Dr Fabian Freund

Supervisor Details

Contact Details

Genetic and Genome Biology, University of Leicester

Scientific Background

Research Interests

Dr Freund's research interests are in population genomics/genetics. They include mathematical/probabilistic modelling (usually involving coalescent theory), statistical methods, data analysis and bioinformatics. These approaches are then used to answer questions concerning evolution and genetics in a variety of species.

Please see Fabian's ORCID profile to see his publications:

https://orcid.org/0000-0002-9958-2703

Supervision Style

In three words or phrases: outcome-oriented, supportive, inclusive.

Provision of Training

I will provide teaching and training whenever necessary. If we deem it beneficial, this may also involve experienced other group members (e.g., experienced PhD students) or external training (workshops, other external labs in collaboration with my group).

Progression Monitoring and Management

Communication is key: We will decide on realistic goals for a time period (can be a week, can be a month) and then we will communicate about the progress in our regular meetings. Through this, we will troubleshoot and refine action plans.

Communication

I will be happy to discuss your ideas and plans, but also any issues that are impacting your ability to fulfil your potential or my/our expectations. My PhD students have high priority and I will see to have a chat quickly as soon as we see the need to discuss.

PhD Students can expect scheduled meetings with me:

In a group meeting

At least once a fortnight

In year 1 of PhD study

At least once a week

In year 2 of PhD study

At least once a week

In year 3 of PhD study

At least once a fortnight

These meetings will be a mixture of face to face, via video chat or telephone, and I am usually contactable for an instant response on 3 or 4 days per week.

Working Pattern

The timing of work in my lab is completely flexible, and (other than attending pre-arranged meetings), I expect students to manage their own time.

Notice Period for Feedback

I need at least 1 week’s notice to provide feedback on written work of up to 5000 words.

MIBTP Project Details

Primary supervisor for:

Evolutionary mechanisms of pathogens - from commensality to virulence

See the PhD Opportunities section to see if this project is currently open for applications via MIBTP.

Please Note: The main page lists projects via BBSRC Research Theme(s) quoted and then relevant Topic(s).

Evolutionary mechanisms of pathogens - from commensality to virulence

Secondary Supervisor(s): Dr Sandra Beleza

University of Registration: University of Leicester

BBSRC Research Themes: Understanding the Rules of Life (Microbiology)

Project Outline

Clinically relevant pathogens evolve through a complex interplay of within-host evolution (including host and antibiotic selective pressure and interactions with other microorganisms), transmission dynamics, and metapopulation processes between hosts. This multifaceted system is yet to be fully understood. However, there are simulation tools, simplified theoretical models and inference methods alongside a wealth of publicly available datasets that allow to assess how these different evolutionary processes play out in real populations.

In this project, you will:

i) collect datasets of paired commensal and invasive/disease strains from common databases such as PubMLST, NCBI and ENA;

ii) use inference tools to estimate fitness effects of mutations, population size changes, and diverse modes of selection on carriage and invasive/disease strains of the same bacterial species;

ii) use computer simulation tools to model different evolutionary scenarios and infer the model that best fits observed data evaluated in

i). This approach will allow you to assess several questions:

- Which evolutionary scenarios lead to a change from being a commensal strain to becoming virulent? Is this purely selection-driven or a chance product of genetic drift?

- Do new invasive strains often evolve via long-term within-host evolution epochs, e.g. within immunocompromised patients?

- Does the distribution of fitness of observed genetic variation differ between commensal and invasive strains? Are these distributions species- or population-specific?

By integrating large-scale genomic datasets with advanced evolutionary inference and simulation modelling, this project will elucidate the mechanisms driving the evolution of bacterial strains from commensal to invasive states, offering critical insights into pathogen emergence and informing strategies for surveillance and intervention