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SUMMARY:DDLS Annual Conference 2026
DESCRIPTION:The Data-Driven Life Science (DDLS) program\, funded by the Knut and Alice Wallenberg Foundation (KAW)\, warmly welcomes you to the 5th DDLS Annual Conference\, taking place November 9-10\, 2026\, at Aula Magna\, Frescati Campus\, Stockholm. \n\n\n\nTarget Group: Anyone interested in data-driven life science is welcome to attend the Conference. You don´t need to be part of the SciLifeLab & Wallenberg National Program for Data-Driven Life Science\, even though the conference focuses on achievements and highlights program activities. \n\n\n\nThis two-day\, in-person event will highlight DDLS research and activities\, showcase new technologies and services available to researchers across Sweden\, and provide a forum for knowledge exchange and collaboration. Participants will have the opportunity to: \n\n\n\n\nShare and discuss research findings\n\n\n\nExplore ideas and insights across disciplines\n\n\n\nBuild networks for collaboration and career development\n\n\n\nGain perspectives beyond their own fields of expertise\n\n\n\n\nThe conference begins with a light network lunch and registration on November 9 (11:30–12:30) and concludes with a networking lunch on November 10 (12:30–13:30). \n\n\n\nKeynotes\n\n\n\n\n\nLindsay EdwardsDr Lindsay Edwards\, Chief Technology Officer and President of PlatformsRelation Therapeutics\n\n\n\n\n\nKaroline FaustAssociate Professor and head of the Lab of Microbial Systems BiologyKU Leuven\n\n\n\n\n\nProgram\n\n\n\nNovember 9\n\n\n\n11:00Registration opens11:30Light lunch12:30Introduction and Welcome12:50Three Impactful Problems in Drug Discovery That Might Be Solvable with Machine Learning (and What We Are Doing About Them at Relation Therapeutics)Lindsay Edwards\, CTO & President of Platforms\, Relation Therapeutics\, London\, UK13:30Panel DiscussionModerators: Ola Engkvist\, Executive Director AstraZeneca & Adjunct Professor Chalmers University of Technology; Tuuli Lappalainen\, Professor\, KTH/SciLifeLab1. Gianfranco Grompone\, PhD\, CSO\, BioGaia AB & Affiliated Scientist\, Univ. of Gothenburg2. Lina Hultin Rosenberg\, Director Data & Analytics\, R&D\, Olink / Thermo Fisher3. Peetra Magnusson\, PhD\, Assoc. Prof.\, Dept. of IGP & DDLS Director\, Uppsala Univ.4. Patrik Rydén\, PhD\, Prof. of Math Statistics & IDS Director\, Umeå University5. Lindsey Edwards\, CTO & President of Platforms\, Relation Therapeutics\, London\, UK14:00Poster session I (odd numbers) with Coffee15:00DDLS Fellows talksUnderstanding Cancer Heterogeneity Through Machine Learning and Molecular NetworksGolnaz Taheri\, KTHPredictors of genome-wide variation in the effects of new mutationsJennifer James\, UUTowards autoregularization of reconstruction and datamining in cryo-EMBjörn Forsberg\, LiU16:00Meet the new DDLS Fellows16:15Presentation of NAISSErik Lindahl\, LiU\, director of NAISS16:30Poster session II (even numbers) with Mingle17:30End of day 1\n\n\n\nNovember 10\n\n\n\n09:00DDLS Fellows talksLeveraging population genomic data to understand clonal hematopoiesis and blood cancer riskAbhishek Niroula\, GUSteering AlphaFold through the distogramNicholas Pearce\, UUExploring chemical space with distillation modelsAndreas Luttens\, KI10:05Coffee break10:50Top-Down and Bottom-Up Exploration of Alternative Community Types in the Human Gut MicrobiomeKaroline Faust\, Associate Professor\, KU Leuven\, Belgium11:30Presentations of DDLS funded projectsLearning 3D genome dynamics from heterogeneous dataJohan Elf\, UUMultimodal AI-based Precision Diagnostics and Decision Support for Breast Cancer (AID4BC)Mattias Rantalainen\, KITIMED: Time-Resolved Imaging and Multi-Channel Evaluation of Cellular DynamicsRocio Mercado\, Chalmers; Ola Spjuth\, UUMicroscopy Foundation Model & AI-Ready Data Hub for Smart MicroscopyHanzhao Zhang\, LiUSwedish Metabarcoding Network (SMN)Tobias Andermann\, UUTissUUmaps RDCPCarolina Wählby\, UUCRITICAL MICROBES – Comprehensive Research on InfecTIons Complications Across the Lifespan – MICROBiology Epidemiology in SwedenAnne-Marie Connolly\, UmU12:15Closing Words and Best Poster Award12:30Lunch and networking13:30End of conference13:30Pre-registered workshop organized by SciLifeLab Data Centre:How to use LLMs to present results from your research project as a web application (register here)\n\n\n\nSee separate registration page to sign up for workshops on November 10. \n\n\n\nProgram_DDLS Annual Conference 2026Download\n\n\n\nKeynotes\n\n\n\nLindsay Edwards\nTitle: Three impactful problems in drug discovery that might be solvable with machine learning (and what we are doing about them at Relation Therapeutics).Bio: Lindsay is an internationally recognised leader at the interface of AI\, machine learning\, computational biology\, and drug discovery. Across both academia and industry\, he has consistently advanced the translation of science into medicine using computational sciences. In his academic career in Australia and at King’s College London\, he developed innovative computational approaches to human metabolism\, hypoxia\, and respiratory physiology. His teams pioneered methods for modelling metabolic and transcriptomic data\, applying these to diverse clinical contexts including respiratory disease\, diabetes\, hypertension\, Parkinson’s disease\, and critical illness. In industry\, Lindsay founded one of the first pharmaceutical data science divisions at GSK\, embedding machine learning and data science into every stage of the discovery pipeline. As CTO of Relation Therapeutics\, he has raised over $200M\, creating one of the most significant TechBio companies rooted in scientific translation (including forging a landmark platform multi billion dollar collaboration with GSK). His role on the Scientific Advisory Board of the Schmidt Centre at the Broad Institute of Harvard and MIT further reflects his international collaborative reach\, engaging with leading global scientists at the intersection of AI and life sciences. His publication record—over 40 papers in top biomedical journals (NEJM\, JAMA\, PNAS) and premier AI/ML venues (NeurIPS\, ICML\, ICLR)—is exceptionally rare\, demonstrating dual impact across medicine and computational science. He was recently elected a Fellow of the Academy of Medical Sciences. \n\n\n\n\nKaroline Faust\nTitle: Top-down and bottom-up exploration of alternative community types in the human gut microbiome.Bio: Karoline Faust is an Associate Professor at KU Leuven who heads the Lab of Microbial Systems Biology\, which focuses on investigating microbial community dynamics in silico and in vitro. In particular\, her team is studying interactions between human gut bacteria and the resulting dynamics with synthetic communities in controlled conditions. In addition\, the team develops new tools for the analysis of microbiome data\, specifically the analysis of microbial networks. The group’s work is thus situated at the intersection of bioinformatics\, systems biology\, and microbial ecology.Abstract: The human colon is home to a diverse microbial community\, the gut microbiome\, which impacts the host’s health and well-being. The composition of the gut microbiome varies across persons\, forming distinct groups known as enterotypes\, which have been linked to differences in host characteristics and behavior such as diet. However\, these environmental factors do not explain observations of transient perturbations leading to community shifts\, which persist even after the perturbation ceased. In my talk\, I will discuss multi-stability as an alternative hypothesis to explain enterotypes. I will present simulations and in vitro experiments that support the idea that alternative community types observed in the gut can be interpreted as different states of a multi-stable community. Investigating the causes behind alternative community types is relevant\, since they affect the interpretation of microbiome data. In particular\, multi-stability implies that different gut microbial community compositions can be due to past perturbations rather than current differences between hosts.Affiliation: Molecular Bacteriology Unit (Rega Institute)\, Department of Microbiology\, Immunology and Transplantation\, KU Leuven \n\n\n\n\nErik Lindahl\nTitle: Presentation of NAISS \n\n\n\nBio:  \n\n\n\nAbstract: \n\n\n\nAffiliation: LiU\, director of NAISS \n\n\n\n\n \n\n\n\nDDLS Fellows Talks\n\n\n\nBjörn Forsberg\nTitle: Towards autoregularization of reconstruction and datamining in cryo-EM \n\n\n\nBio: Researcher in structural biology and method development in cryo-EM \n\n\n\nAbstract: Cryo-EM is the method of choice for visualization of proteins and biomolecules with molecular detail\, permitting multiple states\, binding partners and molecular motions to be visualized with more flexibility than other methods. However\, this is highly dependent on the sample and conditions\, and the wide range of use cases and application details creates a necessity for data processing to be adaptive. Historically\, this has relied on expert users to inject reasonable bias into the processing pipeline\, using deep technical knowledge and elaborate specialized pipelines. Here I will present an overview of how we aim to provide more adaptive control of the data processing\, through construction of natural features that decrease bias with increased meaningful regularization. This also requires a new framework for evaluating performance and better synthetic data for legitimate ground-truth evaluation of current methods of data processing. \n\n\n\nAffiliation: Linköping University \n\n\n\n\nAbhishek Niroula\nTitle: Leveraging population genomic data to understand clonal hematopoiesis and blood cancer risk \n\n\n\nBio: Abhishek Niroula is a DDLS Fellow and Assistant Professor at the University of Gothenburg. He received his PhD from Lund University\, where he studied the impact of genetic variation on human health. During his first postdoctoral appointment\, he worked in computational functional genomics to understand the functional impact of genetic variants identified through genome-wide association studies. He subsequently joined the Broad Institute\, where his postdoctoral research focused on pre-cancerous somatic mutations and the development of models to predict an individual’s risk of developing blood cancer. His laboratory at the University of Gothenburg now investigates the biology of pre-cancerous clones\, the mechanisms that regulate their expansion\, and how these clones contribute to human diseases\, including cancer and cardiovascular disease. \n\n\n\nAbstract: Cancer develops through the gradual accumulation of somatic genetic alterations and clonal evolution\, often over many years. Large-scale genomic and health datasets provide new opportunities to detect these changes at an early stage and identify individuals at increased risk of disease. Our lab studies clonal hematopoiesis\, an age-associated expansion of blood cell clones carrying somatic genetic mutations. Clonal hematopoiesis is associated with an elevated risk of blood cancers as well as non-malignant conditions\, including cardiovascular disease. Using genomic data from large population-based cohorts\, we characterize clonal hematopoiesis in individuals without blood cancer and investigate the genetic drivers and regulators of clonal expansion. By integrating genomic information with demographic\, clinical\, and longitudinal health data\, we investigate the factors that influence malignant transformation and develop models for predicting future blood cancer risk. Ultimately\, our work seeks to improve our understanding of the earliest stages of cancer evolution and provide a foundation for identifying individuals who may benefit from closer monitoring or early intervention. \n\n\n\nAffiliation: Department of Medical Biochemistry and Cell Biology\, Institute of Biomedicine\, Sahlgrenska Academy\, University of Gothenburg\, Gothenburg\, Sweden Science for Life Laboratory\, University of Gothenburg\, Gothenburg\, Sweden \n\n\n\n\nNicholas Pearce\nTitle: Steering AlphaFold through the distogram \n\n\n\nBio: Nicholas Pearce is a DDLS Fellow at Uppsala University working on method development within structural biology\, particularly for the identification of weak and dynamic signals in X-ray crystallography and Cryo-EM data. \n\n\n\nAbstract: Since its publication in 2021\, the networks within AlphaFold2/3 have been prodded and probed to understand how to steer the predicted structures to contain desirable features. We now have several methods that allow the imposition of external restraints on the structure predictions\, whether geometric priors or experimental data. However\, existing methods either use a sample-and-prune approach or else the restraint is a weak prior and can be ignored during structure prediction. We present a method for directly optimizing the pair representation within AlphaFold-like structure predictors to produce desirable features in the distogram\, which can be intuitively linked to different kinds of structural restraint. We demonstrate this “resTrain” method in a variety of applications including conformer generation\, docking\, and NMR structure determination. \n\n\n\nAffiliation: Department of Cell and Molecular Biology\, Uppsala University \n\n\n\n\nGolnaz Taheri\nTitle: Understanding Cancer Heterogeneity Through Machine Learning and Molecular Networks \n\n\n\nBio: Golnaz Taheri is an Assistant Professor and Docent at KTH Royal Institute of Technology and a research group leader at SciLifeLab. She is a WASP and DDLS Fellow and a member of the Digital Futures faculty. Her research focuses on developing machine-learning methods for complex biomedical data\, particularly graph-based modelling and multimodal data integration. Her group combines methodological development with applications in cancer genomics\, drug interactions and response\, with an emphasis on biological interpretation. \n\n\n\nAbstract: Cancer heterogeneity reflects both molecular differences within tumours and interactions between cancer cells and their surroundings. In this talk\, I will discuss how machine learning and network modelling can help study this complexity\, drawing on breast and prostate cancer research and ongoing work investigating how cancer–fibroblast interactions influence responses to drug perturbations. The main focus will be our framework for prioritising candidate regulatory genes in breast cancer. Rather than analysing all tumours through a shared molecular network\, we infer a separate regulatory network for each breast cancer subtype and combine gene-expression features with information about each gene’s position in the network. I will present how our findings recover known cancer genes and subtype-relevant biological pathways\, and highlight a candidate gene in one of the breast cancer subtypes\, where its expression is associated with patient outcomes. Through these examples\, I will discuss the contribution of network structure to gene prioritisation and the biological interpretation of cancer heterogeneity across molecular and cellular scales. \n\n\n\nAffiliation: Department of Computational Science and Technology\, School of Electrical Engineering and Computer Science\, KTH Royal Institute of Technology\, and Science for Life Laboratory (SciLifeLab)\, Stockholm\, Sweden. \n\n\n\n\nJennifer James\nTitle: Predictors of genome-wide variation in the effects of new mutations \n\n\n\nBio: As a researcher\, I’m fascinated by new mutations; these provide the vital raw material necessary for evolution and adaptation. New mutations occur regularly\, generating genetic diversity- new heritable variation among individuals. However\, how many of these new mutations generate variation in the traits of the individuals that carry them? And\, most importantly for evolution by natural selection\, how do new mutations impact fitness\, the ability of individuals to survive and reproduce? My work seeks to address these important questions\, using a combination of population genetics and proteomics. \n\n\n\nAbstract: In our recent work\, we assessed how predictive gene features (such as local recombination rate\, level of organisation within gene gene networks\, and gene length) are to the level of selective constraint genes experience\, and assessed how this impacts the effects that new mutations have on such genes. We used data from the full genomes of Mus musculus castaneus (mouse)\, Drosophila melanogaster (fruit fly) and Saccharomyces cerevisiae (yeast)\, and a combination of population genetics and machine learning inference methods. We found that genes with higher connectivity in gene interaction networks and higher levels of expression were under greater selective constraint\, and new mutations in such genes had the strongest deleterious effects.  Our results highlight the value of using gene features collectively to link the genomic architecture\, fitness landscapes\, and patterns of molecular evolution. We also found that genes with the highest proportion of fixed beneficial mutations and therefore the highest rates of adaptive evolution tended to be those that have intermediate levels of selective constraint- i.e.\, not those genes that are under extremely strong negative selection but also not the genes under low levels of selective constraint. These are perhaps the genes in a ‘happy middle ground’- mutations in these genes are able to effect moderate phenotypic change when adaptive evolution is required\, but not so much change that they risk disrupting many systems at once. \n\n\n\nAffiliation: DDLS fellow Uppsala University \n\n\n\n\nAndreas Luttens\nTitle: Exploring Chemical Space with Distillation Models \n\n\n\nBio: Andreas earned his degree in chemistry from Ghent University\, Belgium\, before completing a PhD in molecular life sciences at Uppsala University\, where he developed computational approaches to explore large chemical databases for novel drug candidates. He then joined MIT as a Wallenberg postdoctoral fellow\, focusing on deep learning methods to discover new antibiotics against drug-resistant bacteria. Recently appointed as an Assistant Professor\, Data-Driven Life Science Fellow\, and MIMS Investigator at Karolinska Institutet\, SciLifeLab\, and Umeå University\, Andreas leads a lab that harnesses deep learning models to design next-generation anti-infectives. \n\n\n\nAbstract: Discovering molecules with the right properties is a central challenge in drug design. While high-throughput screening can test thousands to millions of compounds\, even large internal collections represent a tiny fraction of accessible chemical space\, and the most promising candidates are often not among them. Scaling up to billion-compound make-on-demand libraries requires deep learning models that generalize well from limited experimental data. In this talk\, I will present GLACIER\, a multimodal foundation model designed for exactly this low-data regime. By combining complementary molecular representations and distilling knowledge from large pretrained models into a lightweight architecture\, GLACIER learns rich embeddings that transfer effectively to new tasks with few labels. I will show how this approach enables robust molecular property prediction and discuss its potential to prioritize synthesis candidates across billion-scale chemical libraries. \n\n\n\nAffiliation: Karolinska Institutet\, SciLifeLab\, Umeå University \n\n\n\n\n\n\n\n\nRegistration\n\n\n\nRegister here\n\n\n\nRegistration and Poster Submission Deadline is on October 21.  \n\n\n\nPoster submission is closed! \n\n\n\nSince we expect some cancellations between the deadline (October 21) and the event date\, we will keep participant registration open until the event date. BUT NOTE! \n\n\n\nA) We cannot accommodate allergies or dietary preferences.  \n\n\n\nB) We can only offer refreshments and lunch if we have the same amount of cancellations. Select your attendance for Lunch and poster mingle\, but please check with the on-site registration desk to see if coffee and lunch are available.  \n\n\n\n\n\n\n\nDDLS Annual Poster Prize\n\n\n\nThe DDLS Poster Prize encourages PhD students and postdoctoral researchers to present high-quality work in data-driven life science. The award\, decided by a Scientific Committee jury\, includes a certificate and a travel grant of up to 5\,000 SEK. Conditions apply. If you present a poster\, please ensure you stay for the Poster Award Ceremony at 12:00 by the end of the Conference. \n\n\n\n\n\n\n\nPoster abstract book\n\n\n\n\n\n\n\nDDLS Annual Conference Workshops 2026\n\n\n\nDuring the second day of the DDLS Annual Conference\, participants will have an opportunity to attend one of two workshops. The workshops are designed for researchers in data-driven life sciences at all career stages. \n\n\n\nEach workshop has a limited number of seats on a first-come\, first-served basis. Those registered after the limit is reached will be placed on a waiting list. The workshops will take place in various rooms at the Aula Magna. All workshops start at 13:30 and end 15:30. Coffee/tea and fika will be served during the workshops. \n\n\n\nFeel free to email datacentre@scilifelab.se with any questions about the workshops. \n\n\n\n \n\n\n\nRead more & register\n\n\n\nWorkshop 1. How to use LLMs to present results from your research project as a web application \n\n\n\nWorkshop 2. Public hosting of Shiny apps: hands-on workshop
URL:https://www.scilifelab.se/event/ddls-annual-conference-2026/
LOCATION:Aula Magna\, Frescativägen 6\, Stockholm\, 114 18 Stockholm\, Sweden
CATEGORIES:Event
ATTACH;FMTTYPE=image/jpeg:https://www.scilifelab.se/wp-content/uploads/2026/03/DDLS-pressmeddelande-2026-03-25_bild-scaled.jpg
ORGANIZER;CN="SciLifeLab Event":MAILTO:events@scilifelab.se
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Stockholm:20261110T133000
DTEND;TZID=Europe/Stockholm:20261110T153000
DTSTAMP:20261022T072219Z
CREATED:20260825T091248Z
LAST-MODIFIED:20261022T072219Z
UID:10001880-1794317400-1794324600@www.scilifelab.se
SUMMARY:DDLS Annual Conference 2026 workshops
DESCRIPTION:During the second day of the DDLS Annual Conference 2026 held at Aula Magna at Stockholm University participants will have an opportunity to attend one of the workshops. The workshops are directed at researchers in data-driven life science in all career stages. \n\n\n\nPlease note that each workshop has a limited number of seats\, on the first come first served basis. Those registered after the limit is reached will be placed on a waiting list (and get invited to register if a spot becomes available). The workshops will take place in various rooms of the main venue of the conference – Aula Magna at Stockholm University. The workshops start at 13:30 and last for up to 2 hours. Coffee/tea and fika will be served during the workshops. \n\n\n\nFeel free to email datacentre@scilifelab.se with any questions about the workshops. \n\n\n\nWorkshops\n\n\n\nWorkshop 1. How to use LLMs to present results from your research project as a web application\n\n\n\nHeld by: team members at SciLifeLab Data Centre\, TBA \n\n\n\nWhere and when: November 10\, 2026 at 13:30-15:30\, room Spelbomskan (Frescativägen 6 Stockholm) \n\n\n\nParticipants: max 30 \n\n\n\n\nRegistration\n\n\n\n\nDescription: In case of many research projects\, there is a need to present results not only as a traditional conference or journal paper but also as an interactive web-based tool that can be used by the readers or collaborators to e.g.\, explore the project data as interactive dashboards\, test out performance of a machine learning model\, input own arbitrary parameters in a simulation tool\, etc. Building such web applications for research projects has become rather easy with the help of large language model based tools such as ChatGPT\, Mistral\, Claude\, etc. This means that researchers can build and share web applications for their research projects without learning Python for web in depth (i.e. only using scientific Python knowledge). In this workshop\, we will demonstrate a few examples of where a web-based application can be built to present the results. Subsequently\, we will show how to package the resulting application and make it available to collaborators or general public through a URL using a SciLifeLab hosting service. Participants can follow along on their own laptops and either use our examples or build web-based applications for their own research projects during the workshop. We will be using a SciLifeLab hosted LLM tool during this workshop so that no data is shared with commercial companies. \n\n\n\nParticipants will need to bring a laptop to this workshop to be able to fully participate. \n\n\n\nWorkshop 2. Public hosting of Shiny apps: hands-on workshop\n\n\n\nHeld by: Staff at SciLifeLab Data Centre \n\n\n\nWhere and when: November 10\, 2026 at 13:30-15:30\, room Polstjärnan (Frescativägen 6 Stockholm) \n\n\n\nParticipants: max 30 \n\n\n\n\nRegistration\n\n\n\n\nDescription: Researchers often create R Shiny applications as part of their projects and want to publish them as research output – as analysis tools with a graphical interface\, as supplementary materials for publications\, in order to make annotation easier\, etc. In order to make Shiny apps available to the collaborators or general public\, they need to be hosted somewhere and have a URL. We will start this workshop by giving an overview of what it means to create and publish Shiny apps as researcher in terms of institutional and funder requirements. We will then take a look at different hosting options for Shiny apps. During the hands-on part\, we will focus on packaging Shiny apps for hosting as Docker images. Together with the participants\, we will step-by-step package and publish an example Shiny app or the participants’ own Shiny app. If you are a researcher with an existing Shiny app you’d like to host\, or if you plan to build a Shiny app in the future\, this workshop is for you. If you already have a Shiny app you would like to host\, bring it\, and we will help you with it. \n\n\n\nExpected outcomes: After this workshop\, the participants will both have an understanding of working with Shiny apps within research projects in a Swedish university context and have practical knowledge about public hosting of Shiny apps. \n\n\n\nParticipants will need to bring a laptop to this workshop to be able to fully participate. \n\n\n\nThe second workshop has been cancelled due to workshop leaders’ schedule conflict. This workshop will instead be offered to the DDLS community in some format in 2027.
URL:https://www.scilifelab.se/event/ddls-annual-conference-2026-workshops/
LOCATION:Aula Magna\, Frescativägen 6\, Stockholm\, 114 18 Stockholm\, Sweden
CATEGORIES:Event
ORGANIZER;CN="SciLifeLab Data Centre":MAILTO:datacentre@scilifelab.se
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Stockholm:20261126T083000
DTEND;TZID=Europe/Stockholm:20261126T170000
DTSTAMP:20261026T110042Z
CREATED:20260603T084114Z
LAST-MODIFIED:20261026T110042Z
UID:10001841-1795681800-1795712400@www.scilifelab.se
SUMMARY:Mechanistic Modelling in Pharmacology\, Physiology\, and Neuroscience
DESCRIPTION:Understanding complex biological systems requires more than data\, it demands mechanistic insight. This conference highlights how mechanistic models can integrate clinical and experimental data with computational approaches. \n\n\n\n\n\n\n\nThe volume and complexity of life science data are growing at an unprecedented pace. While computational tools are increasingly used to structure and analyze these data\, there remains a critical gap in our ability to understand the data in a mechanistic and physically realistic way. Bridging this gap is essential for uncovering the fundamental principles that govern the function of molecules\, cells\, organs\, and entire organisms. \n\n\n\nMechanistic models play a central role in this effort and are key to advancing applications such as the development of new pharmaceuticals and improved clinical interventions. However\, progress is often hindered by the lack of a shared language between experimental scientists\, clinicians\, and computational researchers. \n\n\n\nThis conference will bring together experts from these fields to explore state-of-the-art tools and methodologies\, discuss how they can be effectively applied\, and foster stronger integration across disciplines. By promoting collaboration and mutual understanding\, the meeting aims to accelerate progress in pharmacology\, physiology\, and neuroscience. \n\n\n\n\nRegistration closed\n\n\n\n\nRegistration deadline October 25 \n\n\n\nProgram\n\n\n\n8:30 Registration opens \n\n\n\n9:00-12:15 Morning session: Molecules and Cells \n\n\n\nOpening – Fredrik Elinder \n\n\n\n \n\n\n\nJan Ellenberg – Imaging the molecular processes of cell division across scales\nProfessor\, Director of SciLifeLab \n\n\n\nThe rapid development of new imaging technologies allows unprecedented insights into the molecular machinery inside living cells and organisms. For the first time\, light and electron microscopy have molecular sensitivity and resolving power in situ\, and\, if used together\, can connect structural detail with molecular dynamics of the whole cell. Aided by machine learning driven image analysis powered by open sharing of image data\, this provides unprecedented opportunities for new insights into the molecular mechanisms that drive life’s core functions at the scale of the cell. \n\n\n\nI will present the progress we have made to study one of life’s most fundamental functions\, cell division\, by mapping the dynamic protein network\, assembly of individual protein complexes and genome re-folding that drive it. Our work has studied cell division in human cancer cells and early mammalian embryos using advanced cross-scale imaging methods\, including light-sheet\, quantitative fluorescence correlation spectroscopy (FCS)-calibrated\, super-resolution and correlative light and electron microscopy. The quantitative integrated molecular data that these new technologies deliver\, allow us to better understand how the molecular machinery functions in space and time to ensure faithful cell division and prevent the errors that underlie congenital disease\, infertility and cancer. \n\n\n\n\nLucie Delemotte – Common evolutionary mechanism of activation of class A GPCRs\nSciLifeLab fellow\, KTH Royal Institute of Technology \n\n\n\nCells communicate with their environment by integrating signals\, often chemical in nature\, triggered by specific molecules binding to specific membrane-bound receptors\, resulting in a downstream signaling cascade. Arguably\, G-protein-coupled receptors (GPCRs) constitute the most pharmacologically important family of such receptors\, binding small molecules\, peptides\, lipids\, and hormones with high specificity.However\, despite a highly conserved fold and sequence similarity\, GPCRs are still mostly studied on a case-by-case basis. Here\, we infer a general\, evolutionarily conserved mechanism of class A GPCR activation. By leveraging coevolution and machine learning methods applied to all class A GPCRs structures\, we derive a mathematical description (a so-called collective variable – CV) of the receptor’s activation state which is independent of its sequence. Then\, we bias molecular dynamics simulations along this CV to obtain transitions between activation states of a diverse set of class A GPCR family members. To demonstrate that our model generalizes beyond GPCRs in our training set\, we obtain conformational transitions of an orphan receptor\, GPR183. Finally\, we show that we can model ligand effect on the receptors by converging Free Energy Surfaces of activation of the β2-adrenergic receptor within this common mechanism framework. These results\, to ourknowledge\, prove for the first time the existence of a mechanism uniting all class A GPCRs. Our approach thus facilitates direct comparisons between receptors and opens up the possibility of structural and dynamical studies of many orphan and understudied GPCRs. It also serves as a blueprint for inferring family-wide protein mechanisms. \n\n\n\n\nBjörn Forsberg – Direct estimation of cryo-EM fields to control estimation of biological function\nDDLS Fellow\, Linköping University \n\n\n\nLearning molecular variability from cryo-EM data relies on strict manual control of correlated variables\, or the use of general ML frameworks that have to learn the correlative variation in addition to estimating it. This presents a complexity trade-off that is prone to bias in either modality\, preventing maximal use of current datasets. Here we present an approach that seeks to parameterize 3D cryo-EM reconstructions in maximally interpretable ways\, based on general causative mechanisms\, that can then be recast to provide explicit learning objectives through an explicit model. This seeks to minimize the bias incurred by specifying ad-hoc mechanistic models\, and efficiently regularize general learning frameworks. \n\n\n\n\n10:15-10:45 Coffee \n\n\n\nErik Lindahl – Title TBA\nProfessor Stockholm University\, Linköping University and KTH Royal Institute of Technology\, Director of National Academic Infrastructure for Super­computing in Sweden (NAISS) \n\n\n\nFocus TBA \n\n\n\n\nFredrik Heintz – From Language to Knowledge and Back Again\nProfessor\, Linköping University\, WASP Graduate School Director \n\n\n\nFocus TBA \n\n\n\n\nJuliette Griffie – From Black Box to Biological Insight: Generative AI for Life Science and Microscopy\nDDLS fellow\, The Computational Microscopy for Cell Biology (CMCB) laboratory\, Stockholm University \n\n\n\nThe life sciences are entering a transformative era\, with generative AI emerging as a powerful engine for discovery. This talk will explore how these technologies can move beyond prediction towards interpretability\, while being tailored to the realities of biological research\, where datasets are often small and models must be designed to answer very specific biological questions. \n\n\n\n\nPanel Debate\nModerator – Per Petersson \n\n\n\n\n12:15-13:30 Lunch \n\n\n\n13:30-16:45 Afternoon Session – System Biology \n\n\n\n \n\n\n\nPetra Ritter – Trustworthy Digital Brain Twins\nProfessor\, Charité University Medicine Berlin \n\n\n\nThis talk explores the integration of mechanistic modeling\, multiscale physiological data\, and artificial intelligence to construct personalized “Digital Brain Twins” for neuroscientific research and clinical applications. It addresses the computational\, methodological\, and ethical challenges in ensuring these virtual replicas remain dynamic\, precise\, and robustly validated across spatial and temporal scales. Ultimately\, the presentation highlights frameworks for building trustworthy in silico models capable of predicting disease progression\, evaluating targeted pharmacological interventions\, and advancing personalized medicine in neurology and neuroscience. \n\n\n\n\nGunnar Cedersund – M4-HEALTH – hybrid mechanistic and machine learning digital twin models as a basis for a new ecosystem for health\nAssociate Professor\, Linköping University \n\n\n\nFocus TBA \n\n\n\n\nHenrik Jörntell – How brain circuitry operation emerges from nested closed loop interactions through the body\nProfessor\, Lund University \n\n\n\nOne of the hottest scientific topics today is how does the brain actually work and\, in the era of artificial intelligence perhaps even more\, what is ‘intelligence’ really? Here we will address these questions to illustrate the path towards arriving at a general conceptualization of the brain circuitry operation that emerges from the fundamental dynamics of neuron interactions\, i.e. from neurophysiological observations. In this ambition\, there is also a need to explain the resulting roles fulfilled by the different structural subsystems of the nervous system. Fundamental differences with current AI systems is one outcome. Consequences relative to the wider literature are; (i) the function of any one neuron alter depending on context; (ii) in this mode of operation predictive neural activity emerges as a trivial by-product without any assumptions of architectural specializations. This underscores the need for integrated neurophysiological work\, as the broader field is still far away from understanding the physiological infrastructure that underlies the emerging computational architecture of the brain. \n\n\n\n\n14:30-15:00 Coffee \n\n\n\nMorten Grunnet – Neuroscience Drug discovery and AI – Opportunities and limitations\nVice President and Head of Neuroscience at Lundbeck & Affiliate Professor at University of Copenhagen \n\n\n\nHow AI and data-driven approaches are currently being integrated into neuroscience drug discovery\, and how these approaches can be combined with mechanistic understanding across different biological scales – from molecular and cellular mechanisms to circuits\, systems and ultimately clinical phenotypes. A perspective on where the main opportunities\, but also the current limitations\, of AI and mechanistic modeling in pharmaceutical research. \n\n\n\n\nEduard Kerkhoven – From pathway maps to predictive models: Human-GEM and human metabolism\nSenior Researcher\, Chalmers University of Technology \n\n\n\nMetabolic pathway maps are familiar\, but they cannot tell you what happens when a gene is lost or a nutrient runs short. Genome-scale metabolic models turn the same biochemistry into something computable and testable against real perturbations. I will introduce Human-GEM\, the community model of human metabolism\, show a few applications across tissues\, organs and disease\, and suggest that models of this kind become more valuable as omics data accumulate\, by giving those measurements a mechanistic structure to be interpreted against. \n\n\n\n\nJeanette Hellgren-Kotaleski – Simulating the Basal Ganglia in Health and Disease using a Biophysically Detailed Approach\nProfessor\, KTH Royal Institute of Technology \n\n\n\nTo understand any part of the brain\, it is necessary to integrate data from the molecular level up to the systems level. For this effort\, data-driven and mechanistic modelling provides an important tool for synthesizing the diverse experimental data into in silico models. Simulation experiments can then generate new predictions which give rise to novel questions. In our lab\, the microcircuit level is one important starting point when bridging biological scales. Our biophysically detailed neuron and network modelling pipeline integrates data on cellular morphologies\, electrophysiology\, synaptic properties\, and also takes transcriptomics data on e.g. ion channels into account. In my talk I will exemplify insights gained when we apply our modelling pipeline to investigate the structural and functional alterations seen in the diseased striatal microcircuit. \n\n\n\n\nPanel Debate\nModerator: Johan Wessberg \n\n\n\n\nConcluding Remarks – Per Petersson & Johan Wessberg \n\n\n\nConfirmed Panelists \n\n\n\nSiv Andersson\, Professor\, Uppsala University\, Knut and Alice Wallenberg Foundation \n\n\n\nGunnar Schulte\, Professor\, Karolinska Institutet\, The Swedish Research Council \n\n\n\nPartners \n\n\n\nSciLifeLab & the National Committee for Pharmacology\, Physiology and Neuroscience of the Royal Academy of Sciences \n\n\n\nOrganizing committee \n\n\n\nFredrik Elinder\, Professor of Molecular Neurobiology\, Electrophysiologist\, Linköping University \n\n\n\nJohan Wessberg\, Professor and Principal Investigator Department of Physiology / Neurophysiology\, Gothenburg University \n\n\n\nPer Petersson\, Professor in the field of Neurophysiology\, Umeå University
URL:https://www.scilifelab.se/event/mechanistic-modelling-in-pharmacology-physiology-and-neuroscience/
LOCATION:Royal Swedish Academy of Sciences\, Lilla Frescativägen 4A\, Stockholm\, 114 18\, Sweden
CATEGORIES:Event
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ORGANIZER;CN="SciLifeLab Link%C3%B6ping":MAILTO:linkoping@scilifelab.se
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BEGIN:VEVENT
DTSTART;TZID=Europe/Stockholm:20261201T090000
DTEND;TZID=Europe/Stockholm:20261201T190000
DTSTAMP:20261016T151029Z
CREATED:20261015T082050Z
LAST-MODIFIED:20261016T151029Z
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SUMMARY:Hackathon: AI agents transforming life sciences\, medicine\, and healthcare
DESCRIPTION:Join us on 1 December 2026 for a hackathon focused on developing AI agents for applications in life sciences\, medicine and healthcare. There is a lot happening in the agentic AI sphere right now\, and many people have great ideas or knowledge that can be exchanged. We hope that during this event we can build interesting things together\, share knowledge with each other\, and network. \n\n\n\nThe hackathon is open to members of the SciLifeLab\, DDLS and WASP communities and is jointly organised by SciLifeLab\, the WASP Research Arena for Medicine (WARA Medicine)\, and the DDLS research areas of Cell and Molecular Biology and Precision Medicine and Diagnostics. \n\n\n\nEveryone is welcome to join\, whether you have a specific idea for something to build or you simply want to join and build together with others. In the registration form we will ask you to share interests or propose ideas for a project\, and we will do our best to create teams based on the ideas and interests. If you have already formed a team with which you will work\, you can indicate this in the registration form. There will also be opportunities to form or adjust teams at the beginning of the day.  \n\n\n\nDuring the hackathon\, participants will get API keys to access Claude and open weight models hosted by SciLifeLab. We will also facilitate hosting of your tools on the SciLifeLab Kubernetes cluster if needed.We will start the day with breakfast and pitches of various ideas (pre-submitted or spontaneous). We will end the day with pizza for everyone. We can accept up to 70 participants. If you have questions or suggestions\, get in touch with us: datacentre@scilifelab.se \n\n\n\nRegistration\n\n\n\nRegistration is open to members of the SciLifeLab\, DDLS and WASP communities. Participation is free of charge\, and places are limited to 70 participants. \n\n\n\nRegister here\n\n\n\nCancellation\n\n\n\nTo help us minimise empty seats and food waste\, please cancel your registration if you are no longer able to attend the event. If a waiting list is active\, this will allow us to offer your place to another participant. Timely cancellations also help us avoid unnecessary costs for refreshments and lunch. \n\n\n\nIf you have questions or suggestions\, please get in touch with us: datacentre@scilifelab.se \n\n\n\nSamuelssonsalen
URL:https://www.scilifelab.se/event/hackathon-ai-agents-transforming-life-sciences-medicine-and-healthcare/
LOCATION:Samuelssonsalen\, Tomtebodavägen 6\, Stockholm\, 17165\, Sweden
CATEGORIES:Event
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BEGIN:VEVENT
DTSTART;TZID=Europe/Stockholm:20261209T090000
DTEND;TZID=Europe/Stockholm:20261209T163000
DTSTAMP:20261027T133311Z
CREATED:20251124T130114Z
LAST-MODIFIED:20261027T133311Z
UID:10001673-1796806800-1796833800@www.scilifelab.se
SUMMARY:Heart 360° - Harnessing AI for Cardiovascular Research\, Diagnosis\, Therapy & Prevention
DESCRIPTION:Join us for an inspiring day at the forefront of cardiovascular research\, where AI helps to advance precision medicine.\n\n\n\n \n\n\n\nHeart 360° is a focused seminar exploring the transformative role of artificial intelligence (AI) across the cardiovascular disease spectrum\, from early discovery to prevention. As cardiovascular disease remains a leading global health burden\, the integration of AI into research\, clinical care\, drug development\, and public health offers new opportunities to enhance understanding\, precision\, and outcomes. \n\n\n\nThe seminar is structured into four sessions: Discovery\, Diagnosis\, Drug Development\, and Prevention\, bringing together experts from academia\, healthcare\, and industry. Each session will highlight how AI-driven tools are reshaping translational research\, enabling earlier and more accurate diagnoses\, accelerating therapeutic innovation\, and supporting data-driven prevention strategies. \n\n\n\nBy fostering interdisciplinary dialogue\, Heart 360° aims to showcase cutting-edge applications of AI\, identify key challenges\, and inspire collaborative efforts to drive the future of cardiovascular science and care. \n\n\n\nRegistration\n\n\n\n\nRegister here\n\n\n\n\nRegistration deadline: 11 November  \n\n\n\nConfirmed speakers\n\n\n\n\n\n\n\n\n\nSøren BrunakUniversity of Copenhagen \n\n\n\n\n\n\n\n\n\n\n\n\n\nCharlotta LjungmanSahlgrenska University Hospital \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nMarcus SchindlerScientific and Strategic Advisor Former CSO\, Novo Nordisk \n\n\n\n\n\n\n\n\n\nAraz RawshaniSahlgrenska University Hospital \n\n\n\n\n\nCall for Presentations!\n\n\n\nAre you a PhD student\, postdoc\, or working at a start-up company in the field of AI and cardiovascular disease (CVD) research? \n\n\n\nShowcase your work in a short talk or poster! Simply indicate your preference in the registration form. \n\n\n\nAgenda\n\n\n\nPowered By EmbedPress \n\n\n\nOrganized by\n\n\n\nMuhammad Arif\, DDLS Fellow\, Computational Multi-Omics Lab\, University of Gothenburg \n\n\n\nSponsors
URL:https://www.scilifelab.se/event/heart-360-harnessing-ai-for-cardiovascular-research-diagnosis-therapy-prevention/
LOCATION:Conference Centre Wallenberg\, Medicinaregatan 20A\, Gothenburg\, 413 90\, Sweden
CATEGORIES:Event
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ORGANIZER;CN="SciLifeLab Gothenburg":MAILTO:gothenburg@scilifelab.se
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BEGIN:VEVENT
DTSTART;TZID=Europe/Stockholm:20270406T120000
DTEND;TZID=Europe/Stockholm:20270408T160000
DTSTAMP:20260929T180124Z
CREATED:20260902T125845Z
LAST-MODIFIED:20260929T180124Z
UID:10001883-1807012800-1807200000@www.scilifelab.se
SUMMARY:DDLS Cell & Molecular Biology 2027 theme workshop: Toward a national imaging informatics strategy
DESCRIPTION:Please save the day for the 2027 workshop on the theme “Toward a national imaging informatics strategy” – hosted by DDLS Cell & Molecular Biology. \n\n\n\nMicroscopy is facing ever-increasing challenges: Datasets are reaching terabyte sizes\, computing requires software designed for GPUs\, and there is an increased demand for smart microscopy (“AI in the loop”).  \n\n\n\nVision & Aim: \n\n\n\nFind a way to make microscopes and image data AI-ready – particular focus on large images and image collections \n\n\n\nSolution: \n\n\n\n1. Bring all developers in Sweden together\, produce a solution that matches user needs and is deployable on Swedish HPC infrastructure \n\n\n\n2. Make HPC infrastructure image-ready (S3!) \n\n\n\n3. Form long-term collaborations among the top scientific software developers \n\n\n\nStakeholders: \n\n\n\nSciLifeLab AI strategy \n\n\n\nBIIF (BioImage Informatics Facility) \n\n\n\nNAISS\, local HPC facilities & AI factory \n\n\n\nSciLifeLab Serve / Data Centre \n\n\n\nTarget Group: This event aims to gather stakeholders (power users\, software developers\, HPC and core facility managers) in identifying present and future challenges (first day)\, followed by a 2-day hands-on hackathon and/or focus sessions to begin tackling the most pressing issues. \n\n\n\n\n\n\n\nConfirmed Speakers\n\n\n\nJoel Luhti (BioVisionCenter\, U. Zurich) \n\n\n\nAubrey Weigel (AlphaCell) \n\n\n\nArco Bast (HHMI Janelia Farm) \n\n\n\nPreliminary Program\n\n\n\nTuesday\, April 6\, 2027 \n\n\n\n12:00Registration and LunchSession I:Compute & store of big image dataSession II:Smart microscopyMingle dinner withgames and activities\n\n\n\nWednesday\, April 7\, 2027 \n\n\n\n08:30Session III:AI-ready microscopySession IV:A Swedish nationalimaging strategy15:00End of main program;Start of hackathon\n\n\n\nThursday April 8\, 2027 \n\n\n\n08:30Hackathon\, focus sessions TBA16:00End of hackathon\n\n\n\n\n\n\n\nRegistration\n\n\n\nParticipation fee of 1000 SEK/pp. Includes dinner & activities. \n\n\n\nRegistration will open <date>\, limited seats available\, in-person attendance only. Travel and accommodation will be paid by participants.  \n\n\n\nFor questions\, please contact johan.henriksson@umu.se
URL:https://www.scilifelab.se/event/ddls-cell-molecular-biology-2027-theme-workshop-toward-a-national-imaging-informatics-strategy/
CATEGORIES:Event
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