OHAMR Call for proposals 2026

General Information

• Project title: Genome Scale Modellling Based Sensitivity Analysis of Bacterial Strains for Combination Therapies in AMR
• Type: Project looking for partner
• Organisation: Riga Stradins University
• Country: (LV)
• Career stage: Early Career Researcher (up to 8 years** since PhD).

Research area

• Scientific area(s) of the call:
  1. Topic 1: Identify and develop new combination treatments using existing or innovative antimicrobials or antimicrobial with adjunctive treatments to extend drug efficacy and combat resistance.
• One Health Setting:

  H - Human Health

• Keywords:

  Genome-Scale Modelling; Omics-Integration; Flux; Machine Learning; Simulation

• Brief description of your expertise / expertise you are looking for:

  We are looking for Machine Learning experts to link drug tagets to drug databases, the targets will be acquired through genome scale modelling. We\'ll also want to use flux values of reactions for successful treatment simulations as training data for models to further predict unintuitive targets. We are able to integrate omics into sample specific models in order to simulate resistant strains and find where may be attractive targets for now combination therapies.

• Brief description of your project / the project you would like to join:

  A brief description of our project plan is to use Genome-Scale Metabolic Modelling to try and find novel combinations (or if possible, even repurposing) of existing drugs in order to find more effective treatment regiments for bacterial infections. One of the partners we plan on applying with (UK Based) plans on getting genomic data for their bacterial collection (~20,000 strains) which they have known resistance profiles for. With this information/data it is possible to generate simulations of these specific strains and find unintuitive targets for which drugs which may work in synergy. This will be done by simulating for useful perturbations in various aspects of the cell (such as biomass, peptidoglycan, lipopolysaccharide production, etc...). Once targets/combinations of targets are obtained through omics based our genome-scale modelling approach (along with other data generated from our simulations), we\'ll want to link them to drug databases. I would like to use a combination of machine learning to identify synergistic combinations with output data from our simulations & computational chemistry methods in order to predict useful combinations of existing treatments, and are looking for machine learning/computational chemistry expertise to join the consortia.

Rui Tavares

• Organisation: Riga Stradins University
• Position: Researcher
• E-mail: RuiAfonso.Tavares@rsu.lv
• Web: www.biosystems.lv/