Workshop 20 January 2025

Camila Esguerra

Disease models, Drug discovery, Tissue xenografting, Toxicology

Osman Gani

Computer-aided drug design (CADD). Molecular Modeling

- mechanisms of antimicrobial peptides
- nanomedicine: encapsulation of antibiotics
- biophysical characterisation techniques for peptide/protein-membrane interactions

Mechanism of action and encapsulation of antimicrobial peptides

Structural biology & infection biology

Amyloids: Curse or Opportunity? ¨C From Medicine to Cultural Heritage

Josefine Eils? Nielsen

antimicrobial peptides, peptoids, drug delivery, self-assembly, structural characterization, antibiotics, antivirals, peptide-lipid interactions

I am a sociologist of science with expertise in how to best create the kinds of organizations, groups, and social interactions that best facilitate creative interdisciplinary research. My interest is in joining a team to help them plan how they might best organize themselves for interdisciplinary success.

Fernanda Cristina Petersen

Resistome, microbiome, AMR

This project idea is to focus on harnessing the potential of microbiota modulation to combat antimicrobial resistance (AMR) within the framework of the One Health approach. By leveraging interventions such as probiotics, prebiotics, and microbiome-targeted therapies, we aim to restore microbial balance and reduce the spread of resistant pathogens across humans, animals, and the environment.

Ole Andreas ?kstad

Antimicrobial resistance development, Microbial comparative and functional genomics, Microbial evolution, Bacterial virulence mechanisms

UiO AMR - Cross-faculty AMR research initiative (LSB-CE)

Hanne Winther-Larsen

Molecular microbiology, antimicrobial resistance, vaccine development, One health, animal health.

Antimicrobial resistance is one of the major global health threats. Our research groups is interested in approaching this problem on different ways from to understand basic biological mechanisms to drug use and new antimicrobial strategies.

Osman Gani

Computer-aided drug design (CADD). Molecular Modeling

Alvaro K?hn-Luque

Mathematical modeling, knowledge-driven machine learning, digital twins, personalized cancer therapies, biomarkers, genomics, medical imaging.

Personalized cancer therapy integrating multi-type clinical data, biological experimentation, mechanistic mathematical models and artificial intelligence.

Anthony Mathelier

computational biology, gene expression regulation, multi-omics, machine learning, AI, transcription factors

With an integrated computational approach, we will provide insight into the interactions between fundamental biological processes in gene expression across domains and in health and disease.

I am a sociologist of science with expertise in how to best create the kinds of organizations, groups, and social interactions that best facilitate creative interdisciplinary research. My interest is in joining a team to help them plan how they might best organize themselves for interdisciplinary success.

Jonas Paulsen

bioinformatics, 3D genome organization, epigenetics, mechanobiology, gene transcription regulation

With an integrated computational approach, we will provide insight into the interactions between fundamental biological processes in gene expression across domains and in health and disease.

Trine B Rounge

bioinformatics, computational biology, gene expression, health and disease

Use computational approaches to unravel the interplay between the different layers involved in gene expression.

Neuroscience, Memory

Attention is important for memory. How is attention formed in the brain and how is it used to successfully form memories? My idea is to record neural data in humans while they perform a memory task that is highly dependent on attention.

Manuela Zucknick

Statistical learning/ machine learning (ML) and AI for translational and clinical cancer research, in particular for personalised cancer therapies. Integration of data from diverse sources (multi-omics, drug screens, clinical, imaging, genomics) for prediction of drug response and synergies in pharmacogenomic screens and of prognosis and treatment response in patients. Machine learning for small and noisy (biomedical) data, e.g. through integration of knowledge and structure in ML.

AI and robotics for personalised cancer medicine: Next-generation pipelines and tools for overcoming drug resistance in cancer by studying the full dynamics and evolution of the tumor and its environment in large-scale screens, in vitro, in vivo and in silico.

bioinformatics, bioinformatics data, gene expression, gene regulation, chromosomes, epigenetics, evolution

Use bioinformatics to how explore gene expression influences development and disease