Life Sciences

Computational and Systems Biology

Research in the programme for Computational Biology and Bioinformatics focuses on structural biology and biochemistry, intracellular kinetics and bioinformatics. Development of methods for computer simulations and bioinformatics is a key component of the work in the research programme.

The Molecular Systems Biology programme focuses on development of methods for analyzing images from single-molecule tracking in living cells.

Period: 2011
PI/contact: Johan Åqvist, Johan Elf
University: Uppsala University

Computational and Systems Biology
The Molecular Systems Biology

Leverage digital technology to unveil biological mechanisms in avian migration

The project has two main goals. First, we will use advanced AI algorithms to analyse the behaviour of migratory birds in greater detail than ever before, overcoming the limits of manual methods and addressing key questions in migration ecology. Through cue-conflict experiments, we aim to understand how birds synchronise their internal clocks, determine their location, and calibrate magnetic and celestial compasses. Second, we will develop a low-cost, open-source platform for studying bird migration directly at capture sites—a portable, computer-controlled device that brings lab-level analysis to the field and replaces outdated manual techniques.

Period: 2022–2025
PI/contact: Susanne Åkesson
University: Lund University

Diving deeper than ever into dolphin echolocation

The purpose of this study is to investigate how the bottlenose dolphin (Tursiops truncatus) head tissues contribute to forming echolocation clicks into a forward-projected, collimated beam. This will be achieved through multi-physical three-dimensional (3D) Finite Element Modelling (FEM) of the bottlenose dolphin head, based on unique in vivo computer tomography (CT) images of a trained bottlenose dolphin.

Period: 2022–2024
PI/contact: Josefin Starkhammar
University: Lund University

Establishing the link between prostate cancer microstructure and MRI

The purpose of this project is to develop novel MRI-based biomarkers for prostate cancer detection and diagnosis, and to link the proposed biomarkers to features of the prostate tissue microstructure.
Our aims are to:
I. Develop a data-driven analysis tools of prostate tissue microstructure based on multidimensional MRI to produce salient parameters and biomarker candidates.
II. Perform a longitudinal multidimensional MRI study of mice implanted with human prostate cancer that undergo external radiotherapy. Dissect cancer tissue for Aim III.
III. Develop analysis software to establish link between multidimensional MRI signal/biomarkers and the underlying cancer histology (3D) and immunohistochemistry (2D).

Period: 2023–2024
PI/contact: Filip Szczepankiewicz
University: Lund University

Integrative tools for spatial and multi-omics data

Advances in technology are rapidly increasing the data obtainable from clinical samples. In the era of immunotherapy, understanding the tumor microenvironment is key to selecting effective treatments. Spatial molecular data are essential, but tools for integrating data across modalities are still lacking.

Goal: Develop workflows to integrate image-derived spatial metrics with spatial omics, genomics, and clinical metadata to support predictive models for therapy selection.

We aim to:

1. Extract spatial metrics using deep learning for tissue image segmentation, cell classification, and spatial characterization.
2. Integrate multi-omics data across spatial proteomics, transcriptomics, clinical metadata, and genomics.
3. Build prediction models using machine learning to guide patient stratification and clinical decisions.
4. Create user applications for interactive analysis, visualization, and sharing of integrated spatial and omics data.

Period: 2023–2024
PI/contact: Anna Sandström Gerdtsson
University: Lund University

Pollination eScience: Extending latent-variable modelling of plant-pollinator interactions

Ongoing changes in climate and land-use are exposing plant populations worldwide to pollinator declines and changes in the composition of pollinator assemblages. These new conditions impose novel challenges for plants that rely on animal pollinators for reproduction. Efficient conservation decisions and actions in response to these environmental changes rely on accurate forecasts of how and when plant populations adapt evolutionarily to novel pollination environments. Despite recent empirical advances, I propose that further progress is currently hampered by a lack of appropriate digital tools such as software implementing statistical methods needed to analyse large complex datasets from multi-species natural communities. This project will develop such tools, thus facilitating the research necessary to inform more accurate predictions of the future or animal-pollinated plants and their pollinators.

Period: 2023–2024
PI/contact: Øystein Opedal
University: Lund University

New sequence- and structure-based computational methods to find functional domains of proteins

Protein domains are discrete, functional units within proteins, and they play a pivotal role in determining a protein's overall structure and function. Computational domain prediction is important for predicting the function of proteins encoded in sequenced genomes, and therefore organisms’ metabolic capabilities. However, tools available in publicly accessible databases do not represent all domains, making domain discovery not typically systematic, with unusual proteins found in non-model organisms going under the radar. This is important because these proteins and their domains may have biomedically or biotechnologically important functions. The project will remedy the situation by developing a new approach to searching for domains and domain boundaries systematically, across the scale of all protein sequences from life on Earth in the NCBI RefSeq database.

Period: 2024–2025
PI/contact: Gemma Atkinson
University: Lund University

Microstructure imaging of the cortex by diffusion MRI

This project aims to develop methods for mapping cortical microstructure with potential to significantly enhance medical imaging in conditions like epilepsy and Alzheimer’s disease. Current imaging methods cannot precisely map changes in the microstructure of the cortex – the brain's outer layer where neurons are found. This makes it challenging to, for example, detect early signs of Alzheimer's disease and the origin of lesions causing epileptic seizures. Our goal is to overcome this challenge by leveraging computational imaging with advanced acquisition strategies to enhance the image information content and the image resolution.

Period: 2024–2025
PI/contact: Markus Nilsson
University: Lund University

Pandemic preparedness in the era of big data: Disease surveillance tools using individual-level register data and novel mobility data

The overarching goal of this project is to develop novel analytical methods to more rapidly and accurately identify, monitor, and manage health risks in society, such as a pandemic disease, using a unique data infrastructure with rich individual-level register data for entire Sweden linked from different sources together with data on mobility patterns.

Period: 2024–2025
PI/contact: Dominik Dietler
University: Lund University

Breast cancer diagnostics in low-resource settings using point-of-care ultrasound and deep learning

Access to timely diagnosis of breast cancer in low- and middle-income countries is lacking, which is contributing to the poor survival of breast cancer. A promising solution to provide accessible breast diagnostics in low-resource setting is point-of-care ultrasound combined with a smartphone-based artificial intelligence (AI) algorithm. Such an algoritm was devloped in the project eSSENCE@LU 8:7. The tasks in the next phase include to make the algorithm vendor neutral, complement the current algorithm with an uncertainty estimation, develop a prototype app and perform clinical studies in Sweden as well as Kenya.

Period: 2024–2025
PI/contact: Kristina Lång
University: Lund University

Development of methods to treat metal sites in ligand-binding calculations

The project will try to solve an important problem for drug companies. We will test a hierarchy of methods and decide which is most promising in terms of speed, accuracy, transferability and level of automatization. The methods we develop will be useful also in other areas, e.g. for simulations of metalloproteins in general, to study metal binding, flexibility and conformational changes. We expect that the methods will be widely used by other groups in the field of biomolecular modelling. Moreover, the methods will be applied on strategic and interesting targets of interest also outside the university and for other pharmaceutical companies.

Period: 2025–2026
PI/contact: Ulf Ryde
University: Lund University

Developing data-driven simulation of protein structural dynamics

Period: 2023–2024
PI/contact: Magnus Andersson
University: Umeå University

Machine learning methods for parsing symptoms and
treatment responses in Parkinson´s disease

Taking advantage of unique recording capabilities available at the Movement and Reality Lab (recently established at Lund University1), we aim to develop machine learning-based methods to detect and quantify different movement abnormalities and responses to treatment in Parkinson’s disease.

Period: 2025–2026
PI/contact: Angela Cenci Nilsson
University: Lund University

Finite Element Methods for Modern Computing Architectures and Machine Learning Applications

Period: 2025–2026
PI/contact: Mats Larson (UMU), Karl Larsson (UMU) and Anna Persson (UU)
University: Umeå Univeristy