Doctoral Candidates (DC)

Bacteria can live harmlessly alongside humans or cause disease. This project aims to identify genomic features associated with human pathogenicity and understand how they evolve across bacterial lineages. One of the major goals is to find candidate biomarkers for infection diagnostics.

Bacterial genomes will be analyzed together with associated phenotypes to identify features linked to pathogenicity. Comparative genomic and evolutionary analyses will examine these features across different bacteria to determine how often relevant changes occurred independently in different lineages. Case studies in selected bacterial species will evaluate the relevance of findings from the broad dataset. Promising candidate biomarkers will be prioritized for experimental validation through collaborations within the consortium.

Chronic wounds represent a major clinical challenge, particularly when complicated by persistent bacterial infection. This PhD project aims to improve the understanding and modelling of chronic wound infection and to identify biomarkers that may support future monitoring and diagnostic strategies. The project will focus primarily on Staphylococcus aureus and Pseudomonas aeruginosa, studied individually and in combination using in vitro and ex vivo wound models incorporating clinically relevant features such as altered extracellular matrix composition, hypoxia, elevated glucose concentrations and biofilm formation.

The evolution of infection and associated bacterial and host responses will be characterized over time. Mass spectrometry-based proteomics will be used to investigate bacterial and host-derived proteins, alongside other infection-associated parameters such as inflammatory mediators, pH, temperature and reactive oxygen species. Where clinical samples are available through collaborations, proteomic analysis of wound exudate or other chronic wound-associated samples will support the identification of candidate biomarkers associated with infection status or progression.

 Water-associated pathogens can persist for prolonged periods in environmental reservoirs, where interactions with biofilms, protozoa, and other microorganisms shape their survival and adaptation. This project investigates the mechanisms that enable environmental persistence, using pathogen Legionella as model organism. By combining MS-based proteomics and laboratory-based infection models, the project aims to improve our understanding of how environmental factors influence survival and persistence, and to identify protein biomarkers associated with environmental adaptation and pathogenic potential. This could ultimately help us improve our ability to assess and monitor pathogen-associated risks. 

Bloodstream infections and sepsis are life-threatening conditions that require fast diagnosis, yet current methods rely largely on time-consuming culturing. This project aims to identify surface-exposed protein and peptide biomarkers of clinically relevant pathogens from the WHO priority list, including Staphylococcus aureus, Streptococcus pneumoniae and multresistant Enterobacteriaceae.
Surface shaving of intact bacterial cells will be combined with mass spectrometry-based proteomics, genome sequencing and bioinformatics to find species-specific biomarkers on the outer bacterial surface. Additionally, in-vitro models mimicking sepsis blood samples will be established to test BUG-ID prototype sensors.

Medical implant-associated infections remain a significant clinical challenge, particularly due to the ability of bacteria to form biofilms that complicate diagnosis and treatment. This project aims to identify bacterial protein biomarkers with potential applications in the early detection of these infections. Using in vitro biofilm models of Staphylococcus aureus, Staphylococcus epidermidis, and Escherichia coli, combined with mass spectrometry-based proteomics, the research will focus on discovering bacterial proteins and peptides that could serve as diagnostic targets. Promising candidates will be further assessed for their specificity, detectability, and suitability under relevant experimental conditions. The findings are expected to contribute to a better understanding of potential diagnostic targets and support future development of biomarker-based detection strategies within the BUG-ID project.

Novel strategies will be developed to facilitate chemical rupture and homogenization of small number of bacterial and eukaryotic cells. This pipeline will be coupled to state-of-the-art mass spectrometry analyses of proteins and their post-transational modifications, including downstream bioinformatics. Biological applications will include work on bacterial communities (including metaproteomics), host cell and their signal transduction under various perturbations, bacterial persister mechanisms and biomarker discovery

This project integrates plasma proteomics (Olink) and single-cell RNA sequencing (scRNAseq) with clinical data to develop AI-assisted approaches for diagnostics and personalized clinical decision-making. By linking molecular and cellular host status to sensor-based readouts (e.g. sepsis monitoring), the project aims to build predictive, data-driven models that support real-time treatment decisions based on host response.

Develop expertise in graphene synthesis by chemical vapor deposition (CVD), graphene characterization and optimization, and the fabrication of graphene-based biosensors using the state-of-the-art Myfab Chalmers nanofabrication facilities. The project involves microfabrication, device functionalization, electrical and electrochemical characterization, sensor integration, and biosensor performance evaluation. It also includes scientific communication through publications, conference presentations, doctoral training courses, and interdisciplinary collaboration.

E-mail: ulutanir@chalmers.se

Optical sensing offers high speed, high sensitivity, and low sample volume requirements, making it a powerful approach for biological sample analysis. This project aims to develop an optical biosensor exploiting polarization-dependent light absorption in graphene thin films. This effect converts subtle refractive index  changes at the graphene surface into measurable optical or electrical signals,
enabling the detection of low biomarker concentrations. To maximize sensitivity, the graphene film is combined with a porous TiO₂ layer, increasing the internal surface area and encouraging biomarker accumulation near the sensing region.

Project tasks include simulation-based design optimization, sensor fabrication (graphene and porous layer deposition), and the setup of optical and microfluidic testing systems. Following experimental validation with partner-developed biomarkers, the final phase involves integrating the sensor into a unified structure combined with other technologies developed in the BUG-ID network.

This project will focus on the synthesis and pore formation of oxo-Graphene in controlled conditions. These pores will then be functionalized to add groups which can interact with biomolecules. The synthesis and characterizations will be done using the state-of-art facilities available at the SupraFAB to precisely quantify the flake size, defects, pore size and functionalities of the oxo-Graphene. The aim of this work is to create identical functionalized oxo-graphene flakes with minimal defects to maintain the repetitively of the tests and ensure accuracy and reliability of graphene based biosensors.

To identify bacterial strains and their antibiotic resistance in real time, the BUG-ID network leverages the sensitivity of graphene. Anchoring biological receptors onto nanomaterials often causes them to distort and lose detection power. My doctoral research solves this structural bottleneck by bridging predictive biophysical modeling with benchtop biochemistry. Using molecular dynamics (MD) simulations and density functional theory (DFT), I design protective molecular linkers and stabilizing mutations. Then, validate them via wet-lab cloning and protein engineering. This integration of dry-lab and wet-lab validation establishes a design framework for next-generation biosensors.