DDLS Annual Conference

Patrick Bryant, DDLS Fellow, Stockholm University.
My research tries to answer questions about the evolution of proteins and how this information – or the lack of it – can be used to create a new range of AI tools. The tools I have developed make it possible to predict the structure of protein interactions and to design new molecules that bind to proteins. I aim to create a universal molecular framework, where any molecule can be predicted to create new molecular functions e.g. therapeutic applications.

Laura Carroll, DDLS Fellow, Umeå University.

Secondary metabolites (SMs) produced by microbes have critically important roles in human health. While some microbial SMs can be leveraged as essential therapeutics (e.g., antimicrobials, anticancer agents), others have negative impacts on host health (e.g., toxins, carcinogens, pathogen virulence factors). A large proportion of the enzymatic pathways responsible for microbial SM production are encoded by physically clustered groups of genes called biosynthetic gene clusters (BGCs). (Meta)genomic data derived from microbes thus offers tremendous potential as a reservoir for novel BGCs associated with novel SMs. Here, we discuss recent advances in (meta)genome mining approaches for BGC discovery, with an emphasis on rapid computational methods, which allow us to gain insights into the biosynthetic potential of microbial isolates and communities at unprecedented scales.

Chris Erdmann, Head of Open Science, SciLifeLab

In this interactive session, we will explore the principles of FAIR (Findable, Accessible, Interoperable, Reusable) and Open Science, focusing on what they mean for the SciLifeLab community and the broader research landscape. We will highlight real-world examples of how these approaches are being advanced within research workflows and discuss national and international efforts to integrate FAIR and Open Science into funding policies and scholarly workflows. This session will offer practical takeaways for embedding FAIR and Open Science principles into your own research projects. You’ll also learn about current services and projects designed to support these practices, both in operation and under development. Bring your questions and ideas, and get ready to explore how you can make your research more open, accessible, and impactful.

Camila Consiglio, DDLS Fellow, Lund University

Immune-mediated diseases exhibit marked sex bias, with men displaying more severe infections and higher rates of cancer, while women exhibit heightened vaccine responses and prevalence of autoimmunity. Sex hormones are key mediators of such sexual dimorphism, though we currently lack a comprehensive understanding of mechanisms underlying sex differences in human immunity. The Consiglio Lab employs in vitro mechanistic studies and in vivo systems-level immunomonitoring approaches to disentangle the immunological factors contributing to sexual dimorphism in human immunity. Identifying pathways involved in immune sexual dimorphism represents an important step towards precision medicine by enabling tailored treatment for immune-mediated diseases according to one’s biological sex.

Samuel Kaski, Professor, Aalto University, Finland and University of Manchester, UK

Now that research in most empirical sciences has become computational, in the sense that experiments are designed with simulations or insights derived from existing data,  and even replaced with simulations, it is time to ask could we do research more efficiently with new tools, or even do research in new ways. The same questions arise in development work in industry, and design in general.  If we call the constellation of simulation tools a virtual laboratory, an important question is can we have better tools by combining strength across the different fields and developing tools usable across the different virtual laboratories. And when we then have common interfaces for the tools in the virtual laboratories in different fields, we can ask could the researchers be helped even more than the current tools are able to, by collaborative AI methods.

Sven Nelander, Professor, Uppsala University

Abstract TBA

Malvika Sharan, Senior Researcher, Open Research, Tools, Practices and Systems, The Alan Turing Institute, London, UK

In this talk, I will explore open science as a foundation for fostering community-centered adoption of data science and AI, ensuring that research outputs are accessible, openly examined, and able to be built upon by others. I will present The Turing Way, an open-source, community-driven guide focused on making data science and AI reproducible, ethical, and inclusive. I will also share insights from my work in establishing the Research Community Management team alongside the Research Engineering and Research Applications teams at The Turing. Together, these roles serve as a bi-directional conduit for knowledge exchange on data science practices, while championing The Turing Way. Attendees will gain insights into the spectrum of openness in research, including ways to foster and participate in an inclusive, supportive open-source ecosystem as we collaboratively work to improve research culture.

Bio: I am a Senior Researcher at The Alan Turing Institute in London with specialisations in open research and community building. With a PhD in Bioinformatics, I kicked off my professional journey at the European Molecular Biology Laboratory and discovered my passion at the crossroads of research and community impact. I am the co-lead of The Turing Way and the lead of a team of research community managers, both working towards making data science a diverse, global and collaborative effort.

I also co-founded OLS (previously Open Life Science), a non-profit organisation on a mission to build the next generation of diverse leaders through open science. I am a Software Sustainability Institute fellow and have proudly made it to the 100 Brilliant Women in AI Ethics™ – 2024 list. My previous contributions to open science initiatives include advisory roles for NASA TOPS, Society of RSE, Open Bioinformatics Foundation, and MetaDocencia.

Roser Vento-Tormo, Group leader, Wellcome Sanger Institute, UK.

Abstract TBA

Jakob Vogel, DDLS Fellow, Lund University

We have entered a new era of Alzheimer’s disease (AD) research, marked by the emergence of disease modifying therapies that target the disease’s hallmark pathology. However, there is still much we don’t understand about how AD develops, how it progresses, and what drives individual differences in disease expression. Therefore, the field is in need of agile research that uncovers new insights into disease biology, while simultaneously developing practical tools for real-life clinical settings. I will present new work from my lab where we apply data-driven approaches to human clinical data in order to advance our understanding of AD and build useful tools toward clinical translation. This presentation will cover several vignettes including the use of connectome-based generative models to explain the spread of AD pathology through the human brain, using AI to enhance the resolution of clinical brain images, and deriving novel biomarkers and multi-disease diagnostic algorithms from proteomic data. The goal of the presentation will be to introduce the DDLS community to my lab’s pursuits, with the hope of sparking future collaboration.

Aleksej Zelezniak, Associate Professor, Chalmers University of Technology

The central molecular dogma processes, foundational to life sciences, can be precisely controlled to catalyse advancements in biomedicine and biotechnology. Mastery of these processes can lead to transformative therapies and facilitate the transition from a fossil-centric to a bio-based society. To achieve this, our research merges computer and biological sciences by integrating generative machine learning (ML) techniques with synthetic biology. Our primary focus spans two directions: i) enhancing gene regulation for advanced protein expression systems, and ii) refining protein engineering to amplify biotechnology-relevant properties. Through applied science, we elucidate fundamental molecular mechanisms. Our development of quantitatively predictive ML models unveils the underlying mechanistic connections driving the observed molecular phenomena, e.g. such as gene expression control. In this talk, I will provide insights into the applications of generative ML within synthetic biology, share our team’s recent results, and outline our forward-looking research trajectory.