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SUMMARY:Data Driven Systems Biology: Harnessing Big Data and Systems Approaches to Decode Complex Biology
DESCRIPTION:In the era of high-throughput technologies and rapidly expanding biomedical datasets\, the field of systems biology is undergoing a transformative shift. The Data-Driven Systems Biology conference brings together leading researchers who are leveraging computational\, statistical\, and systems-level approaches to integrate and interpret complex biological data. This conference will explore how multi-omics\, single-cell technologies\, and spatial profiling\, combined with advanced computational modeling and machine learning\, are reshaping our understanding of dynamic biological systems. \n\n\n\n\nThe DDLS research area symposia series aims to engage and build a strong national scientific community around the DDLS research themes. Each of the four areas arranges two symposia per year. Everyone interested in data-driven research is welcome to take part. We aim to unite researchers\, industry\, and healthcare to foster collaboration and advance the frontiers of data-driven life science. \n\n\n\nTarget Group: The DDLS research area Expert Group in Cell and Molecular Biology invites all interested in Data-driven life science to meet\, present\, interact\, and discuss Imaging in Cell and Molecular Biology. \n\n\n\nThe event will take place at Life City\, Solna\, Stockholm\, and will include presentations from international and national invited speakers and selected abstracts. The event is free of charge. \n\n\n\nDate: March 24-25\, 2026 \n\n\n\nStart on March 24: 11:00 – 12:30 Registration open. 11:30-12:30 Network lunch. The conference starts in the Lecture hall at 12:30. \n\n\n\nEnd: March 25 with a Network lunch from 12:30 to 13:30. \n\n\n\nVenue: Life City\, Solnavägen 3H i Solna. \n\n\n\nOrganized by: Arne Elofsson and Eduardo Villablanca\, DDLS Expert Group in Cell and Molecular Biology. \n\n\n\nContact: events@SciLifeLab.se \n\n\n\nProgram\n\n\n\nAgenda_DDLS_CMB_March2026_v7Download\n\n\n\nPoster session\n\n\n\nThe poster session will take place at 17:30 in Delta\, Campus Solna. Light food and drinks will be served. Please hang your poster on any empty poster board as soon as you arrive to Campus Solna after the Conference’s first day. Pins will be available. \n\n\n\nAbstract book\n\n\n\nRegistration\n\n\n\nThe registration deadline is March 10. We cannot accept any posters after deadline. To avoid empty seats\, registration will remain open with a que-list until the event begins. However\, registering after March 10 requires you to write your name on a name tag at on-site registration. Unfortunately\, we cannot accommodate allergies or dietary preferences for those who register after March 10. \n\n\n\nRegister here\n\n\n\nCancellation\n\n\n\nPlease! To minimize empty seats and food waste\, cancel your registration if you are unable to attend\, or update your lunch selection if your attendance changes. Cancel/update via the Confirmation email or email events@scilifelab.se. \n\n\n\n\n\n\n\nConfirmed speakers\n\n\n\nAlfonso Valencia\, ICREA Professor\, Barcelona Supercomputing Center\, Spain\nTitle: Data\, Digital Twins and AI \n\n\n\nBio: Prof. Alfonso Valencia is ICREA research Professor\, Director of the Life Sciences Department of the Barcelona Supercomputing Center\, Director of the Spanish National Bioinformatics Institute INB/ELIXIR-ES and coordinator of the data pillar of the Spanish Personalised Medicine intiative\, IMPaCT. His research interest is the development of Computational Biology methods and their application to biomedical problems. Some of the computational methods he developed are considered pioneering work in areas such as biological text mining\, protein coevolution\, disease networks and more recently modelling cellular systems (digital twins). He participates in some of the key cancer related international consortia. In terms of community services\, he is one of the initial promoters of the ELIXIR infrastructure\, founder of the Spanish and International Bioinformatics networks and former president of ISCB\, the international professional association of Bioinformaticians. He is Executive Editor of the main journal in the field (Bioinformatics OUP). \n\n\n\nAbstract: In this talk I will treat in some order these three topics: Data\, human Ditigal Twins and the impact of AI in biomedicine. \n\n\n\nI will address the persistent bottleneck of data access in biomedical research\, where the combination of legal and technical hurdles span the entire data lifecycle\, from discovery and access to integrated analysis. I will mention the current developments to overcome these limitations by implementing federated discovery and analysis systems designed to work across borders and heterogeneous resources. \n\n\n\nRegarding Digital Twins\, I will discuss the importance of those developments in the transition from statistical correlations\, which are standard in genomics analysis\, to mechanistic interpretations that will better align with the core objectives of molecular biology.  We are approaching this underdeveloped area with the construction of mechanistic models of cellular systems\, that are already showing promising results in critical biological systems. \n\n\n\nFinally\, I will discuss how the rapid advances of AI is influencing the work in different areas of biomedicine – including specific examples of how we combine Digital Twins and AI methods- as well as what I see as promises and limitations in this area. \n\n\n\n\nAvlant Nilsson\, KI\nTitle: From Omics to Mechanisms: Deep Learning Models of Molecular Networks for Precision Cancer Medicine \n\n\n\nBio: Avlant Nilsson is an Assistant Professor in Precision Medicine at the Department of Cell and Molecular Biology\, Karolinska Institutet\, and a group leader at SciLifeLab through the DDLS program. He holds an MSc (2014) and a PhD (2019) in Biological Engineering from Chalmers University of Technology\, where his thesis focused on the metabolism of proliferating cells\, including liver cancer. He then pursued postdoctoral research at the Massachusetts Institute of Technology (2019–2023)\, developing neural network models of signal transduction in immune cells. His research group\, currently comprising of two PhD students and two postdoctoral researchers\, develops data-driven models of molecular networks to understand how genetic alterations\, cell type of origin\, and cell–cell interactions shape cancer biology. The long-term goal of the lab is to advance computer-aided design of cancer medicine by predicting drug responses\, resistance mechanisms\, and microenvironmental interactions. \n\n\n\nAbstract: Cancer is highly heterogeneous\, spanning a multitude of genetic alterations\, cell types\, and microenvironmental contexts\, making it difficult to identify effective treatments for individual patients. Deep learning models are powerful predictive tools that could be applied to large-scale molecular data\, but their black-box nature limits their ability to generate mechanistic insight to guide therapeutic intervention. \n\n\n\nTo overcome this\, we develop biologically informed neural network models that embed known molecular interaction networks directly into the deep learning architecture. Specifically\, we construct recurrent neural network models of cells in which biomolecules are represented as nodes with connections defined by their physical interactions. These models take data with molecular causes as input (such as mutations and copy number variations) and are trained to predict omics readouts\, including gene expression\, protein phosphorylation states\, and metabolite levels. \n\n\n\nBy training models on high-throughput datasets spanning different cell types\, perturbations\, and conditions\, we can predict molecular responses in conditions that are withheld during training. We also use these models to expose non-canonical signaling events that would be difficult to identify directly from the data using standard analysis approaches. With this\, our framework offers the potential to identify novel drug targets\, biomarkers\, and to predict resistance mechanisms. \n\n\n\n\nCamilla Engblom\, KI\nTitle: Spatially resolving B cell clonal dynamics in cancer and beyond \n\n\n\nBio: Dr. Camilla Engblom is a SciLifeLab Fellow and an Assistant Professor in the Division of Immunology and Respiratory Medicine and the Department of Medicine\, Solna at the Karolinska Institutet (KI). Dr. Engblom received her PhD in Immunology from Harvard University in 2017 focusing on long-range cancer-host interactions involving myeloid cells (Dr. Mikael Pittet’s lab at Massachusetts General Hospital/Harvard Medical School). As a MSCA postdoctoral fellow in Dr. Jonas Frisén’s lab (KI)\, Dr. Engblom developed a spatial transcriptomics-based tool (Spatial VDJ) to map B cell and T cell receptors within human tissues. Located at SciLifeLab and the Center for Molecular Medicine (KI)\, the Engblom lab’s main research focus is to spatially and functionally resolve B cell clonal dynamics in cancer tissues and beyond. \n\n\n\nAbstract: B cells perform functions critical to human health\, including antibody production and antigen presentation. B cells develop\, differentiate\, and expand in spatially distinct sites across the body. B cells express clonal heritable B cell receptors (BCR) that confer exquisite molecular (i.e.\, antigen) specificity. B cell receptors can be defined by sequencing. Linking specific BCR sequences to their molecular and cellular surroundings\, i.e.\, ‘clonal niche’\, could help us understand and harness B cell activity. A technological bottleneck has been to capture the location of BCR sequences\, and by extension B cell clonal responses\, directly within tissues. We recently developed a spatial transcriptomics-based approach (Spatial VDJ) and associated computational pipelines to reconstruct B cell clonality in human tissues. Here\, we present adaptation of Spatial VDJ to murine tissue to enable preclinical studies and B cell receptor dynamics under inflammatory conditions\, including cancer. \n\n\n\n\nCarsten Hopf\, CeMOS Center for Mass Spectrometry and Optical Spectroscopy\, TH Mannheim\, Germany\nTitle: Enabling technologies for spatial metabolomics: Moving from single cells to 3D-space exploration in mixed reality \n\n\n\nBio: Carsten Hopf obtained his PhD in biochemistry from Tübingen University/Max-Planck-Institute for Developmental Biology. As an EMBO fellow in neuroscience\, he then worked at the Johns Hopkins University School of Medicine for three years\, before joining Cellzome AG\, a proteomics-focused drug discovery platform company in Heidelberg in 2001. There\, for 13 years\, he served in multiple roles in platform technology\, assay development\, drug discovery and business development\, and eventually as part of Cellzome’s leadership team until the end of 2014. \n\n\n\nSince 2005\, Carsten Hopf is a professor of bioanalytics\, proteomics and drug discovery at TH Mannheim. He currently heads TH.M’s CeMOS Research and Transfer Center that was recently selected as mass spectrometry imaging partner site of the EU-OPENSCREEN research infrastructure that SciLifeLab and KI are also parts of. Carsten is the Speaker of the M2Aind partnership for innovation in health industry in Mannheim\, and he serves on various science and innovation cluster boards. He is also an associated professor in the Medical and Biosciences Faculties of Heidelberg University and co-chair for “imaging” of the German Society for mass spectrometry (DGMS). Carsten’s research focuses on Mass Spectrometry and Optical Spectroscopy enabling technologies for health and life science\, especially spatial systems biomedicine. \n\n\n\nAbstract: MALDI-Mass spectrometry imaging (MSI)\, also referred to as spatial metabolomics\, has emerged as a powerful technology for spatially resolved analysis and visualization of lipids and metabolites in systems biology and clinical research. Advancement of MSI requires rapid progress in multiple areas such as instrumentation\, experimental workflows and computational strategies to harness big data. The talk will therefore initially review a “classic” technology show case using tissue slices: Spatial metabolomics revealed that Tet3 knockout enterocytes exhibit an unphysiological metabolic profile when compared with their wild-type counterparts suggesting that terminal cell differentiation is regulated by TET3 at the metabolic level. MSI technology has recently moved into two new directions: Single-cell metabolomics and 3D-reconstructed metabolomics. \n\n\n\nTo study proinflammatory activation of iPSC-derived microglia by bacterial lipopolysaccharide (LPS)\, we developed the PRISM-MS (PRescan Imaging for Small Molecule – Mass Spectrometry) platform for analysis and on-cell MS2 identification of low mass metabolites (<200 Da) in large cell populations. Itaconate and taurine were identified as markers for “activated” versus “resting” microglia\, respectively. Translation of single cell results to endogenous microglia in organotypic rat hippocampal slice cultures indicated that LPS-activation involves changes of the itaconate-to-taurine ratio and alterations in neuron-to-glia glutamine-glutamate shuttling. \n\n\n\nTo investigate fibroblast-colon cancer cell interactions in a simple 3D-culture model and in patient-derived organoids (PDOs)\, we built a translational 3D MSI platform as an end-to-end solution for 3D-enabling sample preparation\, 3D-reconstruction and data processing\, 3D-rendering\, and immersive user interaction with organoid big data in a mixed reality. When applied to colon cancer PDOs\, the methodology revealed that fluid-filled cysts characteristic of these PDOs were rich in purine nucleotides. \n\n\n\n\nErik Sonnhammer\, Stockholm University\nTitle: Harnessing Big Data for Network Biology with FunCoup 6 \n\n\n\nBio: Erik Sonnhammer is Professor of Bioinformatics at Stockholm University\, and previously had the same position at Karolinska Institutet\, Stockholm. He did a Ph.D. in bioinformatics at the Sanger Institute in Cambridge\, England. His research interests are in network and systems biology to understand gene and protein function on a large scale. The group has made many contributions to Gene Regulatory Network analysis\, including inference\, benchmarking\, and simulation. \n\n\n\nAbstract: FunCoup 6 is a major update to the FunCoup network database\, providing researchers with a significantly improved and redesigned platform for exploring the functional coupling interactome.  The FunCoup network database (https://FunCoup.org) contains some of the most comprehensive functional association networks of genes/proteins available. Functional associations are inferred by integrating different types of evidence combined with orthology transfer. FunCoup’s high coverage comes from using ten different types of evidence\, and extensive transfer of information between species.  \n\n\n\nKey innovations in release 6:– Enhanced regulatory link coverage: FunCoup 6 now includes over half a million directed gene regulatory links in the human network alone. 13 species in FunCoup now contain regulatory links..– The website is completely redesigned\, with updated API functionalities\, ​enhancing user accessibility and experience.– Integrated advanced online tools for network analysis: The integration of TOPAS for disease and drug target module identification\, along with network-based pathway enrichment analysis using ANUBIX\, expand the utility of FunCoup 6 for biomedical research.– New training framework: applied to produce comprehensive networks for 23 primary species and 618 additional orthology-transferred species.– FunCoup 6 is also available as a Cytoscape app. \n\n\n\nA unique feature of both the FunCoup website and the Cytoscape app is the possibility to perform ‘comparative interactomics’ such that subnetworks of different species are aligned using orthologs. FunCoup further demonstrates superior performance compared to other functional association networks\, offering researchers enhanced capabilities for studying gene regulation\, protein interactions\, and disease-related pathways.  \n\n\n\n\nJean Hausser\, KI\nTitle: Learning cellular dynamics of tissues from single-cell and spatial omics \n\n\n\nBio: Jean researches mathematical rules in the molecular tricks that cancer cells use to escape destruction by immune cells. We seek to articulate the molecular chat between immune and cancer cells into equations\, to serve as the foundation to engineer personalized cancer immunotherapy. We combine single-cell and spatial tumor profiling experiments\, machine-learning & data science\, and physics-style mathematical modeling. \n\n\n\nAbstract: Cell proliferation and death rates are central to tissue biology but measuring them in vivo remains a persistent challenge. Here we present tissue dynamics inference (TIDYI)\, which quantifies absolute cell proliferation and death rates from non-longitudinal single-cell RNAseq snapshots. TIDYI expands the capability of single-cell RNAseq to extracting the cell dynamics of healthy and pathological tissues in vivo. \n\n\n\n\nKaterina Despoina Nastou\, Statens Serum Institut\, Denmark\nTitle: Extracting protein-protein interactions from the literature with deep learning-based text mining \n\n\n\nBio: Katerina Nastou holds a Ph.D. in Bioinformatics and is a researcher at Statens Serum Institut in Copenhagen\, specializing in multi-omics data analysis\, biomedical text mining\, and systems biology. Her work focuses on applying deep learning to extract and model molecular relationships from large-scale biological data and the scientific literature. She has contributed to the STRING database\, a leading resource on protein networks\, by upgrading its text-mining channel with advanced deep learning-based language models. She also currently collaborates internationally on projects such as AIM-HEART and EPOCH. \n\n\n\nAbstract: Biomedical knowledge about molecular mechanisms is still mostly buried in the vastness of the biomedical literature. In this talk\, I will introduce a deep learning-based text-mining pipeline that reads the biomedical literature to extract protein-protein interactions and typed regulatory relations\, then plugs them into the STRING database. Powered by transformer-based language models\, the approach goes beyond simple co-occurrence to recover interactions that are mechanistically specific and often missed. I will highlight why high-quality labelled data are the real bottleneck\, how we tackle it with human-in-the-loop annotation\, and what we learned from models trained on the ComplexTome and RegulaTome corpora. Finally\, we will explore the possibilities unlocked by scaling up evidence-linked relation extraction. \n\n\n\n\nLucy Colwell\, University of Cambridge\, UK\nTitle: Data driven models that predict protein function from sequence \n\n\n\nBio: Lucy Colwell is a researcher on the Science team at Google DeepMind and a faculty member in chemistry at the University of Cambridge. Her primary interests are in the application of machine learning approaches to better understand the relationship between the sequence and function of proteins. Before moving to Cambridge Lucy received her PhD from Harvard University and held an EPSRC fellowship at the Institute for Advanced Study in Princeton\, NJ and the MRC-LMB in Cambridge. In 2018 Lucy was appointed a Simons Investigator in the Mathematical Modeling of Living Systems. Over the last few years Lucy’s team has worked closely with experts at EMBL-EBI to add millions of AI-generated protein function annotations to public databases. \n\n\n\nAbstract: Predicting protein function from amino acid sequence remains a fundamental challenge\, essential for discovering novel biological mechanisms and interpreting the functional effects of genomic mutations. By training on large curated sequence repositories\, we have developed machine learning models that map raw sequences directly to functional annotations. To provide a full-spectrum portrait of protein function\, our specialized large language models are trained to predict a suite of global functional fields (such as protein names\, GO terms\, and functional descriptions) directly from sequence.Moreover\, we present a novel approach that adapts Vision Transformer (ViT) architectures to the task of sequence segmentation\, enabling the end-to-end prediction of discrete functional domains—allowing a single model to make predictions across complex\, nested\, or discontinuous architectures. Crucially\, these systems successfully bridge large homology gaps; their predictions have been prospectively and independently experimentally validated\, demonstrating high levels of accuracy even for novel sequences that are highly distant from the training set. Finally\, we worked closely with collaborators at EMBL-EBI and across InterPro member databases\, collectively adding millions of predicted annotations to public databases and significantly expanding our functional map of the dark proteome. \n\n\n\n\nMarcel Tarbier\, Uppsala University\nTitle: That’s Gonna Leave a Mark: Computational inference of complex cell features \n\n\n\nBio: Marcel studied Biology and Bioinformatics in Germany before starting his PhD in Computational Biology at Stockholm University. In the lab of Marc Friedländer he characterized subtle gene expression variations in virtually identical cells – linking them to regulatory layers and showing their predictive potential. He moved to the lab of Vicent Pelechano at Karolinska Institute for his postdoc to investigate single-cell RNA degradation dynamics and cell lineage relationships – resulting in pioneering work which showed that cellular ancestries can be predicted using only gene expression. In 2025\, he started his lab as a DDLS fellow in precision medicine and diagnostics at Uppsala University and SciLifeLab\, focusing on computational approaches to infer complex cell features\, such as lineage and micro-environment\, to characterize cancer heterogeneity and phenotype switches. \n\n\n\nAbstract: In molecular biology and medicine the molecular composition of samples is the most utilized readout\, and transcriptomic measurements are at the heart of a myriad of break-throughs from developmental biology to pathophysiology. In complex systems\, single-cell readouts have revolutionized our understanding of molecular mechanisms. But single-cell gene expression measurements are “confounded” by complex cell features such as cell lineage relationship\, cellular micro-environment and cell cycle phase. None of these features can easily be measured alongside comprehensive single-cell readouts\, greatly limiting our ability to draw conclusions from single cell data and to put them into biological context. \n\n\n\nWe therefore develop computational tools to infer ancestry\, environment and cell cycle phase from gene expression data. These tools compute approximations of these features based on the marks they leave on the gene expression profiles. Here we present our latest advances in inferring cell lineage relationships in in situ sequencing data\, as well as the cellular microenvironment and cell-cycle phases in single-cell RNA-sequencing data using neural networks. \n\n\n\n\nMika Gustafsson\, Linköping University\nTitle: “Integrating protein interaction maps and omics for explainable health indicators” \n\n\n\nBio: Mika Gustafsson is a Professor in Translational Bioinformatics (PhD in Theoretical Physics\, 2010) at the Department of Physics\, Chemistry and Biology\, Technical Faculty\, Linköping University. Over the past ten years\, he has led a research group of five to seven members. His core expertise lies in creating and integrating network analyses with omics and has been developing machine learning methods for precision medicine. In many projects\, he has led medical doctors and molecular biologists in testing and validating omics-based findings\, working primarily on complex diseases such as multiple sclerosis. \n\n\n\nAbstract: High-dimensional omics data such as genome-wide DNA methylation capture cumulative effects of development\, environment\, lifestyle\, and disease. Yet\, most predictive models trained on these data remain difficult to interpret biologically\, limiting their utility for systems-level reasoning and clinical decision support. In this work\, we present a unifying framework that integrates protein–protein interaction (PPI) networks into deep representation learning\, yielding biologically structured\, explainable embeddings that support both multi-omic modeling and systems level health assessment. \n\n\n\nWe first show that deep autoencoders trained on large DNA methylation and transcriptomic compendia naturally organize their latent spaces into functionally coherent modules. By introducing a soft PPI prior during training\, we encourage each latent unit to correspond to localized regions of the human interactome\, without hard-wiring biological constraints. This network-guided learning produces compact\, non-redundant latent representations aligned with core biological processes such as immune signaling\, metabolism\, cell-cycle control\, and mitochondrial function. Importantly\, these structured embeddings transfer their mechanistic organization to downstream tasks: in cancer cohorts\, cross-omic translation models built on PPI-guided embeddings outperform accuracy-matched baselines while preferentially recovering known driver genes and hallmark pathways. As an intermediate example linking molecular representation to organismal phenotype\, we apply the same network-coherent embeddings to epigenetic aging. Using whole-blood DNA methylation across the human lifespan (n ≈ 18\,000)\, we develop highly accurate and interpretable neural-network age clocks that integrate data-driven embeddings with established CpG markers. These models not only achieve state-of-the-art precision but also recover age-specific epigenetic signatures enriched for example by developmental processes. Finally\, we use these representations for systems level health modeling. By defining bounded respiratory\, cardiovascular\, and metabolic health scores from clinical reference ranges and disease penalties\, and predicting them from blood methylation embeddings\, we obtain accurate and transparent health indicators that reflect both population structure and multi-system coupling. Feature attribution reveals biologically meaningful processes underlying each system\, such as airway repair and hypoxia responses for respiratory health\, endothelial remodeling for cardiovascular status\, and glucose–lipid metabolism for metabolic function. \n\n\n\nTogether\, this work demonstrates that embedding functional network knowledge directly into representation learning provides a scalable route from omics data to explainable\, system-aware health indicators. By keeping biology in the loss\, the approach remains flexible\, extensible\, and suitable for large cohorts and thereby advancing explainable AI for systems biology\, aging research\, and clinical decision support. \n\n\n\n\nSimon Koplev\, KTH\nTitle: Dynamics of immunological tissue architecture linking inflammation with colorectal cancer \n\n\n\nBio: Simon Koplev is a SciLifeLab Fellow and newly appointed group leader in computational biology at KTH Royal Institute of Technology\, Department of Gene Technology. He leads a computational biology research group investigating the fundamental principles and architecture of human tissues across organs in healthy steady-state and disease perturbations. The group is engaged with collaborative large-scale and open science efforts such as the Human Cell Atlas\, developing the next generation of reference datasets and computational methods. Simon holds a PhD in Medical Science from the University of Cambridge at the Cancer Research UK Cambridge Institute supervised by John Marioni and Martin Miller. He did his postdoc with Sarah Teichmann at the Sanger Institute and Cambridge Stem Cell Institute\, working on human single-cell and spatial studies of intestinal fibroblasts. Simon has 12 years of experience in bioinformatics research having published with more than 500 co-authors 35 peer-reviewed papers\, spanning research on cancer\, cardiovascular diseases\, fibroblasts\, gene regulatory networks\, and computational methods development using machine learning. He holds a MScEng in Systems Biology from the Technical University of Denmark\, supervised by Søren Brunak\, including 2 semesters as a Research Scholar at the Dana-Farber Cancer Institute\, Harvard Medical School. Simon began his scientific career with a BS in Biochemistry from the University of Copenhagen. \n\n\n\nAbstract: TBA \n\n\n\n\n\n\n\n\nManage your registration\n\n\n\n\nImproved event experience: Introducing the Lyyti Event app \n\n\n\nThis event uses Lyyti for registration. Lyyti has launched the Lyyti Event app\, where you can find your Lyyti registration\, confirmation\, and ticket. You can also edit your information until the registration deadline.  \n\n\n\nTo get started\, download “Lyyti Event” and sign up with the same email address you normally use for event registrations. The app only displays events associated with the email address used to create your account. \n\n\n\nIf you register for events using multiple email addresses\, your registrations will be split across separate app accounts. For the best experience\, please use one consistent email address for all Lyyti registrations. We hope this new functionality makes it easier for you to manage your participation.
URL:https://www.scilifelab.se/event/data-driven-systems-biology-harnessing-big-data-and-systems-approaches-to-decode-complex-biology/
LOCATION:Life City\, Solnavägen 3H\, Stockholm\, 113 64
CATEGORIES:Event
ATTACH;FMTTYPE=image/png:https://www.scilifelab.se/wp-content/uploads/2025/09/DDLS_CMB_promo_clean.png
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Stockholm:20260218T130000
DTEND;TZID=Europe/Stockholm:20260218T173000
DTSTAMP:20260519T121853
CREATED:20260117T140103Z
LAST-MODIFIED:20260217T102443Z
UID:10001724-1771419600-1771435800@www.scilifelab.se
SUMMARY:DDLS Evolution & Biodiversity - Planetary Biology meeting
DESCRIPTION:Welcome to this joint meeting between DDLS Evolution & Biodiversity and SciLifeLab Planetary Biology to discuss shared visions and goals\, and potential initiatives in collaboration. \n\n\n\nAgenda \n\n\n\n13:00–13:05 | Welcome & objectives \n\n\n\n13:05–13:40 | Presentation round  \n\n\n\n13:40–13:55 | DDLS EB: Vision\, goals & ongoing activities. Sara Hallin  \n\n\n\n13:55–14:10 | Planetary Biology: Vision\, goals & ongoing activities. Olga Vinnere Pettersson \n\n\n\n14:10–14:20 | The Data Science Node in Evolution and Biodiversity. Henrik Lantz \n\n\n\n14:20–14:30 | Joint reflections & clarifying common objectives \n\n\n\n14:30–15:00 | Coffee break  \n\n\n\n15:00–15:30 | Looking ahead: 2026 planning \n\n\n\n15:30–16:35 | Strengthening collaboration & joint initiatives \n\n\n\n16:35–17:00 | Next steps & wrap-up \n\n\n\n18:30 – | Dinner at Frans Bistro (Dragarbrunnsgatan)
URL:https://www.scilifelab.se/event/ddls-evolution-biodiversity-planetary-biology-meeting-2/
LOCATION:BMC Trippelrummet\, Husargatan 3\, entrance C11\, Uppsala\, Sweden
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Stockholm:20251006T120000
DTEND;TZID=Europe/Stockholm:20251007T160000
DTSTAMP:20260519T121853
CREATED:20250827T144903Z
LAST-MODIFIED:20250910T153530Z
UID:10001594-1759752000-1759852800@www.scilifelab.se
SUMMARY:DDLS Evolution and Biodiversity meeting
DESCRIPTION:The DDLS Evolution and Biodiversity research area is organizing a network event in Umeå\, lunch-to-lunch on October 6-7\, 2025. \n\n\n\n \n\n\n\nAim of the meeting: This event provides an excellent opportunity for the DDLS Evolution and Biodiversity research area to present ongoing and future work\, get to know one another\, and organize and strengthen the growing community. We will hear about and discuss DDLS infrastructure and research support services\, such as bioinformatics support. \n\n\n\nTarget Group: DDLS Fellows and their PhD\, Postdoc group members\, Research School Member + Main PI of the Evolution and Biodiversity research area\, EB Data Science Node representative. \n\n\n\n \n\n\n\n \n\n\n\nPractical Information and Registration\n\n\n\nA registration link to the meeting is provided in the invitation letter. For questions\, contact DDLS EB project coordinator Maria Hellman\, SLU. \n\n\n\nThe EB research area will cover accommodation and meals for all participants. \n\n\n\nParticipants must cover their own travel costs\, except for Fellows and Expert Group members\, for whom SLU will arrange travel. Therefore\, Fellows and Expert Group members need to provide travel information in the registration form so that Maria Hellman\, SLU can book the travel. \n\n\n\nTo ensure smooth handling of catering\, hotel reservations\, and program details\, please respect the registration deadline of September 12. \n\n\n\nWe look forward to seeing you in Umeå! \n\n\n\n \n\n\n\nOrganizing Committee \n\n\n\nMariana P Braga and José Cerca \n\n\n\nProgram\n\n\n\nProgramme_Umeå OctoberDownload
URL:https://www.scilifelab.se/event/ddls-evolution-and-biodiversity-meeting/
LOCATION:SLU\, Skogishuset Skogsmarksgränd 17\,  Umeå
CATEGORIES:Event
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BEGIN:VEVENT
DTSTART;TZID=Europe/Stockholm:20240425T113000
DTEND;TZID=Europe/Stockholm:20240426T170000
DTSTAMP:20260519T121853
CREATED:20231221T105648Z
LAST-MODIFIED:20240425T193524Z
UID:10001075-1714044600-1714150800@www.scilifelab.se
SUMMARY:Cell and Molecular Biology - DDLS symposium
DESCRIPTION:Welcome to the Cell and Molecular Biology Symposium\, starting with lunch at Restaurant Lyktan from 11:30. The conference then takes place in the Birgit Thilander Lecture Hall in Academicum. The program includes presentations from local and national experts within data driven life science research – and a keynote lecture by Prof. Edda Klipp from the Humboldt University in Berlin. \n\n\n\nDay two\, on April 26\, takes place in the AstraZeneca facilities in Mölndal. Here we will meet with the exciting BioVenture Hub and learn about how AstraZeneca works with data driven life science in a clinical setting. Bus transfer from Clarion Hotel Post leaves at 07:30 directly to Astra Zeneca. \n\n\n\n\nPractical information\n\n\n\n\n\nPublic Transport to Medicinareberget \n\n\n\nThursday\, April 27 \n\n\n\nPlease note that due to construction work\, there are disturbances in the tram operations at nearby stops Medicinaregatan and Sahlgrenska Huvudentrén. \n\n\n\nFind your best route through the Västtrafik website or app. \n\n\n\n\n\nCoach to Astra Zeneca \n\n\n\nFriday\, April 26 \n\n\n\nA chartered bus will depart from Clarion Post Hotel at 07:30\, directly to the Astra Zeneca Conference Center (PGN Entrance).  \n\n\n\n\n\nGuidelines for Self-Transport Arrivals to Astra Zeneca \n\n\n\nFriday\, April 26 \n\n\n\nWhether you’re arriving by public transport\, bike\, or car\, kindly make your way to the KC-entrance at Pepparedsleden 1. \n\n\n\nPlease be punctual at 08:00\, as you’ll be guided from the entrance to the conference area. \n\n\n\n\n\n\nProgram\n\n\n\nClick to expand and see the program \n\n\n\nThursday\, April 25\nApril 2510:30Expert Group meeting11:30LunchRestaurant Lyktan13:00Welcome WordsMargit Mahlapuu\, University of GothenburgSverker Holmgren\, ChalmersOla Engkvist\, AstraZeneca13:10Precision Glycomics: How AI and advanced mass spectrometry are changing the gameDaniel Bojar\, University of Gothenburg13:40Towards a universal molecular framework for structure prediction and designPatrick Bryant\, SciLifeLab/Stockholm University (DDLS Fellow)Protein-ligand docking is an established tool in drug discovery and development to narrow down potential therapeutics for experimental testing. However\, a high-quality protein structure is required and often the protein is treated as fully or partially rigid. Here we develop an AI system that can predict the fully flexible all-atom structure of protein-ligand complexes directly\, given a multiple sequence alignment representation of the protein\, protein pocket information\, and a SMILES string representing the ligand.14:10Integrating single-cell transcriptomics with cellular phenotypesJoan Camuñas\, University of Gothenburg14:40Thermodynamics-informed modeling of biochemical reaction and regulation networksEdda Klipp\, Humboldt-Universität zu Berlin15:20Coffee Break15:50Tumor biology using multiomicsAnders Ståhlberg\, University of Gothenburg16:10Multiomics – state of art data generation and analysis at NGIMattias Ormestad and Franziska Bonath\, National Genomics InfrastructureMultiomics projects can span over long time frames\, sometimes years\, until all data is created. Often sequencing is an integral part and is outsourced to a core facility like NGI. For projects in which data generation is performed by different groups\, it is crucial that important information about library preparation\, sequencing and analysis is recorded and available to all other participants. Therefore\, NGI applies quality control measures and reports important metadata to ensure that our users can use our data with confidence even years after data generation.16:30Data-Driven Microscopy: the automation of end-to-end imaging workflowsElisabet Carlsohn and Rafael Camacho\, SciLifeLab/University of GothenburgBiological complexity mandates comprehensive imaging for statistically significant results. Manual operation impedes large dataset acquisition\, affecting reproducibility. Smart microscopy\, integrating image analysis and computer-controlled microscopes\, streamlines workflows. At GU’s Centre for Cellular Imaging\, we offer open-access “smart microscopy” services optimizing data management with next-gen file formats\, ome-zarr\, community-driven visualization tools\, napari\, and collaborative platforms\, webknossos. This ensures efficient resource utilization in the era of data-driven life sciences.17:00Tour to SciLifeLab units in Gothenburg Optional\, sign up in registration form18:00Dinner for CMB fellows and experts\, and speakers\n\n\n\n \n\n\n\n\nFriday\, April 26\nApril 2607:30Bus transport to Astra Zeneca Conference Centre in Mölndal08:00Visitor registration Astra Zeneca Conference Centre in Mölndal08:25IntroductionOla Engkvist\, AstraZeneca & Margit Mahlapuu\, University of Gothenburg08:30Empowering data-driven innovations in Life Sciences: A journey with GU VenturesKlementina Österberg\, GU Ventures & Carl-Peter Mattsson\, GU VenturesKlementina Österberg CEO & Carl-Peter Mattsson Investment director at GU Ventures will present their experiences and the journeys to create successful new business in collaborations with investors\, industry and the eco system. They will also show some examples of these journeys with their companies.09:15BioVentureHub – an antidote to the incubent’s curseMagnus Björsne\, AstraZeneca BioVentureHub10:00Coffee Break10:30Precision Medicine from the start – using omic data to deliver actionable insight to drug discovery and developmentDaniel Muthas\, AstraZeneca11:15Data driven life sciences in a clinical settingJesper Havsol\, AstraZeneca12:00Networking lunch12:40Tour –  “The Amazing Journey the story about Astra Zeneca in Mölndal”Optional\, sign up in registration form\n\n\n\n \n\n\n\n\nSymposium Program for Download and PrintDownload\n\n\n\nData-driven Life Science\n\n\n\nThe future of life science is data-driven\, providing major new opportunities to explore and understand biology\, human health and changing ecosystems. The SciLifeLab & Wallenberg National Program for Data-Driven Life Science (DDLS) is set up to make use of these opportunities.
URL:https://www.scilifelab.se/event/ddls-symposium-cell-and-molecular-biology/
LOCATION:Birgit Thilander Lecture Hall\, Medicinaregatan 3\, Göteborg
CATEGORIES:Event
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BEGIN:VEVENT
DTSTART;TZID=Europe/Stockholm:20231117T090000
DTEND;TZID=Europe/Stockholm:20231117T141000
DTSTAMP:20260519T121853
CREATED:20230830T210018Z
LAST-MODIFIED:20231112T090520Z
UID:10000962-1700211600-1700230200@www.scilifelab.se
SUMMARY:Cell and Molecular Biology Symposium
DESCRIPTION:The DDLS research area\, Cell and molecular biology\, invites you to the first in-person symposium. The day will be an introduction to the research area Fellows and Expert Group. \n\n\n\nRegistration\n\n\n\nWe have a limited number of seats (52p). All participants must register (including the speakers i.e.\, DDLS Fellows and research area Expert group). Participant list here  If you register after Nov 7\, we offer fika and lunch but can’t accommodate food preferences.  \n\n\n\nRegistration\n\n\n\nAgenda\n\n\n\n08:45Registration\, check-in and Coffee09:00Introduction to Data-driven Life Science program and the Cell and molecular biology research areaMargit Mahlapuu\, Chair of the DDLS CMB Expert groupPresentations of fellows\, Expert group members\, and Data science node09:15Title TBAJuliette Griffie\, DDLS Fellow09:40Making it work in reality – bridging the gap between curated proof of concept tests and real world deployment of biomedical image based deep learningIda-Maria Sintorn\, DDLS Expert group  09:55AICell Lab: Towards AI-powered Data-driven Whole-cell ModelingWei Ouyang\, DDLS Fellow10:20Title TBAErik Lindahl\, DDLS Expert group10:35Coffee break 10:50Time-resolved structural studies of photoactive proteinsSebastian Westenhoff\, DDLS Expert group11:05From model systems to global surveillance programs for infectious diseases: Some challenges and the road aheadJohan Bengtsson-Palme\, DDLS Fellow11:30The DDLS Data Science Node – status report towards supporting the national CMB research communityThomas Svensson\, Sverker Holmgren\, Data Science node12:00RNA insights from extinct animals and single cellsMarc Friedländer\, DDLS Expert group12:15Understanding intestinal diseases with spatial transcriptomicEduardo Villablanca\, DDLS Expert group12:30Lunch13:15“Weak\, noisy and uninteresting”: subtle features of macromolecular structuresNicholas Pearce\, DDLS Fellow13:40Metabolic Liver Disease: Molecular Mechanisms and Novel TargetsMargit Mahlapuu\, DDLS Expert group13:55Wrap-up and Action item summaryMargit Mahlapuu14:10End of the day
URL:https://www.scilifelab.se/event/cell-and-molecular-biology-symposium/
LOCATION:Biomedicum\, room Peter Reichard\, Solnavägen 9\, Stockholm\, 171 65
CATEGORIES:Event
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BEGIN:VEVENT
DTSTART;TZID=Europe/Stockholm:20230921T090000
DTEND;TZID=Europe/Stockholm:20230921T170000
DTSTAMP:20260519T121853
CREATED:20230426T155028Z
LAST-MODIFIED:20230922T130208Z
UID:10000871-1695286800-1695315600@www.scilifelab.se
SUMMARY:Computational Methods in Evolution and Biodiversity
DESCRIPTION:This symposium and workshop will showcase the latest computational methods for analysing big data in evolution and biodiversity and provide an opportunity for participants to gain hands-on experience in these methods. Two keynote speakers will discuss a) new advances in using image recognition to analyse biodiversity and b) population genomics approaches to understand the effects of climate adaptation on genetic diversity. In addition\, there will be three parallel computer workshops focused on the application of computational and machine learning methods to genome variation and biodiversity data. Participants should bring their own computers to join these workshops. This symposium is organised by the DDLS Evolution and Biodiversity expert group in conjunction with the SciLifeLab summit on Genomics of Biodiversity and Evolution. \n\n\n\nPost-symposium material from the Workshops\n\n\n\nWe have the workshop leaders’ permission to share links to the material for the workshops in the DDLS symposium Computational Methods in Evolution and Biodiversity. Feel free to do tutorials from the other workshops than the one you attended. \n\n\n\nTobias Andermann’s workshop (slides\, tutorial\, and data): https://github.com/tandermann/ai_workshop \n\n\n\nPer Unneberg’s workshop: https://percyfal.github.io/workshop-biodiversity-summit/lab/index.html \n\n\n\nMarcin Kierczak’s workshop: https://github.com/mkierczak/autoencoders_workshop \n\n\n\n\n\n\n\n\n\n\n\nProgram\n\n\n\n09:00Welcome and introduction09:10Challenges in Fine-Grained Image Analysis. Keynote speaker: Serge Belongie\, Pioneer Center for AI\, Denmark09:50Computational methods give insight into paradigms and paradoxes in landscape genomics. Keynote speaker: Katie E. Lotterhos Northeastern University\, USA10:30CoffeeIntroduction to Workshop x 311:00Looking at population structure from a machine learning perspective\, Marcin Kierczak11:25Neural Networks for biodiversity research: challenges and opportunities\, Tobias Andermann11:50Inference of ancestral recombination graphs for population genomics\, Per Unneberg12:15Lunch13:30Workshop x 3; parallel sessions:Room P232\, P224\, P216. (Guidance on site)From PCA to Generative Deep Learning Models for better understanding population structure\, Marcin KierczakBuilding your own customized neural network model (bring your own data if you want)\, Tobias AndermannIntroduction to whole genome tree sequence inference with tsinfer\, Per Unneberg17:00End of DayVenue: Vivi Täckholmsalen (Q-salen)\, NPQ-huset\, Svante Arrhenius väg 20\, Stockholm University\n\n\n\n\n\n\n\nAbstract Serge Belongie\, Pioneer Center for AI\, Denmark\nChallenges in Fine-Grained Image Analysis\n\n\n\nFine-grained image analysis (FGIA) is a longstanding and fundamental problem in computer vision and pattern recognition\, and underpins a diverse set of real-world applications. The task of FGIA is concerned with visual objects from subordinate categories\, e.g.\, species of birds or models of cars. The small inter-class and large intra-class variation inherent to fine-grained image analysis makes it a challenging problem. Capitalizing on advances in deep learning\, in recent years we have witnessed remarkable progress in deep learning powered FGIA. In this talk we review representative examples in the context of recognition\, retrieval\, and generation/synthesis. In addition\, we also review other key issues of FGIA\, such as publicly available benchmark datasets\, related domain-specific applications\, and connections with other modalities including text and audio. We conclude by highlighting several research directions and open problems. \n\n\n\n\nAbstract Katie E. Lotterhos\, Northeastern University\, USA\nComputational methods give insight into paradigms and paradoxes in landscape genomics \n\n\n\nPredicting organisms’ vulnerabilities to rapid and multivariate climate change is a major scientific challenge. A hurdle to addressing this challenge arises from evolution in multivariate environments. This talk will highlight how adaptation in multivariate environments can lead to unexpected patterns at the alleles under selection\, which has implications for the inference of the genetic basis of adaptation and for predicting vulnerability to environmental change.  \n\n\n\n\n\n\n\n\nWorkshop\n\n\n\nFrom PCA to Generative Deep Learning Models for better understanding population structure\, Marcin Kierczak \n\n\n\nThe workshop will focus on looking at different ways of modelling and visualising population structure based on genomic kinship. Starting from more traditional approaches like PCs or MDS as a benchmark\, we will build more complex deep learning-based models and discuss when such approach can be beneficial. Finally\, we will see how deep learning can potentially be used to augment original input data with some artificially-generated individuals with desired pre-defined kinship relations. Throughout this workshop\, we will be using Python and keras interface to Tensorflow. \n\n\n\nBuilding your own customized neural network model (bring your own data if you want)\, Tobias Andermann \n\n\n\nIn this workshop we will cover some computational and data processing tools that will come in handy when working with neural network models. The workshop is focused on implementing your own custom-built neural network model for a chosen task. The provided examples will be from the field of biodiversity research\, but you can apply the tools we cover during the workshop to problems in other research fields. In general this is an easy to follow and hands-on introduction to using neural network models. The workshop requires very basic familiarity with Python\, as the model will be implemented using the Python tensorflow library. \n\n\n\nIntroduction to whole genome tree sequence inference with tsinfer\, Per Unneberg \n\n\n\nIn this workshop\, we will introduce tree sequence (a.k.a. ARG) inference using the tsinfer library. We will build tree sequences from input variation data and look at some applications and analyses using the resulting tree sequences. As the exercises will be performed in jupyter notebooks in Python\, basic familiarity with Python is required. \n\n\n\n \n\n\n\n\n\n\n\nScientific Committee\n\n\n\n\nFredrik Ronquist\, NRM\n\n\n\nTanja Slotte\, SU\n\n\n\nMatthew Webster\, UU\n\n\n\n\n\n\nData-driven Evolution and biodiversity\n\n\n\nThe DDLS subject area concerns research that takes advantage of the massive data streams offered by techniques such as high-throughput sequencing of genomes and biomes\, continuous recording of video and audio in the wild\, high-throughput imaging of biological specimens\, and large-scale remote monitoring of organisms or habitats. This research subject area aims to lead the development or application of novel methods relying on machine learning\, artificial intelligence\, or other computational techniques to analyze these data and take advantage of such methods in addressing major scientific questions in evolution and biodiversity. \n\n\n\nThe research area Expert Group arranges symposia and workshops and welcomes interested to join the activities. More information about  the research area Evolution and Biodiversity here.
URL:https://www.scilifelab.se/event/computational-methods-in-evolution-and-biodiversity/
LOCATION:Vivi Täckholmsalen (Q-salen)\, NPQ-huset\, Stockholm University\, Svante Arrhenius väg 20\, Stockholm\, Sweden
CATEGORIES:Event
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BEGIN:VEVENT
DTSTART;TZID=Europe/Stockholm:20221207T130000
DTEND;TZID=Europe/Stockholm:20221208T130000
DTSTAMP:20260519T121853
CREATED:20221026T084455Z
LAST-MODIFIED:20221109T150910Z
UID:10000722-1670418000-1670504400@www.scilifelab.se
SUMMARY:Data-driven Epidemiology and biology of infections Research Area Symposium
DESCRIPTION:Welcome to a symposia with the new DDLS Fellows and national as well as international researchers within the area of data-driven epidemiology and biology of infections. \n\n\n\nWe encourage personal participation to network with the new DDLS Fellows and other experts of the field however online participation is possible. \n\n\n\nDeadline for registration November 29th \n\n\n\n\n\n\n\nRegister here\n\n\n\nProgram Wednesday\, 7 December\n\n\n\n12:00 LUNCH will be served \n\n\n\n13:00- 13:20 Welcomes and Introductions \n\n\n\nSiv Andersson\, Head of Basic Research at Knut and Alice Wallenberg Foundation\, Uppsala University\,  \n\n\n\nOlli Kallioniemi\, Director SciLifeLab and DDLS Director\, Karolinska Institutet \n\n\n\nMaria Smedh Site Coordinator\, SciLifeLab Göteborg \n\n\n\n13:20 DDLS in Epidemiology and Biology of Infection  \n\n\n\nOliver Billker\, DDLS Research Area Lead\, Umeå University \n\n\n\n13:30 Keynote: Using genomic surveillance data to understand bacterial epidemiology \n\n\n\nNicholas J Croucher\, Imperial College London \n\n\n\n14:10 DDLS Fellow talk: Predicting the disease threats of the future \n\n\n\nJohan Bengtsson-Palme\, DDLS Fellow at Chalmers University of Technology \n\n\n\n14:50 Keynote (virtual) Data and decisions in an imperfect world  \n\n\n\nBill Hanage\, Associate Professor\, Harvard\, School of Public Health  \n\n\n\n15:30 COFFEE \n\n\n\n16:00 DDLS Fellow talk: A global perspective on antimicrobial resistance in animals raised for food \n\n\n\nThomas van Boeckel\, ETH Zurich and future DDLS Fellow at Gothenburg University \n\n\n\n16:40 Pandemic preparedness – The SciLifeLab perspective \n\n\n\nStaffan Svärd\, SciLifeLab Scientific Director\, Uppsala University \n\n\n\n17:10 Discussion and conclusion of the first day \n\n\n\n17:30 Snacks and mingle in Restaurant Nordic \n\n\n\n18:30 SYMPOSIUM DINNER in Restaurant Nordic \n\n\n\n\n\n\n\nProgram Thursday\, 8 December\n\n\n\n8:30 DDLS Fellow Talk: Strength in numbers: leveraging publicly available (meta)genomic data to gain insight into the epidemiology and virulence potential of bacterial pathogens \n\n\n\nLaura Michelle Carroll\, Umeå University \n\n\n\n9:10 Keynote: Data-driven dynamic disease surveillance and studies for mapping systemic effects of the human gut microbiome \n\n\n\nTove Fall\, Uppsala University \n\n\n\n9:50 COFFEE \n\n\n\n10:20 DDLS Fellow Talk: Intimately microbial: symbioses in OB-GYN\,  \n\n\n\nLuisa Warchavchik Hugerth\, DDLS Fellow Uppsala University  \n\n\n\n11:00 National services for data and bioinformatics within DDLS and SciLifeLab \n\n\n\nSara El-Gebali\, SciLifeLab Data Centre\, \n\n\n\nBjörn Nystedt\, National Bioinformatics Infrastructure \n\n\n\nShort presentations followed by a discussion to determine needs for computational support for infections research in Sweden. \n\n\n\n12:00 Discussion: DDLS initiative and Data-Driven Epidemiology and Biology of Infection: where next? \n\n\n\n12:30 LUNCH will be served
URL:https://www.scilifelab.se/event/data-driven-epidemiology-and-biology-of-infections-research-area-symposium/
LOCATION:Europasalen\, Konferenscentrum Wallenberg\, Gothenburg\, Medicinaregatan 20\, Göteborg\, 413 90\, Sweden
CATEGORIES:Event
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Stockholm:20220908T120000
DTEND;TZID=Europe/Stockholm:20220909T120000
DTSTAMP:20260519T121853
CREATED:20220616T150853Z
LAST-MODIFIED:20220915T092128Z
UID:10000626-1662638400-1662724800@www.scilifelab.se
SUMMARY:Data-Driven Evolution and Biodiversity Research
DESCRIPTION:Mini-symposium \n\n\n\nWelcome to the first mini-symposium in data-driven evolution and biodiversity research sponsored by the Data-Driven Life Science (DDLS) program! At the mini-symposium\, the first group of DDLS Fellows in evolution and biodiversity will present themselves and their research plans. There will be several inspiring keynote talks\, presentations of the Swedish infrastructure for data-driven research in evolution and biodiversity\, and discussions of the future of the DDLS program. \n\n\n\nJoin us for an inspiring lunch-to-lunch meeting\, and help shape the future of data-driven evolution and biodiversity research in Sweden! \n\n\n\n\n\n\n\nHybrid event\n\n\n\nParticipate on site: New venue! Europasalen\, Konferenscentrum Wallenberg\, Medicinaregatan 20A\, Gothenburg\, Sweden. \n\n\n\nParticipate online: Online participation info\, e.g\, zoom link will be sent out upon registration \n\n\n\n\n\n\n\nDownload the Program here (PDF)  \n\n\n\nRegistration\n\n\n\n\n\n\n\nShort program\n\n\n\nThursday\, September 8\n\n\n\n12:00Lunch13:00Symposium starts (first day)17:00End of first day19:00SYMPOSIUM DINNER at Koks\, Vasagatan 9\, GothenburgAttendance is optional and the cost is the participant’s responsibility.\n\n\n\nFriday\, September 9\n\n\n\n08:30Symposium starts (second day)12:00Lunch\, end of the symposium\n\n\n\n\n\nPractical information\n\n\n\nIT support and adaptors will be available for presentersDeadline for onsite registration is August 29 (due to catering). Zoom registrations can be done until the event starts day 2.Lodging for September 8th is the attendee’s responsibility\, and there are many different kinds of options nearby the site. The speakers are booked at Hotel Poseidon.
URL:https://www.scilifelab.se/event/data-driven-evolution-and-biodiversity-research/
LOCATION:Europasalen\, Konferenscentrum Wallenberg\, Gothenburg\, Medicinaregatan 20\, Göteborg\, 413 90\, Sweden
CATEGORIES:Event
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