Biology is cool. Mathematics makes sense.

But what is cool and makes sense? Evolutionary biology!

After studying both biology and physics at the same time, I discovered that evolutionary biology, and especially theoretical evolutionary biology, combines what I like to do with what I actually can do. Therefore, since the beginning of my PhD, I have been focusing on the mathematical modeling of biological processes in a broad spectrum of research topics: from the evolution of cooperation through molecular signaling pathways to adaptation in a changing environment. Since 2020, I have been investigating drug resistance evolution, mainly in biofilms and in parasitic worms. I find evolution of drug resistance extremely urgent and relevant, but also interesting and matching well my skill set. I am hoping that my research will help us to fight this problem.

I like developing mechanistic models, coding in Python, creating pretty graphs, and explaining my research to others, whether they are scientists or not.

2020- Drug resistance evolution

Every year, more than one million deaths can be attributed to infections caused by resistant bacteria alone. Drug resistance is an increasingly pressing problem all over the world, across all known pathogens.

Bacterial biofilms are behind many such infections. However, how biofilm lifestyle affects resistance evolution is still poorly understood. Therefore, I develop mathematical, population genetic models of drug resistance in bacterial biofilms, trying to find out how to best treat them as an ultimate objective. You can find more about my research on biofilms in a Trends paper, Frontiers paper or a poster from ISME conference.

2020-2021 SPARK: Kaleidoscope of adaptation

Switching from one evolutionary topic to another, I learned how difficult and rare communication is between scientists from different fields and between theoretical and empirical biologists. Terminology differs, as do (implicit) assumptions and methodology. I obtained SPARK funding from the Swiss National Science Foundation to carry out a project aiming to synthesize the approaches to studying adaptation. The project was hosted by the University of Zurich, by Hanna Kokko's group. Together with Tadeas Priklopil we developed a prototype of a platform for model sharing. We are still improving its functionality and working on a paper.

2018-2021 gridCoal - predicting genetic diversity from past demographic data

In collaboration with Kati Csillery and Eniko Szep, I developed a simulation tool called gridCoal to estimate diversity from demographic history data. This can be used, for instance, to identify diversity hotspots based on our knowledge (or model prediction) of past population sizes.

This Python based tool uses msprime and coalescence theory. More can be found here.

2017-2020 Adaptation in a changing environment

For thousands of years, we have been changing the environment that surrounds us to satisfy our needs and to make our life safer, easier, and more prosperous. However, we are not alone on this planet, and the increasing magnitude of alterations we impose on our environment impacts the biosphere at every level, from bacteria to mammals. Many of these anthropogenic influences may be fatal for those species, leading to extinction and a decrease in biodiversity.

Inspired by the SAGE project (see below), I applied for Marie Sklodowska Curie fellowship entitled RACE - Rate of Adaptation in a Changing Environment. I developed a polygenic model of adaptation in a changing environment.

Together with my collaborators, we have shown that frequent environmental change, usually considered detrimental for species, can actually help them to avoid extinction by preventing adaptation to a given environment. The organisms evolve to be generalists, instead of specialists, that can survive, though not thrive, in any conditions. The results of my research are here, or, in a more accessible form, here.


2014-2016 SAGE, postdoc at IST Austria

After my PhD, I worked as a post-doctoral scientist at the Institute of Science and Technology Austria in the group of Prof. Nick Barton. At first, I was a part of an extensive, interdisciplinary effort to transfer knowledge between the fields of evolutionary biology and evolutionary computation.

Both fields have studied the speed of adaptation independently, and with orthogonal approaches. Our project united researchers from the theory of evolutionary computation and theoretical population genetics to synergise these complementary approaches. We identified parallels between the fields and created a general, unified framework for evolutionary processes. We applied methods common in evolutionary computation to solve the problems in evolutionary biology and vice versa.

Indirect genetic effects

2009-2013 Evolution of cooperation

Everywhere around us, organisms engage in complex social behaviors. Cells interact to create multicellular organisms, individuals interact within groups and populations, and species interact with each other in complex networks of ecosystems. It is hard to believe that all these behaviors are also encoded by genes and are the result of millions of years of evolution.

During my PhD I studied the evolution of cooperation and altruism using the indirect genetic effects (IGEs) framework. I investigated how IGE influence phenotypes, or the dynamics of social interaction, and how green the beard needs to be to promote altruism (see Dawkins' green-beards).