity (KEGG orthology), taxonomic similarity (NCBI taxonomy), molecular similarity (tanitomo string comparison), reaction similarity (EC number), and environmental similarity. Datanator enable scaleable model creation and consistent provenance tracking. In addition to using Datanator to build a WC model of Mycoplasma pneumoniae, we have shown that Datanator can find missing parameters for ODE models, augment FBA models with kinetic bounds, and recalibrate models to similar organisms. Availability:
84Yanez, Guillermo (notified)Pontificia Universidad Catolica de Chile (Chile)Flapjack: an open-source tool for storing, visualising,analysing and modelling kinetic gene expression dataEngineering design cycles based on accurate parameter estimation from experimental data are key for predictable assembly of complex genetic circuits. In particular, as dynamical systems the reliable design of genetic circuits requires analysis of kinetic gene expression data. This data is often distributed across many institutions, in different file formats, repositories and databases, which makes it difficult to collate and reduces the power of analysis. Thus there is a need for data repositories that can store kinetic gene expression data, link this data to circuit designs, and allow analysis that combines multiple studies and experiments to reliably estimate parameters. Here we present Flapjack, a web-based open-source tool for storing, visualising, analysing and modelling kinetic gene expression data. Flapjack enables users to flexibly query experimental data based on metadata, for example extracting all measurements related to a particular DNA sequence, all growth curves for a given strain, etc. These queries return all relevant data irrespective of the particular study or experiment, or may be restricted to specific experiments of interest. Using the web app users may then visualize the experimental time series using interactive plots (time courses, kymographs, heatmaps), apply analyses such as calculation of expression rates, growth rates, and parameterise transfer functions or induction curves. For custom analysis queried data can be downloaded in CSV, JSON or XML file formats. Currently, Flapjack supports upload of Synergy HTX and BMG Labtech microplate reader data, but can easily be extended to accommodate any data format. We propose a data repository and set of analysis tools that enables scaleable data analysis, visualization and parameter estimation, collating data between institutions, studies, experiments, and individual researchers. Flapjack will thus significantly enhance data sharing, management and analysis for synthetic and systems biology.
115Sheriff, Rahuman (notified)EMBL-EBI (UK)BioModels Parameters: A resource to search and access parameters from published systems modelsSystems biology models of cell signalling, metabolic and gene regulatory networks have been shown to divulge mechanistic insight into cellular regulation. One of the major bottlenecks in building systems models is identification of model parameters. Searching for model parameters from published literature and models is essential, yet laborious task. To address this, we have developed a new resource, BioModels Parameters, that can facilitate easy search and retrieval of parameters values from models stored in BioModels (Chelliah et al. 2015
126Fröhlich, Fabian (notified)Harvard Medical School (USA)Simulation and Sensitivity Analysis for Large Kinetic Models in AMICIIn recent years, large, kinetic, multi-pathway models with hundreds to thousands of species and parameters have become increasingly abundant. These large kinetics models are often constructed with the aim to deepen our mechanistic understanding of signaling pathways. Yet, calibration of these models poses a major computational challenge, which limits our ability to work with these models in practice. Gradient-based methods such as quasi-Newton optimization or Hamiltonian Monte-Carlo have been demonstrated to work well for high-dimensional problems, but require methods to robustly compute parameter sensitivities. To address this issue, we developed AMICI (Advanced Multilanguage Interface to CVODES and IDAS), a C++ library tailored to the simulation and sensitivity analysis of large Ordinary Differential Equation and Differential Algebraic Equation models. AMICI provides python and MATLAB interfaces that allow compilation of simulation executables from SBML and BNGL formats. For gradient computation, AMICI implements symbolic processing of model equations to provide efficient local sensitivity analysis using forward, adjoint and steady-state sensitivity analysis. So far AMICI has been applied for simulation and model calibration in 27 publications. To highlight AMICI features that are essential for model simulation and sensitivity analysis for large models, I will showcase several examples, which includes multi-pathway signaling models with hundreds to thousands of species, reactions and parameters.
147Rampadarath, Anand (notified)Auckland Bioengineering Institute, University of Auckland (NZ)Model curation and annotationIn an attempt to curtail the ongoing problem of irreproducibility of published work, the Center for Reproducible Biomedical Modeling has introduced an annotation and curation service to help journal authors, reviewers, and editors publish reproducible, reusable models. Manuscripts received from partner journals will be curated to make sure that any author supplied code will faithfully reproduce the results presented in the manuscript. In this talk I will give a quick description of this curation and annotation process as well as give an update on the service
178Garny, Alan (notified)University of Auckland (NZ)OpenCOR: current status and future plansOpenCOR: current status and future plans","OpenCOR is an open source cross-platform modelling environment. It can be used to organise, edit, simulate and analyse models encoded in the CellML format. Partial support for SED-ML and COMBINE archives is also available. In this talk, we will give an overview of OpenCOR and illustrate how it fits within the overall COMBINE effort. We will then share some of our future plans for OpenCOR
189Lundengård, Karin (notified)University of Auckland (NZ)Physiome - Publish your models curated for reproducibility and reusability to increase research quality for everyonePhysiome is a journal committed to reproducibility and reusability of mathematical models of physiological processes. Every article published in Physiome is connected to a curated and permanent version of the model code with a persistent identifier. Through the Physiome paper, the code necessary to run the model is easily accessible by just clicking a link, to be reused as it is or as a module in a bigger model. It is also connected to a primary paper published in a domain specific journal, where the validation and scientific value of the model is discussed. A Physiome publication is a complement to your primary article that ensures reproducibility, reusability and discoverability of you model. The format encourages modularity that facilitates combination of different models to develop the next level of systems understanding. And all the models are in one place, easy to find and accessible. Reproducibility and confirmation of results is crucial for useful science and should be one of the supporting pillars of good research. Yet, publication of it is rarely incentivised, often treated as a secondary result at best, which undermines the quality of our work. With the strict formulation of equations and easily shared code, it seems like mathematical models should be reproducible by default, but in fact less than 10% of the models published in scientific journals work when implemented by another group. Waste no more valuable time and effort on trying to implement models from papers that lack information, or having your results lost because others cannot use them. Publish your models in Physiome and contribute to making science useful in society (and less frustrating for your colleagues). For more information visit:
2910Oberortner, Ernst (notified)Berkeley Labs (USA)SBOL and its applicability in partially and fully automated design workflows: Three success storiesThe Synthetic Biology Open Language (SBOL) and its recently added W3C Provenance Ontology (PROV-O) extension enable to exchange data in a standardized format and to track the activities, entities and data across the entire, iterative life cycles of synthetic biology Design-Build-Test-Learn (DBTL) workflows. An integrated design workflow is required when managing various large-scale, complex projects simultaneously, as we experience at the U.S. Department of Energy (DOE) Joint Genome Institute (JGI). The dilemma is, however, to address the varying requirements across the simultaneous projects, to define the activities of each project’s DBTL workflow, and to incorporate frequently changing requirements due to improved technologies and scientific discoveries. In this presentation, I will survey the design of three projects with varying requirements and workflows. The projects focus on the design of a few small proteins to pathways with multiple operons, such as in refactored gene clusters. The design workflow was either performed using a partially or fully automated approach including the adoption of SBOL for exchanging designs and tracking design activities. The design tasks of the projects range from (i) codon optimizing the genes for expression in the target host organism over (ii) grouping genes into operons to modulate their expression using regulatory elements (e.g., promoters, terminators) to (iii) specifying the synthesis limitations as well as the assembly and cloning instructions of the design. When designing a pathway, then various design alternatives need to be considered. We experienced designs ranging from explicitly specified pathway variants to combinatorial designs that need to be sampled either randomly, algorithmically or based on constraints. Lastly, I will also touch on our approach of using SBOL for the specification of build instructions (i.e., synthesis, assembly, cloning) for the three projects. Although in its infancy, this approach could enable to order the physical construction of a design and desired build instructions to downstream manufacturing facilities, such biofoundries or commercial DNA synthesis providers.
3211Snoep, Jacky (notified)Stellenbosch University (ZA)Reproducibility in model construction, validation and analysis workflows in systems biology projects; Xylose metabolism in Caulobacter crescentus as a case study, using JWS Online and the FAIRDOMHubWith the uptake of modelling standards as advocated by the COMBINE community, the description and publication of models in standard formats such as SBML has much improved. Many journals require authors to make their mathematical models available, and the SBML format is often used in Systems Biology studies. However, the requirement to submit a model description does not guarantee that the model simulations shown in the manuscript are reproducible. Usually, reviewers will not check the submitted models, and without clear instructions for simulations, it can be quite challenging to reproduce particular model simulations. Having been active for a long time in technical model curation of scientific manuscripts that contain mathematical models, we need on average about 5 communications with authors to successfuly reproduce their simulation results, and model simulations published in journals without a curation service will very often not be reproducible. Using a standard model simulation description such as SED-ML makes it much easier to check for reproducibility, but not many manuscripts include SED-ML descriptions of model simulations. The JWS Online project includes both a model database and a model simulation database, the tools to construct SBML and SED-ML files, and test for model simulation reproducibility. In addition, it allows for linking experimental data stored on the FAIRDOMHub. Whereas one could consider the reproducibility of model simulations as a minimal requirement, our aims for the scientific process of mathematical modelling should be much higher, including transparancy and reproducibility for the construction, validation and analysis of the model. We will illustrate the approach for a combined experimental and modelling study on xylose metabolism, using initial rate kinetics on isolated enzymes, progress curve analysis, sequential enzyme cascade, one pot cascade and cell extract incubations. All data, model and simulation description files for each of the steps are made available on the FAIRDOMHub, making the complete process reproducible and FAIR.
3312Varma, Susheel (notified)EMBL-EBI (UK)ELIXIR Cloud & AAI: Standardised and Interoperable Services for Human Data CommunitiesCurrently, patient data are geographically dispersed, difficult to access, and often, patient data are stored in siloed project-specific databases preventing large-scale data aggregation, standardisation, integration/harmonisation and advanced disease modelling. The ELIXIR Cloud and Authentication & Authorisation Infrastructure (AAI) for Human Data Communities project aim to leverage a coordinated network of ELIXIR Nodes to deliver a Global Alliance for Genomic Health (GA4GH) standards-compliant federated environment to enable population scale genomic and phenotypic data analysis across international boundaries and a potential infrastructure to enable 1M Genome analysis. The ELIXIR Cloud & AAI project will lay the groundwork to deliver the foundational capability of “federation” of identities, sensitive data access, trusted hybrid cloud providers and sensitive data analysis services across ELIXIR Nodes by underpinning the bi-directional conversation between partners with the GA4GH standards and specifications and ELIXIR trans-national expertise. The project is also developing a framework for secure access and analysis of sensitive human data based on national federations and standardised discovery protocols. The secure authentication and authorisation process alongside guidelines and compliance processes is essential to enable the community to use these data without compromising privacy and informed consent. The project therefore provides a globally available curated repository to store bioinformatics software containers and workflows (Biocontainers - GA4GH TRS), a service to discover and resolve the locations of datasets (RDSDS - GA4GH DRS) and distributed workflow and task execution service (WES-ELIXIR/TESK - GA4GH WES/TES) to leverage the federated life-science infrastructure of ELIXIR. The ambition of the project is to provide a global ecosystem of joint sensitive data access and analysis services where federated resources for life science data are used by national and international projects across all life science disciplines, with widespread support for standard components securing their long-term sustainability. Connecting distributed datasets via common standards will allow researchers unprecedented opportunities to detect rare signals in complex datasets and lay the ground for the widespread application of advanced data analysis methods in the life sciences.
3513Ortega, Oscar O (notified)Vanderbilt University (USA)PySB framework: Tools to build, calibrate and visualize biochemical modelsComputational models are used to understand the dynamics and behavior of complex biological network processes. In order to study cellular network processes, mechanistic models need to be built, calibrated to experimental data, and analyzed to generate hypotheses about the mechanisms that control different cellular processes. Here we present the PySB modeling framework of tools to build, calibrate and visualize models. PySB is a Python-based programming framework for systems biology, and it builds on BioNetGen and Kappa rule-based languages as well as Python libraries to enable model definition as functions that represent biological processes. PySB comprises three different tools for model calibration to experimental data: SimplePso uses the Particle Swarm Optimization algorithm, PyDREAM uses the Differential Evolution Adaptive Metropolis (DREAM) algorithm to obtain posterior distributions of calibrated parameters, and Gleipnir that uses nested sampling algorithms for Bayesian parameter inference. Additionally, PySB contains a tool to obtain static and dynamic representations of model networks and their simulated dynamics. Importantly, the entire PySB framework can be employed within Jupyter Notebooks thus facilitating reproducibility and shareability of complete analysis pipelines. Therefore, we believe that the PySB framework enables users to obtain important mechanistic insights about complex biological processes that could be potentially used for the development of novel therapies to treat different human diseases.
4214Starruß, Jörn (notified)TU Dresden (D)Principles for declarative multicellular modellingNew insights into multicellular processes in tissues and organs, like tissue regeneration, can be gained using spatially resolved modelling and simulation. Correspondingly, many international Systems Medicine projects are developing spatially resolved multicellular models and new simulation software. Exchange, reproducibility and archiving of spatially resolved multicellular models among different projects with different software tools would mean a great leap forward for the community. However, that would require an appropriate and fully declarative model definition language for this class of models. We present our considerations for the design of such a modelling language based on our declarative modelling experience with Morpheus(ML) [1] and highlight central concepts to represent the multicellular complexity. [1] Morpheus(ML) -
5215Hermjakob, Henning (notified)EMBL-EBI (UK) Compact Identifiers for robust data citationCompact identifiers have been informally and widely used for referencing life science data for many years, though the practice has been largely been through ad hoc implementations, serving specific use cases. We describe our implementation, which has already begun to be adopted by publishers. Compact Identifiers consist of an ( assigned unique prefix in combination with a locally (database) assigned accession number (prefix:accession). Compact Identifiers are resolved to database records using information that is stored in an underlying Registry, which contains high quality, manually curated information on over 700 data collections. This information includes the assigned unique prefix, a description of the data collection, identifier pattern, and a list of hosting resources or resolving locations. When a Compact Identifier is presented to the Resolver, it is redirected to a resource provider, taking into consideration information such as the uptime and reliability of all available hosting resources. For example, pdb:2gc4, GO:0006915, doi:10.1101/101279, orcid:0000-0002-5355-2576 etc. In addition, a formal agreement with N2T resolver, based in California Digital Library has been struck to provide backup resolution services. Users can therefore resolve Compact Identifiers using ( or N2T ( resolvers. This implementation of Compact Identifiers has been adopted by Nature Scientific Data for data citations when linking to biomedical datasets with accession numbers [2]. [1] Sarala M. Wimalaratne et al. Uniform resolution of compact identifiers for biomedical data. Sci. Data 5:180029 doi:10.1038/sdata.2018.29 (2018) [2] Open Editorial. On the road to robust data citation. Sci. Data 5:180095 doi:10.1038/sdata.2018.95 (2018)
5816Augustin Luna (notified)Dana-Farber Cancer Institute/Harvard Medical School (USA)Visualization, Access, and Exploration of Biological Pathway Information from Pathway CommonsPathway Commons ( serves researchers by integrating data from public pathway and interaction databases and disseminating this data in a uniform fashion. The knowledge base is comprised of metabolic pathways, genetic interactions, gene regulatory networks and physical interactions 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, resources, and tutorials that 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 and searching the integrated datasets that are provided as file downloads in the Biological Pathway Exchange (BioPAX), Simple Interaction Format (SIF) and gene set (GMT) formats. This data is also provided via web services that allow for integration with external tools (e.g., CyPath2, a Cytoscape app, and the paxtoolsr R package; each is a network analysis tool). Discussion of Pathway Commons will, in part, focus on reusable web application visualization components that are built upon cytoscape.js and the Systems Biology Graphical Notation Markup Language (SBGN-ML). The components are the basis for our web-based 'Search' application that enables users to query pathways by keyword and visualize returned pathways using SBGN with an automated layout. Additionally, these components are used for the ongoing development of various pathway visualization applications, such as the online Newt SBGN pathway editor ( Overall, these software components enhance the accessibility to pathways from third-party applications wishing to integrate support for pathway visualization and interpretation.
6217Martin Golebiewski (notified)HITS gGmbH (D)Two universes – one world: Community standards vs. formal standards in systems biology and systems medicineGiven the increasing flood and complexity of data in life sciences, standardization of these data and their documentation are crucial. This comprises the description of methods, biological material and workflows for data processing, analysis, exchange and integration (e.g. into computational models), as well as the setup, handling and simulation of models. Hence, standards for formatting and describing data, workflows and computer models have become important, especially for data integration across the biological scales for multiscale approaches. To this end many grassroots standards for data, models and their metadata have been defined by the scientific communities and are driven by standardization initiatives such as COMBINE and others. For providing the potential users with an overview and comparable information about such standards we develop information resources, such as the NormSys registry for modelling standards ( For facilitating the integration of data and models, standards have to be harmonized to be interoperable and allow interfacing between the datasets. To support this, we drive and lead the definition of novel standards of the International Organization for Standardization (ISO) in the technical ISO committee for biotechnology standards (ISO/TC 276) in order to define a framework and guideline for community standards and their application. With our activities we aim at enhancing the interoperability of community standards for life science data and models and therefore facilitating complex and multiscale data integration and model building with heterogenous data gathered across the domains.
6418Emek Demir (notified)OHSU (USA)Causal, Mechanistic Pathway Based Analysis of -Omic ProfilesGenomics and imaging data produced by large NIH projects now reaches to Exabyte scale. Current mechanisms of knowledge representation and scientific communication in biology cannot adequately deal with the complexity and volume of this information—a serious bottleneck for developing a causal, predictive understanding of the cell. To address this need we have developed multiple algorithms and tools over the years that bridges causal mechanistic knowledge obtained through decades of low-throughput biology research with big data. Over the last two years these tools are being actively used for molecular tumor boards and cancer precision medicine within the OHSU's SMMART program. In this talk, we will describe the current analysis workflows, how they are facilitated by BioPAX and SBGN, initial tumor board results as well as our future plans.
5319Gkontra, PolyxeniLa Fe Health Research Institute (IIS La Fe, USA)Predictive in-silico multiscale analytics to support cancer personalized diagnosis and prognosis, empowered by imaging biomarkersPRIMAGE (PRedictive In-silico Multiscale Analytics to support cancer personalized diaGnosis and prognosis, Empowered by imaging biomarkers) is a novel, highly innovative H2020 project (GA- 826494) aiming at providing cloud-based computational solutions to the diagnosis, prognosis, choice and follow-up of treatment for two of the most common paediatric cancers with high societal impact, neuroblastoma (NB) and Diffuse Intrinsic Pontine Glioma (DIPG). NB is the most frequent solid cancer in childhood, while DIPG is the leading cause of brain tumour-related death in children. Due to the highly complex nature of both tumours, their diagnosis, prognosis and monitoring require the combination of several sources of data such as biological, clinical, multi-omics and imaging. Apart from the data itself, adequate data infrastructures ensuring secure and anonymized data management, storage and sharing, as well as computational approaches for the effective data exploitation are of paramount importance. Particularly in the case of imaging data, automated high-throughput quantitative image analysis approaches (radiomics) and artificial intelligence are essential in order to translate the huge amount of highly complex images acquired by today´s imaging systems into quantitative information. This information can be used to identify novel imaging biomarkers for disease diagnosis, monitoring of progression, choice and response to therapy. In this context, PRIMAGE will develop a functional prototype cloud-based platform offering predictive tools to assist management of NB and DIPG. Multi-modal real-word imaging data (MRI, CT, MIBG/SPECT, PET-FDG if MIBG is negative) from hospitals around Europe will be used to identify and validate reliable and reproducible imaging biomarkers for NB and DIPG using cutting-edge technologies. The data infrastructures, imaging biomarkers identified, as well as the computational solutions developed within PRIMAGE, including models for in-silico medicine research, will be validated in the context of NB and DIPG, but their application is not limited in these types of cancers but can be extended in a large variety of cancers.